Data Engineering

The software conception, development, testing, deployment, and maintenance processes have fundamentally changed with the use of artificial intelligence (AI) and machine learning (ML) in the software development life cycle (SDLC). Businesses today want to automate their development processes in any way they can with the goals of increasing efficiency, positively impacting time to market, improving the quality of software, and being data-driven in their approaches. AI/ML is instrumental in achieving these goals as it helps in automating repetitive work processes, assists with predictive analytics and empowers intelligent systems that respond to changing needs. This article discusses the role of AI/ML at each stage of the SDLC, how they are able to add value to it, and the challenges organizations face or will face in order to exploit them to the maximum. Planning and Requirements Gathering Planning and requirements gathering is the first step in initiating the software development lifecycle and forming the basis of the entire software development project. With organizations being able to utilize ML and AI-enabled tools that can analyze historical data, they can make more educated guesses about user behavior, requirements, and project time frames. Key Applications Requirement analysis: Now, it is possible to gather and interpret functional requirements based on feedback using NLP tools such as IBM Watson, as they greatly assist in understanding the needs of teams, users, and other stakeholders.Predictive analytics: Machine learning models estimate risks of a project that could arise, allocation of resources and timelines based on the past. This capability helps teams avoid setbacks.Stakeholder sentiment analysis: Feedback from stakeholders is analyzed by AI tools for feature specification prioritization, ensuring time is not wasted on unimportant ones. Benefits Increased precision in capturing true requirements.Reduction in project risk identification time.Strengthened linkage between business objectives and technical aspects. Design Phase AI/ML in the design phase helps by giving the users tools for architecture decision-making, simulations, and visualizations, hence augmenting manual effort and facilitating the workflow. Key Applications Automated UI/UX design: AI solutions such as Figma make recommendations regarding the optimal design layout by applying behavioral data to improve user experience.Codebase analysis and optimization: Investigating business-specific needs, AI systems recommend the most effective system structures or data flow diagrams.Simulation and prototyping: Simulating multi-agent models, AI prototypical images of the product are constructed, helping them imagine converting the idea into an actual product without being fully developed. Benefits Quicker and multiple iterations of prototype models.Better addressing various needs would be through design and user elements integration.Enhancement in interrelationship between the designers of the development and the users of the designs. Development Phase AI/ML can improve the automation of coding tasks, code quality, and productivity during the development stage. Application Code generation: GitHub Copilot and OpenAI Codex are tools that aid developers, particularly in the more monotonous tasks to which they allow these developers to generate snippets of code, thus saving time.Code review and refactoring: Tools such as Deep Code and SonarQube perform a more in-depth function in that they check embedded code against standards, which verify against code quality by looking for vulnerabilities and inefficiencies.Version control optimization: Al algorithms assist by predicting potential solver collision and require more attention while taking care of most problems that involve versioning processes, including Git. Benefits Developments were sped up thanks to lowered coding requirements.Due to improved code quality, the number of defects was also reduced.Some other issues include the fostering of better teams using these automated code reviews. Testing Phase In an all-encompassing way, AI/ML assists in the testing phases by achieving automation of repetitive tasks, test case generation, and improvement of test coverage, which in all, results in quicker and more trustworthy releases. Application Test case generation: ML models reduce the causal part greatly by producing test cases depending on user stories, historical data, and other types of data, including past testing patterns.Automated testing: Intelligent frameworks such as Testim and Applitools guarantee full coverage of UI testing due to their automation capabilities, which allow for the continuous interface and interaction of users.Predictive bug detection: Early defect identification is made possible through machine learning models that do pattern analysis on repositories of code in order to spot potential bugs.Defect prioritization: Artificial intelligence tools assist QA teams by classifying and ordering the defects according to their impact, this assists them to concentrate on the most important ones first. Benefits Decreased manual efforts and increased coverage.Faster identification and resolution of bugs.Improved quality of the product provided there is constant validation. Deployment Phase Minimizing the duration of the downtime of the sense and also improving the efficiency of the deployment processes is part and parcel of the automation of the processes by AI/ML. Key Applications Predictive Deployment Strategies: With the use of AI systems, minimal risk and the duration of redevelopment have been decreased by recommending the most appropriate time to deploy and the strategies needed.Monitoring and Rollbacks: AI-managed deployment statistics that inform Roll Note mechanisms to be enabled once anomalies are detected are employed by Harness.Infrastructure Optimization: Deployments are enhanced by AI, which better predicts and satisfies requirements more effectively and at reduced costs. Benefits Lowered risks when deploying and the time it would take to do so.Cost of infrastructure is significantly lowered due to the effective allocation of resources.Stability has never been better, with operations running smoother and being able to recover from issues much quicker. Maintenance and Operations AI and machine learning tools come into play in the post-deployment stage to provide constant user support while ensuring that the system is reliable and its performance is optimized. Key Applications Anomaly detection: Anomaly detection tools powered by AI continually examine system logs and metrics for signs of abnormality, aiding in the limitation of service outages. Predictive maintenance: Predictive training models are used to estimate the likelihood of failures that might occur and the actions to avoid them, which results in a drop in the amount of repair work that can’t be planned. Chatbots for support: AI chatbots function as a first line of support 24/7 by providing answers to standard questions and passing over challenging cases to human support staff.Dynamic scaling: Real-time reports of how the system is used inform AI models, which then reallocate the system's resources as needed. Benefits A system that is always maintained will result in less equipment interruption.Use of AI-based support features reduces the amount of work needed to run the system.Resources and their allocation and automation are done based on how much is currently in demand. Benefits of AI/ML in SDLC Incorporating AI/ML into the SDLC brings about a multitude of advantages, including but not limited to increased efficiency, better quality products, and a shorter time to enter the market. Improved efficiency: The need for manual effort is eliminated since several repetitive tasks are done automatically, development time is hence shortened with productivity levels increased.Increased quality: AI/ML automated tools are able to raise the quality of the software produced through the modification of the code, increasing the test coverage and decreasing the rate of defects, among other things.Improved decision-making processes: The AI in the models makes a bazillion guesswork, enabling a data-driven decision-making process anytime during the SDLC.Cost reduction: The implementation of AI/ML leads to less reliance on human intervention, thereby ensuring a complete and streamlined process and eliminating unwanted wastage of resources.Adaptive systems: With the help of AI/ML, self-adjusting learning systems are developed that correct themselves to meet changing targets, resulting in a more efficient system with the passage of time. Challenges of AI/ML in SDLC While AI/ML has numerous advantages in the software development lifecycle, there are some challenges organizations should address. Data dependency: Construction of competent AI/ML models requires a large amount of quality data. In the absence of proper data, biases will be introduced, leading to poor performance.Integration complexity: To implement AI/ML tools in the existing framework, numerous changes to the workflow would be required, resulting in severe disruption and loss of time, therefore making the integration process complicated.Skill gaps: These tools have become a necessity across all sectors, yet there remain gaps still where people lack the specialized skills to use AI/ML tools resulting in the need for extra training.Bias and fairness: The algorithms built on AI tend to mirror the inherent biases within the data used to train it. This issue is especially problematic in the use of AI models within the finance and healthcare sectors, as it can generate unjustified consequences. Final Remarks It is celebrated that new technologies in AI/ML have mostly been adopted within the processes of the modern life cycle of system/ software development, deployment, and maintenance, and those actively automate processes, assist with decision-making, and help improve the quality of the software. AI/ML equips companies by enabling them to speed systems to market, slash costs, and design systems that are highly adaptable and efficient. Nevertheless, for organizations to fully enjoy the benefits, certain roadblocks need to be dealt with, things such as the quality of the data, complexity of integration, and lastly, skills. So, as long as they have appropriate adoption approaches, AI/ML can be effectively used for modern-day "software development." References Luger G.F. & Stubblefield W.A. (ref) "Artificial Intelligence: Structures and Strategies for Complex Problem Solving," Montreal: Benjamin/Cummings (1993). Dvorkin and Melnik G (2021) "AI in Software Development Lifecycle: State of the Art and Challenges." Journal of Software Engineering Research and Development. Leekha, Sophiya. (2020) "Impact of AI and Machine Learning on Software Development Lifecycle." Proceedings of the International Conference on Computer Science and Software Engineering. Raj, A. And Verma, A. (2019) Artificial Intelligence and Machine Learning for Agile SDLC: A Comprehensive Review. Journal of Systems and Software. Sharma, Rashmi & Singh, Sharmila. (2021) AI-based Automation in Software Testing: Trends and Challenges. Journal of Testing Technology. Zou, J., & Yuan, S. (2022). "Integrating Machine Learning into Software Development: Benefits, Challenges, and Best Practices." Journal of Software Engineering Practice. Seshan, V., & Mahadevan, P. (2018). Predictive Analytics and AI in Software Development Lifecycle: Opportunities and Challenges. International Journal of Computer Science and Information Systems.

This is a guide for folks who are looking for a way to quickly and easily try out the Vector Search feature in Azure Cosmos DB for NoSQL. This app uses a simple dataset of movies to find similar movies based on a given criteria. It's implemented in four languages — Python, TypeScript, .NET and Java. There are instructions that walk you through the process of setting things up, loading data, and then executing similarity search queries. A vector database is designed to store and manage vector embeddings, which are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent data. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, and images, audio, and other types of data can all be vectorized. These vector embeddings are used in similarity search, multi-modal search, recommendations engines, large language models (LLMs), etc. Prerequisites You will need: An Azure subscription. If you don't have one, you can create a free Azure account. If, for some reason, you cannot create an Azure subscription, try Azure Cosmos DB for NoSQL free.Once that's done, go ahead and create an Azure Cosmos DB for NoSQL account.Create an Azure OpenAI Service resource. Azure OpenAI Service provides access to OpenAI's models including the GPT-4o, GPT-4o mini (and more), as well as embedding models. In this example, we will use the text-embedding-ada-002 embedding model. Deploy this model using the Azure AI Foundry portal. I am assuming you have the required programming language already setup. To run the Java example, you need to have Maven installed (most likely you do, but I wanted to call it out). Configure Integrated Vector Database in Azure Cosmos DB for NoSQL Before you start loading data, make sure to configure the vector database in Azure Cosmos DB. Enable the Feature This is a one-time operation — you will need to explicitly enable the vector indexing and search feature. Create a Database and Container Once you have done that, go ahead and create a database and collection. I created a database named movies_db and a container named movies with the partition key set to /id. Create Policies You will need to configure a vector embedding policy as well as an indexing policy for the container. For now, you can do it manually via the Azure portal (it's possible to do it programmatically as well) as part of the collection creation process. Use the same policy information as per the above, at least for this sample app: Choice of index type: Note that I have chosen the diskANN index type which and a dimension of 1536 for the vector embeddings. The embedding model I chose was text-embedding-ada-002 model and it supports dimension size of 1536. I would recommend that you stick to these values for running this sample app. But know that you can change the index type but will need to change the embedding model to match the new dimension of the specified index type. Alright, let's move on. Load Data in Azure Cosmos DB To keep things simple, I have a small dataset of movies in JSON format (in movies.json file). The process is straightforward: Read movie info data from json file,Generate vector embeddings (of the movie description), andInsert the complete data (title, description, and embeddings) into Azure Cosmos DB container. As promised, here are the language-specific instructions — refer to the one that's relevant to you. Irrespective of the language, you need to set the following environment variables: Plain Text export COSMOS_DB_CONNECTION_STRING="" export DATABASE_NAME="" export CONTAINER_NAME="" export AZURE_OPENAI_ENDPOINT="" export AZURE_OPENAI_KEY="" export AZURE_OPENAI_VERSION="2024-10-21" export EMBEDDINGS_MODEL="text-embedding-ada-002" Before moving on, don't forget to clone this repository: Plain Text git clone https://github.com/abhirockzz/cosmosdb-vector-search-python-typescript-java-dotnet cd cosmosdb-vector-search-python-typescript-java-dotnet Load Vector Data Using Python SDK for Azure Cosmos DB Setup the Python environment and install the required dependencies: Python cd python python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt To load the data, run the following command: Python python load.py Load Vector Data Using Typescript SDK for Azure Cosmos DB Install the required dependencies: Plain Text cd typescript npm install Build the program and then load the data: Plain Text npm run build npm run load Load Vector Data Using Java SDK for Azure Cosmos DB Install dependencies, and build the application: Plain Text cd java mvn clean install Load the data: Plain Text java -jar target/cosmosdb-java-vector-search-1.0-SNAPSHOT.jar load Load Vector Data Using .NET SDK for Azure Cosmos DB Install dependencies and load the data: Plain Text cd dotnet dotnet restore dotnet run load Irrespective of the language, you should see the output similar to this (with slight differences): Plain Text database and container ready.... Generated description embedding for movie: The Matrix Added data to Cosmos DB for movie: The Matrix .... Verify Data in Azure Cosmos DB Check the data in the Azure portal. You can also use the Visual Studio Code extension, which is pretty handy! Let's move on to the search part! Vector/Similarity Search The search component queries Azure Cosmos DB collection to find similar movies based on a given search criteria - for example, you can search for comedy movies. This is done using the VectorDistance function to get the similarity score between two vectors. Again, the process is quite simple: Generate a vector embedding for the search criteria, andUse the VectorDistance function to compare it. This is what the query looks like: Plain Text SELECT TOP @num_results c.id, c.description, VectorDistance(c.embeddings, @embedding) AS similarityScore FROM c ORDER BY VectorDistance(c.embeddings, @embedding) Just like data loading, the search is also language-specific. Here are the instructions for each language. I am assuming you have already set the environment variables and loaded the data. Invoke the respective program with your search criteria (e.g. inspiring, comedy, etc.) and the number of results (top N) you want to see. Python Plain Text python search.py "inspiring" 3 Typescript Plain Text npm run search "inspiring" 3 Java Plain Text java -jar target/cosmosdb-java-vector-search-1.0-SNAPSHOT.jar search "inspiring" 3 .NET Plain Text dotnet run search "inspiring" 3 Irrespective of the language, you should get the results similar to this. For example, my search query was "inspiring," and I got the following results: Plain Text Search results for query: inspiring Similarity score: 0.7809536662138555 Title: Forrest Gump Description: The story of a man with a low IQ who achieves incredible feats in his life, meeting historical figures and finding love along the way. ===================================== Similarity score: 0.771059411474658 Title: The Shawshank Redemption Description: Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency. ===================================== Similarity score: 0.768073216615931 Title: Avatar Description: A paraplegic Marine dispatched to the moon Pandora on a unique mission becomes torn between following his orders and protecting the world he feels is his home. ===================================== Closing Notes I hope you found this useful! Before wrapping up, here are a few things to keep in mind: There are different vector index types you should experiment with (flat, quantizedFlat).Consider the metric your are using to compute distance/similarity (I used cosine, but you can also use euclidean, or dot product).Which embedding model you use is also an important consideration - I used text-embedding-ada-002 but there are other options, such as text-embedding-3-large, text-embedding-3-small.You can also use Azure Cosmos DB for MongoDB vCore for vector search.

By Abhishek Gupta CORE

Through achieving graduation status from the Cloud Native Computing Foundation, CubeFS reaches an important breakthrough as a distributed file system created by community input. CubeFS's graduation status demonstrates its achieved technical sophistication while establishing its reliable history of managing production workloads on a large scale. CubeFS provides low-latency file lookups and high throughput storage with strong protection through separate handling of metadata and data storage while remaining suited for numerous types of computing workloads. The inherent compatibility between CubeFS's cloud-native design and Kubernetes achieves full automation of deployments together with rolling upgrades as well as scalable node adaptation to meet increasing data needs. CubeFS establishes itself as a trustworthy high-performance solution for container-based organizations wanting to upgrade their storage systems because of its dedicated open-source community support and adherence to the CNCF quality standards. Introduction to CubeFS CubeFS functions as a distributed file system that developers worldwide can access under an open-source license. The distribution of file operations occurs between MetaNodes, which handle metadata management tasks, and DataNodes manage data storage tasks overseen by the Master Node, which coordinates cluster activities. Authored structure achieves quick file searches and maintains high data processing speed. When data nodes fail, replication mechanisms safeguard them, resulting in highly reliable support for essential large-scale applications. Why Deploy on Kubernetes Kubernetes offers automated container orchestration, scaling, and a consistent way to deploy microservices. By running CubeFS on Kubernetes: You can quickly add or remove MetaNodes and DataNodes to match storage needs.You benefit from Kubernetes features like rolling updates, health checks, and autoscaling.You can integrate with the Container Storage Interface (CSI) for dynamic provisioning of volumes. End-to-End Deployment Examples Below are YAML manifests that illustrate a straightforward deployment of CubeFS on Kubernetes. They define PersistentVolumeClaims (PVCs) for each component, plus Deployments or StatefulSets for the Master, MetaNodes, and DataNodes. Finally, they show how to mount and use the file system from a sample pod. Master Setup Master PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-master-pvc labels: app: cubefs-master spec: accessModes: - ReadWriteOnce resources: requests: storage: 5Gi storageClassName: <YOUR_STORAGECLASS_NAME> Master Service YAML apiVersion: v1 kind: Service metadata: name: cubefs-master-svc labels: app: cubefs-master spec: selector: app: cubefs-master ports: - name: master-port port: 17010 targetPort: 17010 type: ClusterIP Master Deployment YAML apiVersion: apps/v1 kind: Deployment metadata: name: cubefs-master-deploy labels: app: cubefs-master spec: replicas: 1 selector: matchLabels: app: cubefs-master template: metadata: labels: app: cubefs-master spec: containers: - name: cubefs-master image: cubefs/cubefs:latest ports: - containerPort: 17010 volumeMounts: - name: master-data mountPath: /var/lib/cubefs/master env: - name: MASTER_ADDR value: "0.0.0.0:17010" - name: LOG_LEVEL value: "info" volumes: - name: master-data persistentVolumeClaim: claimName: cubefs-master-pvc MetaNode Setup MetaNode PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-meta-pvc labels: app: cubefs-meta spec: accessModes: - ReadWriteOnce resources: requests: storage: 10Gi storageClassName: <YOUR_STORAGECLASS_NAME> MetaNode StatefulSet YAML apiVersion: apps/v1 kind: StatefulSet metadata: name: cubefs-meta-sts labels: app: cubefs-meta spec: serviceName: "cubefs-meta-sts" replicas: 2 selector: matchLabels: app: cubefs-meta template: metadata: labels: app: cubefs-meta spec: containers: - name: cubefs-meta image: cubefs/cubefs:latest ports: - containerPort: 17011 volumeMounts: - name: meta-data mountPath: /var/lib/cubefs/metanode env: - name: MASTER_ENDPOINT value: "cubefs-master-svc:17010" - name: METANODE_PORT value: "17011" - name: LOG_LEVEL value: "info" volumes: - name: meta-data persistentVolumeClaim: claimName: cubefs-meta-pvc DataNode Setup DataNode PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-data-pvc labels: app: cubefs-data spec: accessModes: - ReadWriteOnce resources: requests: storage: 100Gi storageClassName: <YOUR_STORAGECLASS_NAME> DataNode StatefulSet YAML apiVersion: apps/v1 kind: StatefulSet metadata: name: cubefs-data-sts labels: app: cubefs-data spec: serviceName: "cubefs-data-sts" replicas: 3 selector: matchLabels: app: cubefs-data template: metadata: labels: app: cubefs-data spec: containers: - name: cubefs-data image: cubefs/cubefs:latest ports: - containerPort: 17012 volumeMounts: - name: data-chunk mountPath: /var/lib/cubefs/datanode env: - name: MASTER_ENDPOINT value: "cubefs-master-svc:17010" - name: DATANODE_PORT value: "17012" - name: LOG_LEVEL value: "info" volumes: - name: data-chunk persistentVolumeClaim: claimName: cubefs-data-pvc Consuming CubeFS With the Master, MetaNodes, and DataNodes running, you can mount CubeFS in your workloads. Below is a simple pod spec that uses a hostPath for demonstration. In practice, you may prefer the CubeFS CSI driver for dynamic volume provisioning. YAML apiVersion: v1 kind: Pod metadata: name: cubefs-client-pod spec: containers: - name: cubefs-client image: cubefs/cubefs:latest command: ["/bin/sh"] args: ["-c", "while true; do sleep 3600; done"] securityContext: privileged: true volumeMounts: - name: cubefs-vol mountPath: /mnt/cubefs volumes: - name: cubefs-vol hostPath: path: /mnt/cubefs-host type: DirectoryOrCreate Inside this pod, you would run: mount.cubefs -o master=cubefs-master-svc:17010 /mnt/cubefs Check logs to ensure successful mounting, and test file I/O operations. Post-Deployment Checks Master Logs: kubectl logs cubefs-master-deploy-<POD_ID>MetaNode Logs: kubectl logs cubefs-meta-sts-0 and kubectl logs cubefs-meta-sts-1DataNode Logs: kubectl logs cubefs-data-sts-0, etc.I/O Test: Write and read files on /mnt/cubefs to confirm everything is functioning. Conclusion Through its CNCF graduation, CubeFS achieves confirmed enterprise-grade status as a cloud-native storage system that withstands demanding data workloads. Organizations gain simple operational storage solutions that improve performance while optimizing resource usage through CubeFS’s scalable architecture and efficient Kubernetes integration, which also provides fault tolerance. CubeFS stands as a dependable choice thanks to features that consistently evolve due to active community backing empowered by CNCF graduation while providing key users support for modern storage solutions that handle any data volume.

By Sai Sandeep Ogety CORE

In today's technologically advanced society, fraud detection is a major concern. According to the Association of Certified Fraud Examiners (ACFE), fraud costs companies trillions of dollars worldwide, or nearly 5% of their yearly sales (ACFE Report to the Nations 2024). Businesses are increasingly using cutting-edge technology like artificial intelligence (AI) and machine learning (ML) as fraudsters become more skilled. Behavioral analytics is at the forefront of this movement and directly fights fraud. Problem Statement In today's modern era, fraud is growing beyond conventional methods because of utilizing the volume and velocity of online transactions. The primary challenges include: Transaction volume: Financial institutions conduct 100,000+ transactions a day on average, making manual reviews impracticable (Statista Report on Banking Transactions).Changing strategies: Con artists constantly modify their methods by using social engineering, fake identities, and credentials that have been stolen (FTC 2023 Fraud Trends Report). False positives: Overly conservative fraud detection can lead to $48 billion in false declines annually, frustrating customers and damaging business reputations (Juniper Research Report 2023). Understanding Behavioral Analytics To find abnormalities, behavioral analytics uses dynamic user behavior. It detects minute behavioral anomalies, including odd keystrokes, mouse movements, or transaction patterns, in real time, unlike static fraud detection systems. It identifies unusual typing speed during a login attempt in mobile applications, which is significantly quicker than the user's usual pattern and may indicate bot activity. Example from recent news: In 2024, a major retailer used behavioral analytics to uncover a coordinated fraud ring exploiting their gift card system. By analyzing transaction timings and purchase patterns, they flagged suspicious activity and prevented a potential loss of over $15 million (Retail Dive). In essence, behavior analytics are required in your product to stop malicious actors from abusing it. Let's examine the many industry trends that have been observed thus far to address this issue. AI/ML Techniques Empowering Behavioral Analytics 1. Supervised Learning Models This technique uses labeled datasets where input-output pairs are known.This uses ML train models to predict specific outcomes, such as fraud likelihood.Here are the algorithms usually used under this technique: Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks. Example: A bank used a neural network to analyze transaction data, achieving a 40% reduction in fraud-related losses within six months (Financial Times). 2. Unsupervised Learning Models This technique focuses mainly on identifying hidden patterns or groupings in the data instead of depending on labeled data. This technique is most effective for anomaly detection, clustering similar behaviors, and reducing dimensionality. This technique does not rely on labeled data; instead, it focuses on identifying hidden patterns or groupings in the data.The algorithms usually used in this technique are K-Means Clustering, Principal Component Analysis (PCA), and Isolation Forests. Example: PayPal employs clustering algorithms to monitor over $900 billion in payments annually, detecting outliers indicative of fraud (PayPal Annual Report 2023). 3. Reinforcement Learning This technique uses trial and error to learn optimum behaviors in a dynamic environment.This method is best suited for real-time fraud detection since adaptive strategies are required. Based on the decisions, the ML training model may receive rewards or penalties. Case study: A leading e-commerce platform integrated reinforcement learning to optimize fraud detection. This resulted in a 30% decrease in customer complaints due to false positives (McKinsey Insights). Key Features With Visuals 1. Anomaly Detection This technique focuses on learning optimal actions through trial and error in a dynamic environment. Diagram: A line chart showcasing normal transaction behaviors vs. flagged anomalies Anomaly detection is used to identify patterns in data that deviate significantly from anticipated behavior. These outliers, or anomalies, typically indicate crucial events such as fraud, system failures, or data errors. It employs machine learning, artificial intelligence, and statistics to detect aberrant activities in real time. Key Steps in Anomaly Detection Define normal behavior: Establish a baseline using historical or behavioral data (e.g., average transaction amounts or typical login times).Identify deviations: Use algorithms to detect deviations from the baseline.Flag anomalies: Label transactions or behaviors that exceed thresholds as anomalies for further investigation. Example of Anomaly Detection in Action Scenario: A Mobile Banking App An illustration of anomaly detection in action is provided. Typical conduct: In the United States, a user usually signs in from their home device and transfers modest sums once a week.Anomaly found: The same account attempts a high-value transfer while logging in from a fresh device in a foreign nation.Reactive: The software flags the transaction as suspicious and starts an additional verification procedure. By analyzing these deviations in real time, anomaly detection helps organizations respond quickly to threats and ensure legitimate users are minimally impacted. 2. Behavioral Heatmaps Behavioral heatmaps highlight places with high or low activity and graphically reflect user interactions. They are used in fraud detection to identify patterns or abnormalities in user behavior across several dimensions, such as app screens, transaction types, and geographic locations. Example chart: A heatmap illustrating high-risk geographies for fraud attempts Applications of Behavioral Heatmaps Geographic Fraud Detection Heatmaps show regions with unusual transaction activity, which helps to identify high-risk areas for fraud.Example: A spike in failed login attempts from a specific country can indicate a bot attack.In-App Behavior Monitoring This tracks which parts of an app users interact with the most. Unusual activity in least-used features may indicate malicious behavior.Example: A sudden increase in interactions with the account recovery feature could potentially signal account takeover attempts.Transaction Anomalies This highlights patterns in transaction volume or frequency.Example: A user initiating multiple high-value transactions in quick succession may trigger a fraud alert. 3. User Flow Diagram A user flow diagram visually represents the step-by-step process in which a system detects and responds to fraudulent behavior in real time. Below is an example tailored for a mobile app. Diagram: A flowchart showing real-time behavioral fraud detection — login attempts, transactional behavior, anomaly scoring, and action (approve/decline) Key Steps in the Flow Diagram User Login The user enters credentials like username, password, or biometric data, and behavioral data such as device details, typing speed, and location is captured.Behavioral Analytics Engine This includes analyzing login patterns against historical data and then assigning an anomaly score based on deviations (e.g., new device, location).Risk Assessment High-risk: Block access and notify the user or security team.Medium-risk: Prompt for additional verification (e.g., OTP, security question).Low-risk: Proceed to the app dashboard.Transaction Monitoring This includes user actions such as navigation patterns and transaction types in real time.Anomaly Handling This consists of flagging unusual transactions or interactions and temporarily holding high-risk activities for manual review or user confirmation.Fraud Prevention Action In the event that fraud is verified, reverse transactions, block the account or notify authorities.If it is real, then communicate the reported behavior to the user in order to maintain trust. Challenges With Supporting Data Here are possible challenges. Challenge Impact Example Data Privacy Regulations Compliance with GDPR/CCPA slows data collection EU fines on data misuse reached €2 billion in 2023 (EU GDPR Fines Tracker) Scale of Data Real-time analysis of millions of events Major banks process 1M+ transactions per minute (Statista Banking Data 2023) Adversarial Tactics Fraud mimics legitimate behaviors Fraudulent credit card use rose 15% in 2023 (FTC Report 2023) Conclusion Organizations struggle to detect fraud because of privacy regulations, the volume of data, and evolving adversarial tactics. Finding a balance between compliance, scalability, and agility is necessary to maintain effective and lawful fraud protection systems. To efficiently combat fraud, businesses must embrace AI/ML-driven behavioral analytics. Future innovations will likely include hybrid systems combining biometric and behavioral analytics for robust fraud prevention.Thanks for reading this! 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When you develop generative AI applications, you typically introduce three additional components to your infrastructure: an embedder, an LLM, and a vector database. However, if you are using MariaDB, you don't need to introduce an additional database along with its own SQL dialect — or even worse — its own proprietary API. Since MariaDB version 11.7 (and MariaDB Enterprise Server 11.4) you can simply store your embeddings (or vectors) in any column of any table—no need to make your applications database polyglots. "After announcing the preview of vector search in MariaDB Server, the vector search capability has now been added to the MariaDB Community Server 11.7 release," writes Ralf Gebhardt, Product Manager for MariaDB Server at MariaDB. This includes a new datatype (VECTOR), vector index, and a set of functions for vector manipulation. Why Are Vectors Needed in Generative AI Applications? Vectors are needed in generative AI applications because they embed complex meanings in a compact fixed-length array of numbers (a vector). This is more clear in the context of retrieval-augmented generation (or RAG). This technique allows you to fetch relevant data from your sources (APIs, files, databases) to enhance an AI model input with the fetched, often private-to-the-business, data. Since your data sources can be vast, you need a way to find the relevant pieces, given that current AI models have a finite context window — you cannot simply add all of your data to a prompt. By creating chunks of data and running these chunks of data through a special AI model called embedder, you can generate vectors and use proximity search techniques to find relevant information to be appended to a prompt. For example, take the following input from a user in a recommendation chatbot: Plain Text I need a good case for my iPhone 15 pro. Since your AI model was not trained with the exact data containing the product information in your online store, you need to retrieve the most relevant products and their information before sending the prompt to the model. For this, you send the original input from the user to an embedder and get a vector that you can later use to get the closest, say, 10 products to the user input. Once you get this information (and we'll see how to do this with MariaDB later), you can send the enhanced prompt to your AI model: Plain Text I need a good case for my iPhone 15 pro. Which of the following products better suit my needs? 1. ProShield Ultra Case for iPhone 15 Pro - $29.99: A slim, shock-absorbing case with raised edges for screen protection and a sleek matte finish. 2. EcoGuard Bio-Friendly Case for iPhone 15 Pro - $24.99: Made from 100% recycled materials, offering moderate drop protection with an eco-conscious design. 3. ArmorFlex Max Case for iPhone 15 Pro - $39.99: Heavy-duty protection with military-grade durability, including a built-in kickstand for hands-free use. 4. CrystalClear Slim Case for iPhone 15 Pro - $19.99: Ultra-thin and transparent, showcasing the phone's design while providing basic scratch protection. 5. LeatherTouch Luxe Case for iPhone 15 Pro - $49.99: Premium genuine leather construction with a soft-touch feel and an integrated cardholder for convenience. This results in AI predictions that use your own data. Creating Tables for Vector Storage To store vectors in MariaDB, use the new VECTOR data type. For example: MariaDB SQL CREATE TABLE products ( id INT PRIMARY KEY, name VARCHAR(100), description TEXT, embedding VECTOR(2048) ); In this example, the embedding column can hold a vector of 2048 dimensions. You have to match the number of dimensions that your embedder generates. Creating Vector Indexes For read performance, it's important to add an index to your vector column. This speeds up similarity searches. You can define the index at table creation time as follows: MariaDB SQL CREATE TABLE products ( id INT PRIMARY KEY, name VARCHAR(100), description TEXT, embedding VECTOR(2048) NOT NULL, VECTOR INDEX (embedding) ); For greater control, you can specify the distance function that the database server will use to build the index, as well as the M value of the Hierarchical Navigable Small Worlds (HNSW) algorithm used by MariaDB. For example: MariaDB SQL CREATE TABLE products ( id INT PRIMARY KEY, name VARCHAR(100), description TEXT, embedding VECTOR(2048) NOT NULL, VECTOR INDEX (embedding) M=8 DISTANCE=cosine ); Check the documentation for more information on these configurations. Inserting Vectors When you pass data (text, image, audio) through an embedder, you get a vector. Typically, this is a series of numbers in an array in JSON format. To insert this vector in a MariaDB table, you can use the VEC_FromText function. For example: MariaDB SQL INSERT INTO products (name, embedding) VALUES ("Alarm clock", VEC_FromText("[0.001, 0, ...]")), ("Cow figure", VEC_FromText("[1.0, 0.05, ...]")), ("Bicycle", VEC_FromText("[0.2, 0.156, ...]")); Remember that the inserted vectors must have the correct number of dimensions as defined in the CREATE TABLE statement. Similarity Search (Comparing Vectors) In RAG applications, you send the user input to an embedder to get a vector. You can then query the records in your database that are closer to that vector. Closer vectors represent data that are semantically similar. At the time of writing this, MariaDB has two distance functions that you can use for similarity or proximity search: VEC_DISTANCE_EUCLIDEAN: calculates the straight-line distance between two vectors. It is best suited for vectors derived from raw, unnormalized data or scenarios where spatial separation directly correlates with similarity, such as comparing positional or numeric features. However, it is less effective for high-dimensional or normalized embeddings since it is sensitive to differences in vector magnitude.VEC_DISTANCE_COSINE: measures the angular difference between vectors. Good for comparing normalized embeddings, especially in semantic applications like text or document retrieval. It excels at capturing similarity in meaning or context. Keep in mind that similarity search using the previous functions is only approximate and highly depends on the quality of the calculated vectors and, hence, on the quality of the embedder used. The following example, finds the top 10 most similar products to a given vector ($user_input_vector should be replaced with the actual vector returned by the embedder over the user input): MariaDB SQL SELECT id, name, description FROM products ORDER BY VEC_DISTANCE_COSINE( VEC_FromText($user_input_vector), embedding ) LIMIT 10; The VEC_DISTANCE_COSINE and VEC_DISTANCE_EUCLIDEAN functions take two vectors. In the previous example, one of the vectors is the vector calculated over the user input, and the other is the corresponding vector for each record in the products table. A Practical Example I have prepared a practical example using Java and no AI frameworks so you truly understand the process of creating generative AI applications leveraging MariaDB's vector search capabilities. You can find the code on GitHub.

By Alejandro Duarte CORE

As transitioning to the digital world is the norm today, businesses face the challenge of constantly maximizing performance while keeping a lookout for potential threats. However, whether it’s spotting fraud in banking and eCommerce, moderating content on social media and any other sites with user-generated content, or identifying anomalies, it is a balancing act between strong security and a smooth user experience. The iterative experimentation supported by A/B testing can serve as a valuable mechanism to fine-tune algorithms and the overall user experience across services in multiple sectors under the right conditions. Not only do these methods enhance people’s productivity — they develop trust and satisfaction from users as well. Why Experimentation Is Essential Contemporary systems frequently rely on intricate algorithms that influence two primary domains: Security and accuracy: Keeping the threats at bay while ensuring our systems run smoothly.User experience (UX): False hitting the path of legitimate users with pointless obstacles. Teams can systematically test and evaluate a variety of settings, decision points, or interface designs through experimentation. With data-driven insights, organizations optimize their systems, improve results, and provide scalable and user-centric solutions. Four Fundamental Vectors of Industry Experimentation 1. Tuning Algorithm Thresholds A common approach to system tuning is to experiment with different thresholds to identify actions to perform. It can highly improve your outcomes with this approach: Option A: Raise thresholds of verification, potentially flagging more transactions, posts, or activity. Alternatively, they can set a lower threshold, making it easier for users to pass but increasing the risk (option b). Metrics to Track False positive rate (aka the share of legitimate actions tagged wrongly)Fraud, spam, anomaly detection rates, etc.Customer satisfaction scores 2. Flexibly Responsive Adjustments to Context Study how systems deal with risk or operate when stakes are at their highest: Option A: Automated responses (biometric checks, default recommendations, etc.)Option B: Levi something more like iterations, alternative behavior logic, manual reviews or otherwise customized explanation. Metrics to Track User abandonment (or disengagement) metricsTime to resolution/process completionUser satisfaction rates post-responses 3. Improving Communication and Feedback from End-Users Look into different approaches to warn users of possible threats: Option A: Provide simple alerts, e.g., "An issue was detected."Option B: Send informative notifications, for example, "We noticed unusual activity on [platform/feature name]." Metrics to Track Trust scores from follow-up surveysRates of participation in support or feedback mechanismsUser-initiated reports or rectifying actions That allows the information to be straight to the point, focused on what really matters. How to Move from Experiments to Organizations Define Success Metrics Simply Track the right metrics for a good experience to your customers. Some of the key performance indicators: Correctness: In fraud or anomaly detection, this refers to accurately identifying fraudFalse positives reduction: Reducing the unwanted alertsUser satisfaction and NPS: Tracking NPS to understand customer loyalty Scale Well, But Start Small Start by testing your experiments with smaller user groups or systems. Once you get promising initial results, then you scale up. Using feature flagging to execute your experiments will help you control the risks associated with your implementation. Monitor Real-Time Metrics With online decision-making systems, you have to track streaming data while the experiment is running. This also helps in identifying issues early and rolling back quickly if needed. Use Segmentation to Gain Deeper Insights We would build an abstract model of system scenarios in which different types of users interact with systems in different ways. With micro-measurements, this starts to really pay off. The performance of each group within each segment can provide a clearer picture of real user behavior. Results: Balancing Security and User Experience This process of experimentation can help find the balance between system and user performance. example of which is a company testing out dynamic authentication in high-risk scenarios, may compare the efficacy of biometric verification against one-time passwords (OTPs). The outcomes might show that biometrics reduce completion times by 30%, and OTP, in some edge cases, increases overall trust, and users feel a greater sense of security. Conclusion Experimentation: Agility — making real, incremental changes that earn trust and network effects rather than just fine-tuning algorithms and adjusting settings. This leads to data-driven decision-making, reduced ambiguity, and flexibility to change as the need arises through the test-and-learn process. Adopting a culture of experimentation equips teams to deliver safe, smooth experiences that drive engagement, create loyalty, and achieve sustainable impact. This iterative experiment, if employed even if you are in banking, eCommerce, social media, or any other area, will lead to excellence and is a very powerful tool. Think big, start small, and experiment your way to amazing user experiences. Thanks for reading this! You can connect with us through Swapnil’s LinkedIn and Aditi's LinkedIn! References "The Role of A/B Testing in Fraud Detection Systems" – Tech Insights Blog"Best Practices for Experimentation in E-Commerce Platforms" – Digital Growth Strategies"How Real-Time Data Monitoring Enhances User Experience" – Analytics Today"Dynamic Authentication: Balancing Security and Usability" – Journal of Cybersecurity Research

Apache Paimon is made to function well with constantly flowing data, which is typical of contemporary systems like financial markets, e-commerce sites, and Internet of Things devices. It is a data storage system made to effectively manage massive volumes of data, particularly for systems that deal to analyze data continuously such as streaming data or with changes over time like database updates or deletions. To put it briefly, Apache Paimon functions similarly to a sophisticated librarian for our data. Whether we are operating a large online business or a little website, it keeps everything organized, updates it as necessary, and ensures that it is always available for use. An essential component of Apache Paimon's ecosystem, Apache Flink is a real-time stream processing framework that significantly expands its capabilities. Let's investigate how well Apache Paimon and Apache Flink work with each other so effectively. Handling Real-Time Data Streams Apache Paimon incorporates real-time streaming updates into the lake architecture by creatively fusing the lake format with a Log-Structured Merge Tree (LSM Tree). LSM Tree is a creative method for managing and organizing data in systems that process a lot of writes and updates, such as databases or storage systems. On other side, Flink serves as a powerful engine for refining or enhancing streaming data by modifying, enriching, or restructuring it upon arrival of incoming data streams (e.g., transactions, user actions, or sensor readings) in real-time. After that, it saves and refreshes these streams in Paimon, guaranteeing that the data is instantly accessible for further use, such as analytics or reporting. This integration makes it possible to maintain up-to-date datasets even in fast-changing environments. Consistent and Reliable Data Storage In real-time data systems, maintaining data consistency — that is, preventing missing, duplicate, or contradictory records — is one of the main issues. To overcome this, Flink and Paimon collaborate as follows: Flink adds filters, aggregations, or transformations after processing the events. Paimon ensures consistency in the results' storage, even in the event of updates, deletions, or late-arriving events. As an example, to guarantee that the inventory is always correct, Flink, for instance, may process order updates in an online shopping platform and feed them into Paimon. Support for Transactions in Streaming Workloads In order to guarantee data integrity, Paimon supports ACID transactions (Atomicity, Consistency, Isolation, Durability). This transactional model and Flink are closely integrated where writing data into Paimon guarantees that either the entire operation succeeds or nothing is written, avoiding partial or corrupted data. Ensuring exactly-once processing, meaning every piece of data is processed and stored exactly once, even if there are failures. Ensuring exactly-once processing, which means that, despite errors, each piece of data is processed and saved exactly once. In this transactional synergy, Flink and Paimon are a strong option for systems that need to be highly reliable. Real-Time Analytics and Querying Paimon is optimized for analytical queries on both real-time and historical data. With Flink, streaming data is immediately available for querying after being processed and stored in Paimon. Paimon organizes and indexes the data so that queries are fast, whether they target historical or current data. This integration allows businesses to perform real-time analytics, like detecting anomalies, generating live dashboards, or deriving customer insights, directly on Paimon’s storage. Streaming and Batch Support in One Flink is renowned for using the same engine to process both the batch and streaming data workloads. Paimon complements this by storing data in a format that is optimized for both types of workloads. By leveraging the capabilities of Flink to process both historical and streaming data together seamlessly, making Flink-Paimon combination is ideal for systems that need a unified approach to data processing, such as customer behavior analysis combining past and current interactions. Effective Data Compaction and Evolution Over time, the storage structure for streaming data can lead to fragmentation and inefficiencies. Flink and Paimon together address this, with Paimon organizing data into log-structured merge trees (LSM Trees), which handle frequent updates and deletes efficiently. On the other hand, Flink works with Paimon to compact and merge data periodically, ensuring that storage remains clean and queries remain fast. For instance, a social media platform can manage a high volume of user activity logs without storage inefficiencies. Real-time fraud detection is an example use case. Real-time fraud detection is crucial in a financial application. Incoming transactions are processed by Apache Flink, which then forwards them to Paimon after identifying any questionable trends or flagging suspicious patterns. Paimon stores these flagged transactions, ensuring they’re available for immediate review and long-term analysis. Analysts can query Paimon’s data to investigate fraud patterns and adjust Flink’s processing logic. This demonstrates how Paimon and Flink collaborate to build intelligent, real-time systems. Note:- Paimon currently supports Flink 1.20, 1.19, 1.18, 1.17, 1.16, 1.15 and at the moment, it offers two different kinds of jars. The bundled jar for read/write data, and the action jar for tasks like manual compaction. You can read here (https://paimon.apache.org/docs/master/flink/quick-start/) for a download and quick start with Flink. Takeaway Apache Flink is a crucial component of Apache Paimon since it offers real-time processing power that enhances Paimon's strong consistency and storage features. They work together to create a potent ecosystem for handling, processing, and evaluating rapidly evolving data, giving organizations the ability to make decisions instantly and obtain insights while preserving the efficiency and integrity of their data. I hope you enjoyed reading this. If you found this article valuable, please consider liking and sharing it.

By Gautam Goswami CORE

AWS Lambda is enhancing the local IDE experience to make developing Lambda-based applications more efficient. These new features enable developers to author, build, debug, test, and deploy Lambda applications seamlessly within their local IDE using Visual Studio Code (VS Code). Overview The improved IDE experience is part of the AWS Toolkit for Visual Studio Code. It includes a guided setup walkthrough that helps developers configure their local environment and install necessary tools. The toolkit also includes sample applications that demonstrate how to iterate on your code both locally and in the cloud. Developers can save and configure build settings to accelerate application builds and generate configuration files for setting up a debugging environment. With these enhancements, you can sync local code changes quickly to the cloud or perform full application deployments, enabling faster iteration. You can test functions locally or in the cloud and create reusable test events to streamline the testing process. The toolkit also provides quick action buttons for building, deploying, and invoking functions locally or remotely. Additionally, it integrates with AWS Infrastructure Composer, allowing for a visual application-building experience directly within the IDE. If anyone has worked with AWS Lambda, you will find IDE is not developer-friendly and has poor UI. It's hard to make code changes in the code and test from the present IDE. On top of that, if you don't want to use AWS-based CI/CD services, automated deployment can be a bit challenging for a developer. You can use Terraform or Github actions now, but AWS came up with another better option to deploy and test code AWS Lambda code. Considering these challenges, AWS Lambda recently announced the Visual Studio integration feature, which is a part of the AWS toolkit. It will make it easier for the developers to push, build, test, and deploy the code. This integration feature option uses Visual Studio. Although it still has restrictions on using 50 MB code size, it now provides a better IDE experience similar to what Visual Studio will be like on your local host. This includes dependencies installation with extension, split screen layout, writing code and running test events without opening new windows, and live logs from CloudWatch for efficient debugging. In addition, Amazon Q's in the console can be used as a coding assistant similar to a co-pilot. This provides a better developer experience. To start using Visual Studio for AWS Lambda: 1. You should have Visual Studio locally installed. After that, install the AWS Toolkit from the marketplace. You will see that the webpage will redirect to Visual Studio and open this tab. You can go ahead and install this. 2. After installing the AWS Toolkit, you will see the AWS logo on the left sidebar under extensions. Click on that. 3. Now, select the option to connect with your AWS account. 3. After a successful connection, you will get a tab to invoke the Lambda function locally. As you can see below, this option requires AWS SAM installed to invoke Lambda locally. After login, it will also pull all your Lambda functions from your AWS account. If you want to update those, you can right-click on the Lambda function and select Upload Lambda. It will ask you for the zip file of the Lambda function. Alternatively, you can select samples from the explorer option in the left sidebar. If you want to go with remote invoke, you can click on any Lambda functions visible to you from the sidebar. 4. If you want to create your own Lambda function and test the integration, you can click on the Application Builder option and select AWS CLI or SAM. If you want the Lambda code to deploy to the AWS account, you can select the last option, as shown in the above screenshot. After that, if you log into your AWS account, you will be asked to log in. Then, it will let you deploy AWS code. This way, you can easily deploy AWS code from your IDE, which can be convenient for developer testing. Conclusion Lambda is enhancing the local development experience for Lambda-based applications by integrating with the VS Code IDE and AWS Toolkit. This upgrade simplifies the code-test-deploy-debug workflow. A step-by-step walkthrough helps you set up your local environment and explore Lambda functionality through sample applications. With intuitive icon shortcuts and the Command Palette, you can build, debug, test, and deploy Lambda applications seamlessly, enabling faster iteration without the need to switch between multiple tools.

Controlled Unclassified Information (CUI) requires careful identification and classification to ensure compliance with frameworks like CMMC and FedRAMP. For developers, building automated systems to classify CUI involves integrating machine learning, natural language processing (NLP), and metadata analysis into document-handling workflows. Key Challenges in CUI Document Classification 1. Ambiguity in Definitions CUI categories often overlap with non-sensitive data, making manual classification error-prone. 2. Scalability Large organizations may handle millions of documents, requiring automated classification systems. 3. Compliance Standards Ensuring the classification process adheres to NIST SP 800-171 and CMMC Level 2 requirements. Automating CUI Classification Step 1: Define Classification Criteria Start by understanding the CUI categories relevant to your organization. Examples include: Export control: Data related to international trade regulations.Critical infrastructure: Information about energy, transportation, and other critical systems.Financial records: Bank details and financial transaction data. Implementing a Classification Schema Develop a JSON schema to represent classification categories: JSON { "CUI_Category": "Export Control", "Subcategory": "International Traffic", "Keywords": ["export", "license", "regulation"], "Security_Level": "Confidential" } Step 2: Automate Document Identification Leverage machine learning and NLP to identify sensitive documents. Example: Using Python With SpaCy for Keyword Extraction Python import spacy # Load NLP model nlp = spacy.load("en_core_web_sm") # Define keywords for CUI classification keywords = ["export", "regulation", "license"] # Analyze document def classify_document(text): doc = nlp(text) for token in doc: if token.text.lower() in keywords: return "CUI - Export Control" return "Non-CUI" # Test with sample document document = "This document contains export regulations." print(classify_document(document)) Step 3: Integrate Metadata Analysis Use metadata tags (e.g., author, creation date, sensitivity level) for automated classification. Example: Extracting Metadata With PyPDF2 Python from PyPDF2 import PdfFileReader def extract_metadata(pdf_file): pdf = PdfFileReader(open(pdf_file, 'rb')) metadata = pdf.getDocumentInfo() return { "Author": metadata.author, "Title": metadata.title, "CreationDate": metadata["/CreationDate"], "CUI_Status": "CUI" if "export" in metadata.title.lower() else "Non-CUI" } # Example usage metadata = extract_metadata("document.pdf") print(metadata) Step 4: Develop a Classification Pipeline Combine NLP, metadata analysis, and user-defined rules to classify documents at scale. Using Apache Tika for Content Extraction Apache Tika can extract text and metadata from various file types: SQL tika --text document.docx > output.txt tika --metadata document.docx Integrating the Workflow Extract document content with Tika.Use NLP models for keyword-based classification.Cross-reference metadata for validation.Store results in a database for compliance tracking. Step 5: Implement Machine Learning for Advanced Classification Train a supervised learning model using labeled datasets of CUI and non-CUI documents. Example: Using Scikit-Learn for Document Classification Python from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # Training data documents = ["export regulations", "meeting notes", "financial transactions"] labels = ["CUI", "Non-CUI", "CUI"] # Vectorize text vectorizer = CountVectorizer() X = vectorizer.fit_transform(documents) # Train model model = MultinomialNB() model.fit(X, labels) # Classify new document new_doc = ["license for export"] X_new = vectorizer.transform(new_doc) print(model.predict(X_new)) Step 6: Enhance Usability with a Web Interface Build a user interface for manual verification and correction. Example: Flask Application for Classification Python from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/classify', methods=['POST']) def classify(): data = request.json result = classify_document(data['text']) # Use NLP function here return jsonify({"classification": result}) if __name__ == '__main__': app.run(debug=True) Step 7: Deploy Classification Models in the Cloud Host your classification pipeline using AWS, Azure, or GCP for scalability. Use serverless functions like AWS Lambda to process documents in real time. AWS Lambda Example Python import boto3 def lambda_handler(event, context): s3 = boto3.client('s3') bucket = event['bucket'] key = event['key'] file_obj = s3.get_object(Bucket=bucket, Key=key) content = file_obj['Body'].read().decode('utf-8') # Classify document classification = classify_document(content) return {"classification": classification} Step 8: Integrate Compliance Reporting Store classification results and metadata in a database for compliance tracking. Example: Logging Results in MongoDB Python from pymongo import MongoClient client = MongoClient("mongodb://localhost:27017/") db = client.cui_classification def log_classification(doc_id, classification, metadata): db.logs.insert_one({ "doc_id": doc_id, "classification": classification, "metadata": metadata }) Conclusion CUI document identification and classification are critical for regulatory compliance. By leveraging tools like NLP, machine learning, and metadata analysis, developers can automate these processes efficiently. This guide provides the technical foundation to design and deploy a scalable classification pipeline. With additional integration into CI/CD and cloud platforms, organizations can ensure consistent compliance across workflows.

Human Capital Management (HCM) cloud systems, such as Oracle HCM and Workday, are vital for managing core HR operations. However, migrating to these systems and conducting necessary testing can be complex. Robotic Process Automation (RPA) provides a practical solution to streamline these processes. Organizations can accelerate the implementation and operationalization of HCM cloud applications through data migration, managing multi-factor authentication (MFA), assigning roles post-deployment, and conducting User Acceptance Testing (UAT). This article offers practical guidance for finance and IT teams on leveraging RPA tools to enhance HCM cloud implementation. By sharing best practices and real-world examples, we aim to present a roadmap for effectively applying RPA across various HCM platforms to overcome common implementation challenges. Introduction Through our work with HCM cloud systems, we’ve witnessed their importance in managing employee data, payroll, recruitment, and compliance. However, transitioning from legacy systems presents challenges such as complex data migration, secure API integrations, and multi-factor authentication (MFA). Additionally, role-based access control (RBAC) adds compliance complexities. Robotic Process Automation (RPA) can automate these processes, reducing manual efforts and errors while improving efficiency. This white paper explores how RPA tools, especially UiPath, can address these challenges, showcasing use cases and practical examples to help organizations streamline their HCM cloud implementations. Role of RPA in HCM Cloud Implementation and Testing RPA provides a powerful means to streamline repetitive processes, reduce manual efforts, and enhance operational efficiency. Below is the list of areas where RPA plays a key role in HCM cloud implementation and testing. 1. Automating Data Migration and Validation Migrating employee data from legacy systems to HCM cloud platforms can be overwhelming, especially with thousands of records to transfer. In several migration projects we managed, ensuring accuracy and consistency was critical to avoid payroll or compliance issues. Early on, we realized that manual efforts were prone to errors and delays, which is why we turned to RPA tools like UiPath to streamline these processes. In one project, we migrated employee data from a legacy payroll system to Oracle HCM. Our bot read records from Excel files, validated missing IDs and job titles, and flagged errors for quick resolution. This automation reduced a two-week manual effort to just a few hours, ensuring an accurate and smooth transition. Without automation, these discrepancies would have caused delays or disrupted payroll, but the bot gave our HR team confidence by logging and isolating issues for easy correction. Lessons from Experience Token refresh for API access: To prevent disruptions, we implemented automatic token refresh logic, ensuring smooth uploads.Batch processing for efficiency: In high-volume migrations, batch processing avoided API rate limits and system timeouts.Comprehensive error logging: Detailed logs allowed us to pinpoint and resolve issues without needing full reviews.Validation at key stages: Bots validated data both before and after migration, ensuring compliance and data integrity. Seeing firsthand how automation reduced errors, saved time, and gave HR teams peace of mind has been deeply rewarding. These experiences have confirmed my belief that RPA isn’t just a tool — it’s essential for ensuring seamless, reliable HCM transitions. 2. Handling Multi-Factor Authentication (MFA) and Secure Login Many cloud platforms require Multi-Factor Authentication (MFA), which disrupts standard login routines for bots. However, we have addressed this by programmatically enabling RPA bots to handle MFA through integration with SMS or email-based OTP services. This allows seamless automation of login processes, even with additional security layers. Example: Automating Login to HCM Cloud With MFA Handling In one of our projects, we automated the login process for an HCM cloud platform using UiPath, ensuring smooth OTP retrieval and submission. The bot launched the HCM portal, entered the username and password, retrieved the OTP from a connected SMS service, and completed the login process. This approach ensured that critical workflows were executed without manual intervention, even when MFA was enabled. Best Practices from Experience Secure credential management: Stored user credentials in vaults to protect sensitive data.Seamless OTP integration: Integrated bots with external OTP services, ensuring secure and real-time code retrieval.Validation and error handling: Bots were designed to log each login attempt for easy tracking and troubleshooting. This method not only ensured secure access but also improved operational efficiency by eliminating the need for manual logins. Our collaborative efforts using RPA have enabled businesses to navigate MFA challenges smoothly, reducing downtime and maintaining continuity in critical processes. 3. Automating Role-Based Access Control (RBAC) Setup It’s essential that users are assigned the correct authorizations in an HCM cloud, with ongoing maintenance of these permissions as individuals transition within the organization. Even with a well-defined scheme in place, it’s easy for someone to be shifted into a role that they shouldn’t hold. To address this challenge, we have leveraged RPA to automate the assignment of roles, ensuring adherence to least-privilege access models. Example: Automating Role Assignment Using UiPath In one of our initiatives, we automated the role assignment process by reading role assignments from an Excel file and executing API calls to update user roles in the HCM cloud. The bot efficiently processed the data and assigned the appropriate roles based on the entries in the spreadsheet. The automation workflow involved reading the role assignments, iterating through each entry, and sending HTTP requests to the HCM cloud API to assign roles. This streamlined approach not only improved accuracy but also minimized the risk of human error in role assignments. Best Practices from Experience Secure credential management: We utilized RPA vaults or secret managers, such as HashiCorp Vault, to securely manage bot credentials, ensuring sensitive information remains protected.Audit logging: Implementing comprehensive audit logs allowed us to track role changes effectively, providing a clear history of modifications and enhancing accountability. By automating role assignments, we ensured that users maintained the appropriate access levels throughout their career transitions, aligning with compliance requirements and enhancing overall security within the organization. Our collaborative efforts in implementing RPA have significantly improved the management of user roles, contributing to a more efficient and secure operational environment. 4. Automated User Acceptance Testing (UAT) User Acceptance Testing (UAT) is a critical phase in ensuring that HCM cloud systems meet business requirements before going live. To streamline this process, we implemented RPA bots capable of executing predefined UAT scenarios, comparing expected and actual results, and automatically logging the test results. This automation not only accelerates the testing phase but also ensures that any issues are identified and resolved before the system goes live. In one of our initiatives, we developed a UiPath workflow that executed UAT scenarios from an Excel sheet, capturing the outcomes of each test. By systematically verifying each functionality, we ensured that the system performed as intended, significantly reducing the risk of post-deployment issues. Best Practices from Experience Automate end-to-end scenarios: We ensured higher test coverage by automating comprehensive end-to-end scenarios, providing confidence that the system meets all functional requirements.Report generation for UAT results: By implementing automated report generation for UAT results, we maintained clear documentation of test outcomes, facilitating transparency and accountability within the team. Through our collaborative efforts in automating UAT, we significantly improved the testing process, allowing for a smooth and successful go-live experience. 5. API Rate Limits and Error Handling With Exponential Backoff Integrating with HCM systems through APIs often involves navigating rate limits that can disrupt workflows. To address this challenge, we implemented robust retry logic within our RPA bots, utilizing exponential backoff to gracefully handle API rate limit errors. This approach not only minimizes disruptions but also ensures that critical operations continue smoothly. In our projects, we established a retry mechanism using UiPath that intelligently handled API requests. By incorporating an exponential backoff strategy, the bot could wait progressively longer between retries when encountering rate limit errors, thereby reducing the likelihood of being locked out. Best Practices from Experience Implement retry logic: We incorporated structured retry logic to handle API requests, allowing the bot to efficiently manage rate limits while ensuring successful execution.Logging and monitoring: By logging attempts and outcomes during the retry process, we maintained clear visibility into the bot's activities, which facilitated troubleshooting and optimization. By effectively managing API rate limits and implementing error-handling strategies, our collaborative efforts have enhanced the reliability of our automation initiatives, ensuring seamless integration with HCM systems and maintaining operational efficiency. Conclusion RPA tools significantly accelerate the implementation and testing of Human Capital Management (HCM) cloud systems by automating complex and repetitive tasks. This includes data migration, multifactor authentication (MFA) handling, role-based access setup, user acceptance testing (UAT) execution, and error handling. By automating these processes, organizations can complete them more quickly, without the need for human intervention, resulting in fewer errors. Organizations that adopt RPA for HCM cloud projects can achieve several key benefits: Faster deployment timelines: Automation reduces the time required for implementation and testing, allowing organizations to go live more swiftly.Improved data accuracy: Automated processes minimize the risk of human error during data migration and other critical tasks, ensuring that information remains accurate and reliable.Better compliance: RPA helps organizations adhere to security protocols and regulations by consistently managing tasks that require strict compliance measures. To fully realize the benefits of RPA in scaling HCM cloud implementations and maintaining operational efficiency over time, organizations should follow best practices. These include secure credential management, effective exception handling, and comprehensive reporting. By doing so, enterprises can leverage RPA to optimize their HCM cloud systems effectively.