Machine Learning Operations (MLOps) 

This intensive course provides a condensed introduction to the core principles, tools, and practices of MLOps. It's designed for developers, data scientists, and anyone interested in understanding the production lifecycle of machine learning models.

Target Audience

• Data Scientists

• Machine Learning Engineers

• Software Developers

• DevOps Engineers

• Anyone interested in the operational aspects of deploying and managing machine learning models in production

Course Overview

The Machine Learning Operations (MLOps) landscape is rapidly evolving, and this course equips you with the foundational knowledge and practical skills to navigate this critical field.  Over the course of this program, you'll gain insights into the challenges and best practices for deploying, monitoring, and managing machine learning models in production environments.

Through a combination of lectures, hands-on labs, and group discussions, you'll explore key MLOps concepts like version control, CI/CD pipelines, containerization, model serving frameworks, and monitoring tools.

Course Structure

The course will be delivered in a fast-paced and interactive format, combining lectures, hands-on labs, and group discussions. Labs will focus on practical skills using open-source tools to experience key MLOps concepts.

Course Duration: 4 days

Course Outline

Module 1: MLOps Fundamentals

• Introduction to MLOps:

  • What is MLOps and why is it important?

  • Challenges of deploying ML models in production.

  • The Machine Learning Lifecycle: Data Acquisition, Preprocessing, Feature Engineering, Model Training, Evaluation, Deployment, Monitoring.

• Hands-on Lab 1: Introduction to Git for Version Control (focus on code and model versioning)

• CI/CD for MLOps:

  • Importance of Continuous Integration and Continuous Delivery (CI/CD) for ML pipelines.

  • Tools for CI/CD: Introduction to Jenkins or GitLab CI/CD.

• Hands-on Lab 2: Building a simple CI/CD pipeline for model training (using a chosen CI/CD tool)

Module 2: Model Deployment and Management

• Model Deployment Strategies:

  • Different approaches to deploying ML models (cloud vs. on-premise)

  • Introduction to containerization with Docker for MLOps.

• Hands-on Lab 3: Dockerizing a simple ML model for deployment

• Model Serving Frameworks:

  • Deploying models as APIs with frameworks like TensorFlow Serving or PyTorch Serving.

• Hands-on Lab 4: Deploying a containerized model as an API using a serving framework

Module 3: Monitoring and Observability

• Importance of Model Monitoring:

  • Monitoring metrics for model performance (e.g., accuracy, precision, recall).

  • Detecting model drift and performance degradation.

• Hands-on Lab 5: Implementing basic model monitoring with tools like Prometheus or Grafana

• A/B Testing and Feature Flagging:

  • A/B testing for evaluating new models in production.

  • Feature flagging for controlling model rollouts.

• Introduction to tools for A/B testing and feature flagging (e.g., Flagsmith)

Module 4: Putting it Together & The Future

• MLOps Case Studies:

  • Exploring real-world examples of successful MLOps implementations.

• Best Practices and Considerations:

  • Security considerations for deploying ML models.

  • Ethical guidelines for MLOps (brief overview).

• The Future of MLOps:

  • Emerging trends and advancements in MLOps tools and technologies.