MLOps Exhaustive Guide AWS GCP Apple Cases
What you’ll learn
Productionize ML Solutions to Real World Business Problems
Roll out ML Models to Multiple Environments
Learn to Work in AWS Sagemaker
Learn to Work in GCP Vertex AI
Train & Deploy ML Models for Apple Devices
Create a Solid Case to GET PROMOTED in Your Career
Requirements
Basic Python experience
Basic Docker experience
Basic Terminal experience
Description
Would you like to learn best practices of Automation & Deployment for ML models?Maybe you would also like to practice doing it?You’ve come to the right place!There’s no better way to achieve that than by creating a strong theoretical foundation and getting hands dirty by applying newly learnt concepts in practice straight away!MLOps has been helping me automate & roll out robust, easily maintainable and state-of-the-art ML in IT, Food and Travel industries over the last 6 years.With the help of modern Cloud Computing and open source software I’ve brought live dozens of ML research projects, successfully solved very complex Business challenges and even changed the country where I live & work!Join me in this fun and Industry-shaped course to get new skills and improve your MLOps & Cloud & Business acumen!By the end of this course you will be able to:Set up CI & CD pipelinesPackage ML models into DockerRun AutoML locally & in the CloudTrain ML models for Apple devicesMonitor and Log ML experiments with MLFlow frameworkSet up and manage MLOps pipelines in AWS SageMakerOperate Model Registry & Endpoints in GCP VertexAIBoost your Career and MLOps studying efficiencyP.S. If you’d like to learn how to deploy your solutions in form of Interactive Analytical Apps, check out my course on Streamlit.Happy learning!
Overview
Section 1: Introduction
Lecture 1 Welcome!
Lecture 2 Course Contents
Lecture 3 About the Instructor
Lecture 4 Motivation to study MLOps
Section 2: Theory Stack (+practice)
Lecture 5 DevOps
Lecture 6 Continuous Integration (CI): Theory
Lecture 7 Continuous Integration (CI): Practice
Lecture 8 Continuous Delivery (CD)
Lecture 9 Infrastructure as Code (IaC)
Lecture 10 Microservices
Lecture 11 Management principles
Lecture 12 MLOps & Deployment Strategies
Section 3: Best Practices of MLOps
Lecture 13 Packaged MLOps: Part 1
Lecture 14 Packaged MLOps: Part 2
Lecture 15 AutoML: Local
Lecture 16 AutoML: Apple
Lecture 17 AutoML: AWS
Lecture 18 Controlled Deployments
Lecture 19 Monitoring ML
Lecture 20 Logging ML
Lecture 21 Data Versioning with DVC
Section 4: MLOps in AWS (practice)
Lecture 22 Intro
Lecture 23 MLOps in AWS: Part 1 (setting up env)
Lecture 24 MLOps in AWS: Part 2 (increasing quotas, running pipeline)
Lecture 25 MLOps in AWS: Part 3 (getting deeper into SageMaker)
Lecture 26 MLOps in AWS: Part 4 (cleaning up #1)
Lecture 27 MLOps in AWS: Part 5 (cleaning up #2)
Section 5: MLOps in GCP (practice)
Lecture 28 MLOps in GCP: Part 1 (setting up GCP&VertexAI)
Lecture 29 MLOps in GCP: Part 2 (models registry, endpoints)
Section 6: Industry Hacks – Boost Your Career!
Lecture 30 Hack #1
Lecture 31 Hack #2
Lecture 32 Hack #3
Lecture 33 Farewell
Machine Learning Enthusiasts, Data Scientists, Data Analysts, Developers, AI professionals
Course Information:
Udemy | English | 3h 36m | 3.05 GB
Created by: Maxim Migutin
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