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