Machine Learning Operations (MLOps) - ML Lifecycle

Overview

This course focuses on the implementation and management of machine learning models throughout their lifecycle. It provides ML engineers, data scientists, and data engineers with a comprehensive understanding of MLOps concepts, tools, and best practices. The course covers key stages of the ML lifecycle, including data versioning, feature store, model governance, deployment, scaling, optimization, and monitoring. Participants will learn how to automate processes, establish feedback loops, and ensure continuous integration, continuous delivery (CI/CD), and retraining of models.

Target Audience

• ML engineers
• Data scientists
• Data engineers

Prerequisites

• Basic knowledge of Python
• Familiarity with machine learning concepts and ML in Python

Content

Day 1:

• Introduction to MLOps
  • Overview of MLOps and its importance
  • Key components and challenges in the ML lifecycle
  • Roles and responsibilities of stakeholders
• ML Lifecycle and Steps
  • Overview of the ML lifecycle stages
  • Data collection, preprocessing, and labelling
  • Model training, validation, and evaluation
  • Model deployment, monitoring, and maintenance
• Data Versioning
  • Importance of data versioning in ML projects
  • Techniques for managing data versioning
  • Tools and platforms for data versioning
• Feature Store
  • Introduction to feature stores and their benefits
  • Building and managing feature stores
  • Integration of feature stores with ML pipelines
• Model Governance and Experiment Management
  • Best practices for managing ML experiments
  • Version control and tracking of models
  • Model governance

Day 2:

• Model Deployment
  • Strategies for deploying ML models
  • Containerization and orchestration of ML models
• Model Scaling and Optimization
  • Techniques for scaling ML models
  • Optimization methods for improving model performance
• Monitoring ML Models
  • Importance of model monitoring in production
  • Health checks and performance metrics
  • Detection of data and model drift
• Feedback Loop and Continuous Improvement
  • Establishing a feedback loop for model updates
  • Incorporating user feedback into model retraining
• Automation and Retraining
  • Automating ML workflows and pipelines
  • Continuous retraining of models
  • Integration of MLOps with CI/CD processes

For all inquiries regarding education, please contact us at learn@croz.net.

More information:

• Automating ML Factory
• MLflow at CROZ
• MLOps pipeline in Kubeflow using MLflow