Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.
Overview
Syllabus
Introduction
- Getting started with MLOps
- Scope and prerequisites
- Machine learning life cycle
- Unique challenges with ML
- What is DevOps?
- What is MLOps?
- Principles of MLOps
- When to start MLOps?
- Selecting ML projects
- Creating requirements
- Designing the ML workflow
- Assembling the team
- Choosing tools and technologies
- Managed data pipelines
- Automated data validation
- Managed feature stores
- Data versioning
- Data governance
- Tools and technologies for data processing
- Managed training pipelines
- Creating data labels
- Experiment tracking
- AutoML
- Tools and technologies for training
- Model versioning
- Model registry
- Benchmarking models
- Model life cycle management
- Tools and technologies for model management
- Solution integration pipelines
- Notebook to software
- Solution integration patterns
- Best practices for solution integration
- Continuing on with MLOps
Taught by
Kumaran Ponnambalam
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