A Deep Dive Into AIOps and MLOps
Tech leaders are becoming more and more interested in MLOps and AIOps. Discover how MLOps and AIOps can work together to accelerate DevOps efficiency.
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This is an article from DZone's 2023 DevOps Trend Report.
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Monitoring and managing a DevOps environment is complex. The volume of data generated by new distributed architectures (such as Kubernetes) makes it difficult for DevOps teams to effectively respond to customer requests. The future of DevOps must therefore be based on intelligent management systems. Since humans are not equipped to handle the massive volumes of data and computing in daily operations, artificial intelligence (AI) will become the critical tool for computing, analyzing, and transforming how teams develop, deliver, deploy, and manage applications.
What Are Machine Learning Operations?
Machine learning operations (MLOps) refers to the lifecycle management of machine learning (ML) projects. It is a key concept of modern machine learning application development, and its purpose is to make the training, deploying, and maintaining of machine learning applications seamless and efficient. MLOps is not a set of specific technologies but rather an umbrella term for activities focused on building reliable and well-functioning machine learning models. It includes both development work practices and ways of working as a project team — essentially functioning as a set of best practices for machine learning application development.
With the application of MLOps principles, data scientists can focus on the core development of machine learning models while the MLOps practices take care of tasks such as data cleaning, quality control, and model versioning.
Applying MLOps equally benefits business owners and clients. Automation increases the velocity of development, leading to faster results and more reliable machine learning models. This leads to shorter development times that, in turn, bring faster end-result delivery and cost-effectiveness. Finally, automated quality control ensures more reliable solutions that are ensured and tested to function as intended, reducing the risk of faulty deployments.
Lifecycle of a Machine Learning Model
The lifecycle of a machine learning project differs from a traditional application. The diagram in Figure 1 details the steps to deploy a machine learning project in production:
Figure 1: Machine learning model lifecycle
- Data Extraction – ingesting data from various sources
- Exploratory Data Analysis – understanding the data format
- Data Preparation – cleaning and processing the data for easy processing
- Model Training – creating and training a model to process the data
- Model Validation and Evaluation – evaluating the model on test data to validate the performances
- Model Versioning – releasing a version of the model
- Model Deployment – deploying the model in production
Core Elements of MLOps
There are several machine learning frameworks that allow you to deploy, manage, and monitor models — for example, KubeFlow is a toolkit that simplifies model management on the Kubernetes platform. A toolkit should be composed of:
- A version control to keep track of any changes in the datasets or the models
- A feature store to centralized data and frequently used features
- A tracker to monitor the performance of models in training
- A tool to train models using a set of optimal hyperparameters automatically
- A platform to deploy models in production
- A monitoring tool to track and govern machine learning models deployed in production
What Are Artificial Intelligence Operations?
Gartner, creator of the term "artificial intelligence operations" (AIOps), defines it as the utilization of big data and machine learning to automate IT operations tasks, such as event correlation, identifying unusual events, and determining cause and effect. AIOps uses big data, analytics, and AI algorithms to analyze vast amounts of data generated by IT systems and applications in real-time. This data includes log files, performance metrics, and security events, among others. The AI algorithms process this data to identify patterns, detect anomalies, and generate insights that can help IT teams resolve incidents quickly and prevent potential problems before they occur.
AIOps solutions can also automate manual tasks such as event correlation, root cause analysis, and incident resolution, freeing IT teams to focus on more strategic initiatives. AIOps can also help organizations achieve faster problem resolution, reduced downtime, and improved overall IT operations efficiency. It helps teams to work faster and smarter by unleashing the power of AI.
The core capabilities of AIOps that enable efficient digitization of workflows are:
- Process optimization – Enhances efficiency throughout the enterprise by giving a comprehensive understanding of the connections and effects between systems. After identifying a problem, it facilitates refinement and ongoing monitoring of processes.
- Performance analytics – Anticipates performance bottlenecks by examining trends and making necessary improvements as needed.
- Predictive intelligence – Utilizes machine learning to categorize incidents, suggest solutions, and proactively alert critical issues.
- AI search – Offers precise, personalized answers through semantic search capabilities.
- Configuration management database – Enhances decision-making with visibility into the IT environment by connecting products throughout the digital lifecycle, allowing teams to comprehend impact and risk.
Figure 2: AIOps lifecycle
Core Element of AIOps
AIOps definitions vary among enterprises as each enterprise has unique needs and approaches to implementing AI solutions in IT operations. The primary objective of AIOps is to identify and respond to real-time issues efficiently. Some core components of AIOps can assist in the implementation of AI in IT operations:
- ML-based pattern discovery – AIOps or IT analytics involves identifying patterns. Machine learning leverages the computational capability of computers to identify these patterns in IT data.
- Anomaly detection – Unusual system behavior, such as downtime or poor customer experience, can occur from changes in normal behavior. AIOps enables the detection of any deviations from typical activities.
- Predictive insights – AIOps introduces predictability in IT operations, enabling IT staff to proactively address issues before they occur, ultimately reducing the number of service desk tickets.
- Automated root cause analysis – Simply having insights isn't enough. It's important to take action. In traditional IT management, staff monitor systems and take action as needed. However, with the growing volume of IT infrastructure issues, it can be difficult for staff to manage and resolve issues in a timely manner, especially when multiple systems are involved and root cause analysis can be time-consuming. AIOps automates this process in the background.
AIOps tools gather data from multiple sources to provide a comprehensive view of IT operations. They collect data such as application logs and measure system performance, breaking down silos of IT information and bridging the gap between software, hardware, and cloud issues. AIOps solutions aid IT operations by providing tools for root cause analysis, event correlation, and cloud mapping to support automation:
- Intelligent observability – AIOps employs advanced monitoring techniques with the use of contextual information, AI, and automation to gain a complete understanding of IT issues. Precise root cause analysis with actionable insights is provided.
- AI-assistance – Performs efficient and error-free root cause analysis. Precise and reproducible results are achieved with the AI engine integrated into every aspect.
What Is the Difference Between MLOps and AIOps?
Coupled with the increasing complexity of architectures of modern applications, the demands of this digital economy have made the role of IT operations much more complex. As a result, ML and AI have emerged to automate some manual business processes to increase efficiency.
MLOps and AIOps both aim to serve the same end goal: business automation. While MLOps bridges the gap between model building and deployment, AIOps focus on supporting and reacting to issues in real time and providing analytics to the operations team. AIOps combines big data and machine learning to automate performance monitoring, event analysis, correlation, and IT automation.
Figure 3: AIOps vs MLOps vs DevOps
MLOps, on the other hand, focuses on managing training and testing data that is needed to create machine learning models effectively. It is all about monitoring and managing ML models. In other words, MLOps standardizes processes whereas AIOps automates machine monitoring.
There are parallels in the teams and abilities needed to properly execute AIOps and MLOps, despite the obvious distinctions. It is worthwhile to consider where they intersect to determine which resources can support both disciplines.
Organizations throughout the world are increasingly looking to automation technologies as a means of improving operational efficiency. This indicates that tech leaders are becoming more and more interested in MLOps and AIOps.
Machine learning systems can simplify data collection from various parts of the DevOps system like velocity, defects found, and burn rate. MLOps takes care of the continuous integration and deployment of the models. It allows users to shed light on important patterns and exploit data to extract meaningful information. It also implies surveillance and continuous model training in production in order to ensure the reliability and stability of those models.
AIOps can play a crucial role in accelerating DevOps efficiency. It is defined as the usage of big data and machine learning to automate operations such as event correlation, determining cause and effect, and identifying unusual events.
In other words, MLOps and AIOps can work together. Artificial intelligence will help boost performance by enabling instant development and operations cycles, and by delivering a compelling customer experience on these features. Machine learning will enable companies to gather metrics such as the number of integrations, the time between them, their success rate, and defects per integration, which are only valuable when they are accurately evaluated and correlated.
This is an article from DZone's 2023 DevOps Trend Report.
Read the Report
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