DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Please enter at least three characters to search
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Because the DevOps movement has redefined engineering responsibilities, SREs now have to become stewards of observability strategy.

Apache Cassandra combines the benefits of major NoSQL databases to support data management needs not covered by traditional RDBMS vendors.

The software you build is only as secure as the code that powers it. Learn how malicious code creeps into your software supply chain.

Generative AI has transformed nearly every industry. How can you leverage GenAI to improve your productivity and efficiency?

Related

  • DevOps: The Key to Reliable AI Data and Governance
  • How To Use Artificial Intelligence to Optimize DevOps
  • When ML Meets DevOps: How To Understand MLOps
  • What Is Model Ops?

Trending

  • Introduction to Retrieval Augmented Generation (RAG)
  • Exploring Intercooler.js: Simplify AJAX With HTML Attributes
  • IoT and Cybersecurity: Addressing Data Privacy and Security Challenges
  • How GitHub Copilot Helps You Write More Secure Code
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. A Deep Dive Into AIOps and MLOps

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.

By 
Hicham Bouissoumer user avatar
Hicham Bouissoumer
DZone Core CORE ·
Nicolas Giron user avatar
Nicolas Giron
·
Feb. 21, 23 · Analysis
Likes (7)
Comment
Save
Tweet
Share
7.5K Views

Join the DZone community and get the full member experience.

Join For Free

This is an article from DZone's 2023 DevOps Trend Report.

For more:


Read the Report

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: 

Machine learning model lifecycle

Figure 1: Machine learning model lifecycle

  1. Data Extraction – ingesting data from various sources
  2. Exploratory Data Analysis – understanding the data format
  3. Data Preparation – cleaning and processing the data for easy processing
  4. Model Training – creating and training a model to process the data
  5. Model Validation and Evaluation – evaluating the model on test data to validate the performances
  6. Model Versioning – releasing a version of the model
  7. 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: 

  1. 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.
  2. Performance analytics – Anticipates performance bottlenecks by examining trends and making necessary improvements as needed.  
  3. Predictive intelligence – Utilizes machine learning to categorize incidents, suggest solutions, and proactively alert critical issues.
  4.  AI search – Offers precise, personalized answers through semantic search capabilities.
  5. 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.

AIOps lifecycle

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: 

  1. 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.
  2. 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.
  3. 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.  
  4. 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 Toolset

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: 

  1. 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.
  2. Continuous automation – Reduces manual effort in deployment, configuration, and management and automatically identifies and assesses the severity of issues in terms of user and business impact. Achieving continuous discovery, effortless deployments, and automatic dependency mapping is made possible. 
  3. 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. 

AIOps vs MLOps vs DevOps

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. 

Conclusion

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.

For more:


Read the Report

AI DevOps Machine learning

Opinions expressed by DZone contributors are their own.

Related

  • DevOps: The Key to Reliable AI Data and Governance
  • How To Use Artificial Intelligence to Optimize DevOps
  • When ML Meets DevOps: How To Understand MLOps
  • What Is Model Ops?

Partner Resources

×

Comments
Oops! Something Went Wrong

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends:

Likes
There are no likes...yet! 👀
Be the first to like this post!
It looks like you're not logged in.
Sign in to see who liked this post!