DevOps for AI and Machine Learning

Apply DevOps practices to AI and machine learning model development, testing, deployment, and operations through practical MLOps workflows.

Description

MLOps is the application of DevOps practices and principles to the machine learning process. In DevOps for AI and Machine Learning, DevOps engineers and testers learn foundational knowledge and practical skills to integrate machine learning operations into existing AI model development workflows.

This workshop covers the full MLOps lifecycle, focusing on concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments. Through practical, hands-on exercises, participants gain real-world experience in areas such as feature stores, CI/CD for ML models, and model deployment and monitoring.

Key takeaways from this class include:

Gaining a deep understanding of MLOps principles and integration with DevOps practices
Setting up automated data engineering pipelines
Tracking model experiments effectively
Implementing CI/CD pipelines for machine learning models
Establishing robust monitoring and retraining strategies
Navigating security and compliance considerations in MLOps

Who Should Attend

This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand into MLOps. It is also valuable for professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who want to understand best practices for deploying and maintaining machine learning models.

The course is designed for participants with a technical DevOps background and limited machine learning exposure, helping bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.

Course Duration and Schedule

Two-Day Format

8:30 AM - 4:30 PM each day with a 1-hour lunch break and morning and afternoon breaks.

Three-Day Format

11:30 AM - 5:00 PM each day with afternoon breaks.

Upcoming Training

There are currently no scheduled classes for this course. If you would like to request one, click here for more information.

Request a Class

Course Outline

Session 1: Introduction to AI, ML, and MLOps

What is Artificial Intelligence (AI)?
Concerns in AI and machine learning
Steps and challenges in the ML process
Definition and importance of MLOps
Key MLOps activities
Example AI-based application
Exercise #1: Review example application

Session 2: MLOps During Model Development

What is model development?
Model experimentation process
Exercise #2: Using Jupyter notebooks
Creation of training datasets
Model experimentation
Experiment tracking
Exercise #3: Experiment tracking with MLflow
Model training pipelines
Exercise #4: Using nbconvert to export notebook code

Session 3: MLOps During Model Testing and Deployment

Approaches to testing and deploying AI models
Types of testing to perform
Model management
Using a model registry for model tracking
Exercise #5: Using MLflow to register and track models
Continuous delivery and deployment process
Exercise #6: Test and deploy models using MLflow and pytest
Online experimentation
Integration of AI models into applications

Session 4: MLOps During Model Inference and Monitoring

Prediction serving process
Model monitoring
Types of model monitoring
Dealing with model decay
Exercise #7: Identify types of model decay
Model retraining

Session 5: MLOps for Dataset and Feature Engineering

What are features?
Defining and managing features
Dataset management
Using feature stores
Exercise #8: Identifying steps in dataset and feature management

Session 6: Model Governance and Compliance

Model governance versus compliance
Types of model governance
Explainability
Fairness and bias
Data security and privacy
Model compliance standards
Exercise #9: Identifying types of governance

Wrap up & Next steps

Comprehensive ML workflow review
Summary and wrap-up of the course
References
Q&A session to address participant questions