Machine Learning Essentials Outline

Lesson 1: Model Deployment and Inferencing

  • What is Model Deployment and how to put it to use?

  • Key Concepts

  • Deployment Infrastructure

  • Optimization

Lesson 2: MLOps

  • What is MLOps

  • Key Concepts

  • Need for MLOps

  • Model Drift

Lesson 3: Hybrid and Multi-Cloud Machine Learning

  • Hybrid and Multi-Cloud Deployments

  • Admin and Data Scientists team

  • How you can enable your Machine Learning Solutions to leverage your On-Premises and third party Cloud Infrastructure

Lesson 4: Openness and Interoperability of Machine Learning Environments 

  • Choice of Tools and Languages

  • Interoperability with Frameworks

  • OSS Contribution

  • Ecosystem Support

Lesson 5: Training ML Models at Scale

  • Managed vs Bring your Own

  • The right Compute Configuration

  • Managed Compute Options

  • Data and Model Parallelism

Lesson 6: Securing your Machine Learning Environments 

  • Authentication & Authorization

  • Network Security

  • Data Encryption

  • Policy & Monitoring