Complete MLOps Bootcamp | From Zero to Hero in Python 2022

سرفصل های دوره

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

01 - Introduction to this course

001 How to get the most out of the course

002 Course material.html

002 Course-material.7z

002 MLOPs-Guide.pdf

02 - Challenges and evolution of Machine Learning

001 Introduction to Machine Learning

002 Benefits of Machine Learning

003 MLOps Fundamentals

004 DevOps and DataOps Fundamentals

03 - MLOps Fundaments

001 Problems that MLOps solves

002 MLOps Components

003 MLOps Toolbox

004 MLOps stages

04 - Installation of tools and libraries

001 How to install libraries and prepare the environment

002 Jupyter Notebook Basics

003 Installing Docker and Ubuntu

05 - Productivization and structuring of ML projects

001 Cookiecutter for managing the structure of the Machine Learning model

002 Libraries and tools for project management from start to finish

003 Poetry for dependency management

004 Makefile for automated task execution

005 Hydra to manage YAML configuration files

006 Hydra applied to a Machine Learning project

007 Automatically check and fix code before commit in Git

008 Code review with Black and Flake8 in the pre-commit

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

010 Automatically generate documentation for ML project

06 - MLOps Phase 1 Solution Design

001 Volere design and implementation

07 - MLOps Phase 2 Automating the ML Model Cycle

001 AutoML Basics

002 Building a model from start to finish with Pycaret

003 EDA and Advanced Preprocessing with Pycaret

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

005 Production deployment with Pycaret

08 - MLOps phase 2 Model versioning and registration with MLFlow

001 Model registry and versioning with MLFlow

002 Registering a Scikit-Learn model with MLFlow

003 Registering a Pycaret model with MLFlow

09 - Versioning dataset with DVC

001 Introduction to DVC

002 DVC commands and process

003 Hands-on lab with DVC

004 DVC Pipelines

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

001 Introduction to DagsHub for the code repository

002 EDA and data preprocessing

003 Training and evaluation of the prototype of the ML model

004 DagsHub account creation

005 Creating the Python environment and dataset

006 Deployment of the model in DagsHub

007 Training and versioning the ML model

008 Improving the model for a production environment

009 Using DVC to version data and models

010 Sending code, data and models to DagsHub

011 Experimentation and registration of experiments in DagsHub

012 Using DagsHub to analyze and compare experiments and models

11 - Automated registration and versioning with Pycaret and DagsHub

001 Pycaret and Dagshub integration

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

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

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

005 Hands-on exercise. Generating a repository with DagsHub

006 Solution. Generating a repository with DagsHub

007 Hands-on exercise. Data versioning with DVC

008 Solution. Data versioning with DVC

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

010 Solution. Registering the model on a shared MLFlow server

12 - Model interpretability

001 Basics of interpretability with SHAP

002 Interpreting Scikit Learn models with SHAP

003 Interpreting models with SHAP in Pycaret

13 - Putting models into production

001 Deploying Models in Production

14 - MLOps phase 3 Model serving through APIs

001 Fundamentals of APIs and FastAPI

002 Functions, methods and parameters in FastAPI

003 POST Method, Swagger and Pydantic in FastAPI

004 API development for Scikit-learn model with FastAPI

005 Automated API development with Pycaret

15 - MLOps Phase 3 Model serving with Web Applications

001 Serve the model through a Web Application

002 Basic Gradio commands

003 Development of a Gradio web application for Machine Learning

004 Automated web application development with Pycaret

005 Web application development with Streamlit

006 Laboratory Web application development with Streamlit and Altair

007 Laboratory Streamlit and Pycaret to develop a ML web service

16 - Flask for application development

001 Flask Fundamentals

002 Building a project from start to finish with Flask

003 Back-end development with Flask and front-end development with HTML and CSS

17 - Docker and containers in Machine Learning

001 Containers to isolate our applications

002 Docker and Kubernetes Basics

003 Generating a container for an ML API with Docker

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

18 - BentoML for automated development of ML services

001 Introduction to BentoML for generating ML services

002 Generating an ML service with BentoML

003 Putting the service into production with BentoML and Docker

004 BentoML and MLflow integration and custom models

005 GPU, preprocessing, data validation and multiple models in BentoML

006 Different tools for developing ML services

007 Exercise Using BentoML to develop a ML service

008 Exercise Solution Using BentoML to develop a ML service

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

001 Introduction to Machine Learning in Cloud

002 Putting the ML application into production in Azure Container with Docker

003 SDKs and Azure Blob Storage for model deployment to Azure

004 Model training and production deployment in Azure Blob Storage

005 Download the Azure Blob Storage model and get predictions

20 - Deployment of ML services on Heroku

001 Heroku Fundamentals

002 Hands-on Laboratory Deploying a ML Service on Heroku

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

001 Introduction to GitHub Actions

002 GitHub Actions basic workflow

003 GitHub Actions hands-on lab

004 CI with Continuous Machine Learning (CML)

005 CML Use Cases

006 Hands-On Lab Applying GitHub Actions and CML to MLOps

007 Hands-On Lab Tracking Performance with GitHub Actions and CML

22 - Model and service monitoring with Evidently AI

001 Introduction to monitoring ML models and services

002 Data Drift, Concept Drift, and Model Performance

003 ML model and service monitoring tools

004 Evidently AI Fundamentals

005 Drift and data quality, target drift and model quality

006 Hands-on Lab Monitoring a model with Evidently AI

007 Hands-on Laboratory Monitoring the model in production

008 Hands-on Laboratory Identification of data drift in production

23 - End-to-end MLOps Project

001 MLOps end-to-end projectMLOps end-to-end project

002 Development of the ML model

003 Validation of the quality of the code, model and preprocessing

004 Project versioning with MLFlow and DVC

005 Shared repository with DagsHub and MLFlow

006 API development with BentoML

007 App development with Streamlit

008 CI-CD Data validation workflow with GitHub Actions

009 CICD Validating app functionality with GitHub Actions

010 CICD Automated app deployment with GitHub Actions and Heroku

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تولید کننده: Udemy