Deployment of Machine Learning Models

سرفصل های دوره

Learn how to integrate robust and reliable Machine Learning Pipelines in Production

01 - Introduction

001 Introduction to the course

002 Course curriculum overview

003 Course requirements

004 Setting up your computer.html

005 Course Material

006 The code.html

007 Presentations.html

008 Download Dataset.html

009 Additional Resources for the required skills.html

010 How to approach the course

02 - Overview of Model Deployment

001 Deployments of Machine Learning Models

002 Deployment of Machine Learning Pipelines

003 Research and Production Environment

004 Building Reproducible Machine Learning Pipelines

005 Challenges to Reproducibility

006 Streamlining Model Deployment with Open-Source

007 Additional Reading Resources.html

03 - Machine Learning System Architecture

001 Machine Learning System Architecture and Why it Matters

002 03.2-Notes.pdf

002 Specific Challenges of Machine Learning Systems

003 03.3-Notes.pdf

003 Principles for Machine Learning Systems

004 03.4-Notes.pdf

004 Machine Learning System Architecture Approaches

005 Machine Learning System Component Breakdown

006 Additional Reading Resources.html

04 - Research Environment - Developing a Machine Learning Model

001 Research Environment - Process Overview

002 Machine Learning Pipeline Overview

003 Feature Engineering - Variable Characteristics

004 Feature Engineering Techniques

005 Feature Selection

006 Training a Machine Learning Model

007 Research environment - second part.html

008 Code covered in this section.html

009 Python library versions.html

010 Data analysis demo - missing data

011 Data analysis demo - temporal variables

012 Data analysis demo - numerical variables

013 Data analysis demo - categorical variables

014 Feature engineering demo 1

015 Feature engineering demo 2

016 Feature selection demo

017 Model training demo

018 Scoring new data with our model

019 Research environment - third part.html

020 Python Open Source for Machine Learning

021 Open Source Libraries for Feature Engineering

022 Feature engineering with open source demo

023 Research environment - fourth part.html

024 Intro to Object Oriented Programing

025 Inheritance and the Scikit-learn API

026 Create Scikit-Learn compatible transformers

027 Create transformers that learn parameters

028 Feature engineering pipeline demo

029 Should feature selection be part of the pipeline

030 Research environment - final section.html

031 Getting Ready for Deployment - Final Pipeline

032 Bonus Additional Resources on Scikit-Learn.html

external-links.txt

05 - Packaging The Model for Production

001 Introduction to Production Code

002 Repo for this section.html

003 Code Overview

003 dmlm-notebook-to-code-diagram.zip

004 Understanding the Reasoning Behind the Prod Code Structure

005 Reminder Download the Kaggle Data.html

006 Package Requirements Files

007 Working with tox [Do NOT skip - important]

008 Migrating from Tox 3 to Tox 4.html

009 Troubleshooting Tox.html

010 Package Config

011 The Model Training Script & Pipeline

012 Introduction to Pytest [Optional]

013 Feature Engineering Code in the Package

014 Making Predictions with the Package

015 Building the Package

016 Tooling

017 Section Notes & Further Reading.html

06 - Serving and Deploying the model via REST API

001 Running the API Locally

002 Understanding the Architecture of the API

003 Introduction to FastAPI

004 The API Endpoints

005 Using Schemas in our API

006 Logging in our Application

007 The Uvicorn Web Server

008 Introducing Railway App and Platform as a Service

009 What Is a Platform as a Service (PaaS)

010 Why Use Railway as Our PaaS

011 Railway Links.html

012 Deploying our ML Application to Railway - Hands On

013 Limitations to Be Aware Of & Wrap Up

014 Section Notes & Further Reading.html

07 - Continuous Integration and Deployment Pipelines

001 Introduction to CICD

002 Setting up CircleCI

003 CICD Automation Overview Part 1

004 CICD Config Explanation

005 CICD Automation Overview Part 2

006 Using a Private Index Server (Gemfury)

007 Hands on Run the CI Tests in your own Github Fork

008 Hands on Run the CI Deploy on Your Own Github Fork

009 Hands on Run the CI Publish on Your Own Github Fork

010 Section Notes & Further Reading.html

08 - Deploying The ML API With Containers

001 Docker Refresher [Optional - For those unfamiliarrusty with Docker]

001 docker-notes.pdf

002 The Value of Docker and Containers

003 Understanding The Container Deployment Process

004 Docker Install Setup.html

005 Hands On Containerising the App Locally

006 Updating the CI Pipeline for a Container Deployment

007 Section Notes & Further Reading.html

09 - Differential Testing

001 Attention !!! - This section still works with old version of code.html

002 How to Use the Course Resources

002 Section5.3a-Notes.pdf

003 9.1 - Introduction

004 9.2 - Setting up Differential Tests

004 Section9.2-Notes.pdf

005 9.3 - Differential Tests in CI (Part 1 of 2)

006 9.4 - Differential Tests in CI (Part 2 of 2)

006 Section9.4-Notes.pdf

007 9.5 Wrap Up

007 Section9.5-Notes.pdf

10 - Deploying to IaaS (AWS ECS)

001 Attention!!! We are currently updating this section.html

002 12.1 - Introduction to AWS

003 12.2 - AWS Costs and Caution

003 Section12.2-Notes.pdf

004 12.3a - Intro to AWS ECS

004 Section12.3-Notes.pdf

005 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm

005 Section12.3-Notes.pdf

006 12.4 - Create an AWS Account

006 Section12.4-Notes.pdf

007 12.5 - Setting Permissions with IAM

007 Section12.5-Notes.pdf

008 12.6 - Installing the AWS CLI

008 Section12.6-Notes.pdf

009 12.7 - Configuring the AWS CLI

009 Section12.7-Notes.pdf

010 12.8 - Intro the Elastic Container Registry (ECR)

010 Section12.8-Notes.pdf

011 12.9 - Uploading Images to the Elastic Container Registry (ECR)

011 Section12.9-Notes.pdf

012 12.10 - Creating the ECS Cluster with Fargate Launch Method

012 Section12.10-Notes.pdf

013 12.11 - Updating the Cluster Containers

013 Section12.12-Notes.pdf

014 12.12 - Tearing down the ECS Cluster

014 Section12.13-Notes.pdf

015 12.13 - Deploying to ECS via the CI pipeline

015 Section12.14-Notes.pdf

016 12.14 - Wrap Up

016 Section12.15-Notes.pdf

11 - A Deep Learning Model with Big Data

001 Challenges of using Big Data in Machine Learning

002 Installing Keras.html

003 Download the data set.html

004 Introduction to a Large Dataset - Plant Seedlings Images

005 Building a CNN in the Research Environment

005 CNN-Analysis-and-Model.zip

006 CNNProdCode.zip

006 Production Code for a CNN Learning Pipeline

007 Reproducibility in Neural Networks

008 Setting the Seed for Keras.html

009 Seed for Neural Networks - Additional reading resources.html

010 13.8 - Packaging the CNN

010 Section13.8-Notes.pdf

011 13.9 - Adding the CNN to the API

011 Section13.9-Notes.pdf

012 13.10 - Additional Considerations and Wrap Up

012 Section13.10-Notes.pdf

12 - Common Issues found during deployment

001 Troubleshooting.html

001 Troubleshooting.pdf

13 - Appendix Former Section Serving the model via REST API

001 Appendix - PLEASE READ.html

002 7.1 - Introduction

002 Section7.1-Notes.pdf

003 Primer on Monorepos

003 Section5.3c-Notes.pdf

004 7.2 - Creating the API Skeleton

004 Section7.2-Notes.pdf

005 7.2b - Note On Flask.html

006 7.3 - Adding Config and Logging

006 Section7.3-Notes.pdf

007 7.4 - Adding the Prediction Endpoint

007 Section7.4-Notes.pdf

008 7.5 - Adding a Version Endpoint

008 Section7.5-Notes.pdf

009 7.6 - API Schema Validation

009 Section7.6-Notes.pdf

010 7.7 - Wrap Up

14 - Final Section

001 Congratulations.html

002 Bonus lecture.html

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تولید کننده: Udemy