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MLOps Key Concepts - An Ongoing Series
video

MLOps Key Concepts - An Ongoing Series

by Alfredo Deza, Noah Gift
December 2022
Intermediate
4h 48m
English
Pragmatic AI Labs

Overview

MLOps Key Concepts - An Ongoing Series

Select MLOps Key Concepts

This video series live coding languages iteratively thus learning the language. Lessons Covered Include:

  • 1.0 MLOps Hierarchy of Needs: DevOps, DataOps, Platform Automation and MLOps
  • 2.0 mlops trends
  • 3.0 Heavy vs Light MLOps
  • 4.0 MLOps Maturity Model
  • 5.0 What is Continuous Delivery?
  • 6.0 MLOps Hierarchy of Needs: DevOps, DataOps, Platform Automation and MLOps
  • 7.0 Key Components MLOps Landscape
  • 8.0 Build Cutting Edge MLOps Tools with Pre-trained LLM (Large Language Model)) for Hugging Face and OpenAI and Github Copilot
  • 9.0 feature store data warehouse
  • 10.0 data drift taleb
  • 11.0 ai enabled workflows
  • 12.0 fine tuning raw ingredients hugging face
  • 13.0 advantages transfer learning
  • 14.0 containerized ml microservices
  • 15.0 ai builds ai to write ai
  • 16.0 bespoke system core business
  • 17.0 simulations vs experiment tracking
  • 19.0 Build Pytorch Fastapi microservices deployed via AWS App Runner using AWS Cloud9 and GitHub Codespaces
  • 20.0 SRE Mindset for MLOps
  • 21.0 Teaching MLOps at scale with Github Codespaces and Copilot
  • 22.0 Using default Python.org download to install datascience packages with virtualenv and pip
Learning Objectives
  • Learn key terminology and concepts in machine learning, data science, data operations and DevOps
  • Understand foundational concepts in MLOps
  • Using Hugging Face and OpenAI to build a cutting edge MLOps pipelines
  • Use Github Codespaces and Copilot with Open AI Codex use AI to write AI
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Publisher Resources

ISBN: 08192022VIDEOPAIMLOtherOtherOther