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..

1. Challenges and evolution of Machine Learning

1. Course material.html

2. Introduction to Machine Learning

3. Benefits of Machine Learning

4. MLOps Fundamentals

5. DevOps and DataOps Fundamentals

Exercise Files.zip

2. Fundamentos de MLOps

1. Problems that MLOps solves

2. MLOps Components

3. MLOps Toolbox

3. Etapas del MLOps

1. MLOps stages

4. Instalacion de herramientas y librerias

1. Jupyter Notebook Basics

2. Installing Docker and Ubuntu

5. MLOps Phase 1 Solution Design

1. Volere design and implementation

6. 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

7. MLOps phase 2 Registration and versioning of the model

1. Registration and versioning of models with MLFlow

2. Registering a Scikit-Learn model with MLFlow

3. Registering the Pycaret model with MLFlow

8. Model interpretability

1. Basics of interpretability with SHAP

2. Interpreting Scikit Learn models with SHAP

3. Interpreting models with SHAP in Pycaret

9. Putting models into production

1. Commissioning of Models

10. 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

11. Flask for application development

1. Flask Basics

2. Building a project from start to finish with Flask

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

12. Docker and containers for 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

13. Deploy Flask app to Cloud with Azure Container

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

14. Despliegue de modelos en Azure

1. Model training and production deployment in Azure Blob Storage

2. Download the Azure Blob Storage model and get predictions

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تولید کننده: Udemy-Training