Mastering MLOps: Complete course for ML Operations

سرفصل های دوره

Advanced hands-on bootcamp of MLOps with MLFlow, Scikit-learn, CI/CD, Azure, FastAPI, Gradio, SHAP, Docker, DVC, Flask..

1. Introduction to this course

1. Introduction to this course

2. How to get the most out of the course

3.1 Course material.7z

3.2 MLOps Course Slides.pdf

3. Course material.html

2. Challenges and evolution of Machine Learning

1. Introduction to Machine Learning

2. Benefits of Machine Learning

3. MLOps Fundamentals

4. DevOps and DataOps Fundamentals

3. MLOps Fundaments

1. Problems that MLOps solves

2. MLOps Components

3. MLOps Toolbox

4. MLOps stages

4. Installation of tools and libraries

1. How to install libraries and prepare the environment

2. Jupyter Notebook Basics

3. Installing Docker and Ubuntu

5. Productivization and structuring of ML projects

1. Cookiecutter for managing the structure of the Machine Learning model

2. Libraries and tools for project management from start to finish

3. Poetry for dependency management

4. Makefile for automated task execution

5. Hydra to manage YAML configuration files

6. Hydra applied to a Machine Learning project

7. Automatically check and fix code before commit in Git

8. Code review with Black and Flake8 in the pre-commit

9. Code review with Isort and Iterrogate in the Pre-commit and Git integration

10. Automatically generate documentation for ML project

6. MLOps Phase 1 Solution Design

1. Volere design and implementation

7. MLOps Phase 2 Automating the ML Model Cycle

1. AutoML Basics

2. Building a model from start to finish with Pycaret

3. EDA and Advanced Preprocessing with Pycaret

4. Development of advanced models (XGBoost, CatBoost, LightGBM) with Pycaret)

5. Production deployment with Pycaret

8. MLOps phase 2 Model versioning and registration with MLFlow

1. Model registry and versioning with MLFlow

2. Registering a Scikit-Learn model with MLFlow

3. Registering a Pycaret model with MLFlow

9. Versioning dataset with DVC

1. Introduction to DVC

2. DVC commands and process

3. Hands-on lab with DVC

4. DVC Pipelines

10. Code repository with DagsHub, DVC, Git and MLFlow

1. Introduction to DagsHub for the code repository

2. EDA and data preprocessing

3. Training and evaluation of the prototype of the ML model

4. DagsHub account creation

5. Creating the Python environment and dataset

6. Deployment of the model in DagsHub

7. Training and versioning the ML model

8. Improving the model for a production environment

9. Using DVC to version data and models

10. Sending code, data and models to DagsHub

11. Experimentation and registration of experiments in DagsHub

12. Using DagsHub to analyze and compare experiments and models

11. Automated registration and versioning with Pycaret and DagsHub

1. Pycaret and Dagshub integration

2. Hands on laboratory of registering a model and dataset with Pycaret and DagsHub

3. Hands-on Exercise.Development of a model with Pycaret and registration in MLFlow

4. Solution. Development of a model with Pycaret and registration in MLFlow

5. Hands-on exercise. Generating a repository with DagsHub

6. Solution. Generating a repository with DagsHub

7. Hands-on exercise. Data versioning with DVC

8. Solution. Data versioning with DVC

9. Hands-on exercise. Registering the model on a shared MLFlow server

10. Solution. Registering the model on a shared MLFlow server

12. Model interpretability

1. Basics of interpretability with SHAP

2. Interpreting Scikit Learn models with SHAP

3. Interpreting models with SHAP in Pycaret

13. Putting models into production

1. Deploying Models in Production

14. MLOps phase 3 Model serving through APIs

1. Fundamentals of APIs and FastAPI

2. Functions, methods and parameters in FastAPI

3. POST Method, Swagger and Pydantic in FastAPI

4. API development for Scikit-learn model with FastAPI

5. Automated API development with Pycaret

15. MLOps Phase 3 Model serving with Web Applications

1. Serve the model through a Web Application

2. Basic Gradio commands

3. Development of a Gradio web application for Machine Learning

4. Automated web application development with Pycaret

5. Web application development with Streamlit

6. Laboratory Web application development with Streamlit and Altair

16. Flask for application development

1. Flask Fundamentals

2. Building a project from start to finish with Flask

3. Back-end development with Flask and front-end development with HTML and CSS

17. Docker and containers in Machine Learning

1. Containers to isolate our applications

2. Docker and Kubernetes Basics

3. Generating a container for an ML API with Docker

4. Docker to generate a container of a web application from Flask, HTML

18. BentoML for automated development of ML services

1. Introduction to BentoML for generating ML services

2. Generating an ML service with BentoML

3. Putting the service into production with BentoML and Docker

4. BentoML and MLflow integration and custom models

5. GPU, preprocessing, data validation and multiple models in BentoML

6. Different tools for developing ML services

7. Exercise Using BentoML to develop a ML service

8. Exercise Solution Using BentoML to develop a ML service

19. Deploy to Azure Cloud with Azure Container and Azure SDKs

1. Introduction to Machine Learning in Cloud

2. Putting the ML application into production in Azure Container with Docker

3. SDKs and Azure Blob Storage for model deployment to Azure

4. Model training and production deployment in Azure Blob Storage

5. Download the Azure Blob Storage model and get predictions

20. Deployment of ML services on Heroku

1. Heroku Fundamentals

21. Continuous integration and delivery (CICD) with GitHub Actions and CML

1. Introduction to GitHub Actions

2. GitHub Actions basic workflow

3. GitHub Actions hands-on lab

4. CI with Continuous Machine Learning (CML)

5. CML Use Cases

6. Hands-On Lab Applying GitHub Actions and CML to MLOps

7. Hands-On Lab Tracking Performance with GitHub Actions and CML

22. Model and service monitoring with Evidently AI

1. Introduction to monitoring ML models and services

2. Data Drift, Concept Drift, and Model Performance

3. ML model and service monitoring tools

4. Evidently AI Fundamentals

5. Drift and data quality, target drift and model quality

6. Hands-on Lab Monitoring a model with Evidently AI

7. Hands-on Laboratory Monitoring the model in production

8. Hands-on Laboratory Identification of data drift in production

23. End-to-end MLOps Project

1. MLOps end-to-end projectMLOps end-to-end project

2. Development of the ML model

3. Validation of the quality of the code, model and preprocessing

4. Project versioning with MLFlow and DVC

5. Shared repository with DagsHub and MLFlow

6. API development with BentoML

7. App development with Streamlit

8. CI-CD Data validation workflow with GitHub Actions

9. CICD Validating app functionality with GitHub Actions

10. CICD Automated app deployment with GitHub Actions and Heroku

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