Complete MLOps Bootcamp | From Zero to Hero in Python 2022

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Data Bootcamp

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01 - Introduction to this course

001 How to get the most out of the course.mp4

15:27

002 Course material.html

002 Course-material.7z

002 MLOPs-Guide.pdf

02 - Challenges and evolution of Machine Learning

001 Introduction to Machine Learning.mp4

03:19

002 Benefits of Machine Learning.mp4

00:57

003 MLOps Fundamentals.mp4

02:41

004 DevOps and DataOps Fundamentals.mp4

03:25

03 - MLOps Fundaments

001 Problems that MLOps solves.mp4

01:31

002 MLOps Components.mp4

05:04

003 MLOps Toolbox.mp4

03:47

004 MLOps stages.mp4

05:50

04 - Installation of tools and libraries

001 How to install libraries and prepare the environment.mp4

04:00

002 Jupyter Notebook Basics.mp4

04:37

003 Installing Docker and Ubuntu.mp4

04:09

05 - Productivization and structuring of ML projects

001 Cookiecutter for managing the structure of the Machine Learning model.mp4

05:26

002 Libraries and tools for project management from start to finish.mp4

01:29

003 Poetry for dependency management.mp4

03:48

004 Makefile for automated task execution.mp4

01:46

005 Hydra to manage YAML configuration files.mp4

06:07

006 Hydra applied to a Machine Learning project.mp4

06:40

007 Automatically check and fix code before commit in Git.mp4

02:38

008 Code review with Black and Flake8 in the pre-commit.mp4

03:55

009 Code review with Isort and Iterrogate in the Pre-commit and Git integration.mp4

05:44

010 Automatically generate documentation for ML project.mp4

04:15

06 - MLOps Phase 1 Solution Design

001 Volere design and implementation.mp4

02:48

07 - MLOps Phase 2 Automating the ML Model Cycle

001 AutoML Basics.mp4

03:52

002 Building a model from start to finish with Pycaret.mp4

05:41

003 EDA and Advanced Preprocessing with Pycaret.mp4

05:49

004 Development of advanced models (XGBoost, CatBoost, LightGBM) with Pycaret).mp4

03:25

005 Production deployment with Pycaret.mp4

04:58

08 - MLOps phase 2 Model versioning and registration with MLFlow

001 Model registry and versioning with MLFlow.mp4

03:45

002 Registering a Scikit-Learn model with MLFlow.mp4

05:02

003 Registering a Pycaret model with MLFlow.mp4

04:45

09 - Versioning dataset with DVC

001 Introduction to DVC.mp4

07:19

002 DVC commands and process.mp4

05:52

003 Hands-on lab with DVC.mp4

07:13

004 DVC Pipelines.mp4

06:48

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

001 Introduction to DagsHub for the code repository.mp4

03:19

002 EDA and data preprocessing.mp4

08:32

003 Training and evaluation of the prototype of the ML model.mp4

09:16

004 DagsHub account creation.mp4

04:59

005 Creating the Python environment and dataset.mp4

04:38

006 Deployment of the model in DagsHub.mp4

04:00

007 Training and versioning the ML model.mp4

06:13

008 Improving the model for a production environment.mp4

07:07

009 Using DVC to version data and models.mp4

04:01

010 Sending code, data and models to DagsHub.mp4

08:35

011 Experimentation and registration of experiments in DagsHub.mp4

07:25

012 Using DagsHub to analyze and compare experiments and models.mp4

05:06

11 - Automated registration and versioning with Pycaret and DagsHub

001 Pycaret and Dagshub integration.mp4

02:45

002 Hands on laboratory of registering a model and dataset with Pycaret and DagsHub.mp4

09:09

003 Hands-on Exercise.Development of a model with Pycaret and registration in MLFlow.mp4

01:13

004 Solution. Development of a model with Pycaret and registration in MLFlow.mp4

07:51

005 Hands-on exercise. Generating a repository with DagsHub.mp4

01:34

006 Solution. Generating a repository with DagsHub.mp4

02:11

007 Hands-on exercise. Data versioning with DVC.mp4

01:46

008 Solution. Data versioning with DVC.mp4

07:07

009 Hands-on exercise. Registering the model on a shared MLFlow server.mp4

01:04

010 Solution. Registering the model on a shared MLFlow server.mp4

06:52

12 - Model interpretability

001 Basics of interpretability with SHAP.mp4

03:11

002 Interpreting Scikit Learn models with SHAP.mp4

03:47

003 Interpreting models with SHAP in Pycaret.mp4

03:39

13 - Putting models into production

001 Deploying Models in Production.mp4

03:13

14 - MLOps phase 3 Model serving through APIs

001 Fundamentals of APIs and FastAPI.mp4

04:07

002 Functions, methods and parameters in FastAPI.mp4

05:49

003 POST Method, Swagger and Pydantic in FastAPI.mp4

03:51

004 API development for Scikit-learn model with FastAPI.mp4

04:55

005 Automated API development with Pycaret.mp4

04:16

15 - MLOps Phase 3 Model serving with Web Applications

001 Serve the model through a Web Application.mp4

01:57

002 Basic Gradio commands.mp4

04:12

003 Development of a Gradio web application for Machine Learning.mp4

04:33

004 Automated web application development with Pycaret.mp4

01:35

005 Web application development with Streamlit.mp4

08:19

006 Laboratory Web application development with Streamlit and Altair.mp4

06:29

007 Laboratory Streamlit and Pycaret to develop a ML web service.mp4

10:48

16 - Flask for application development

001 Flask Fundamentals.mp4

05:04

002 Building a project from start to finish with Flask.mp4

04:38

003 Back-end development with Flask and front-end development with HTML and CSS.mp4

05:43

17 - Docker and containers in Machine Learning

001 Containers to isolate our applications.mp4

03:01

002 Docker and Kubernetes Basics.mp4

04:13

003 Generating a container for an ML API with Docker.mp4

03:30

004 Docker to generate a container of a web application from Flask, HTML.mp4

05:20

18 - BentoML for automated development of ML services

001 Introduction to BentoML for generating ML services.mp4

09:15

002 Generating an ML service with BentoML.mp4

08:18

003 Putting the service into production with BentoML and Docker.mp4

05:44

004 BentoML and MLflow integration and custom models.mp4

03:40

005 GPU, preprocessing, data validation and multiple models in BentoML.mp4

09:08

006 Different tools for developing ML services.mp4

04:23

007 Exercise Using BentoML to develop a ML service.mp4

01:11

008 Exercise Solution Using BentoML to develop a ML service.mp4

05:37

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

001 Introduction to Machine Learning in Cloud.mp4

03:56

002 Putting the ML application into production in Azure Container with Docker.mp4

07:35

003 SDKs and Azure Blob Storage for model deployment to Azure.mp4

06:36

004 Model training and production deployment in Azure Blob Storage.mp4

07:36

005 Download the Azure Blob Storage model and get predictions.mp4

03:18

20 - Deployment of ML services on Heroku

001 Heroku Fundamentals.mp4

04:44

002 Hands-on Laboratory Deploying a ML Service on Heroku.mp4

06:52

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

001 Introduction to GitHub Actions.mp4

04:48

002 GitHub Actions basic workflow.mp4

03:56

003 GitHub Actions hands-on lab.mp4

07:15

004 CI with Continuous Machine Learning (CML).mp4

05:51

005 CML Use Cases.mp4

06:21

006 Hands-On Lab Applying GitHub Actions and CML to MLOps.mp4

06:52

007 Hands-On Lab Tracking Performance with GitHub Actions and CML.mp4

03:55

22 - Model and service monitoring with Evidently AI

001 Introduction to monitoring ML models and services.mp4

03:22

002 Data Drift, Concept Drift, and Model Performance.mp4

07:06

003 ML model and service monitoring tools.mp4

04:07

004 Evidently AI Fundamentals.mp4

05:43

005 Drift and data quality, target drift and model quality.mp4

11:45

006 Hands-on Lab Monitoring a model with Evidently AI.mp4

07:29

007 Hands-on Laboratory Monitoring the model in production.mp4

07:22

008 Hands-on Laboratory Identification of data drift in production.mp4

04:50

23 - End-to-end MLOps Project

001 MLOps end-to-end projectMLOps end-to-end project.mp4

02:09

002 Development of the ML model.mp4

11:52

003 Validation of the quality of the code, model and preprocessing.mp4

07:42

004 Project versioning with MLFlow and DVC.mp4

07:17

005 Shared repository with DagsHub and MLFlow.mp4

06:03

006 API development with BentoML.mp4

07:21

007 App development with Streamlit.mp4

06:08

008 CI-CD Data validation workflow with GitHub Actions.mp4

04:01

009 CICD Validating app functionality with GitHub Actions.mp4

02:48

010 CICD Automated app deployment with GitHub Actions and Heroku.mp4

03:22

Description

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

What You'll Learn?

MLOps fundamentals
MLOps toolbox
Model versioning with MLFlow
Data versioning with DVC
Auto-ML and Low-code MLOps
Model Explainability, Auditability, and Interpretable machine learning
Containerized Machine Learning WorkFlow With Docker
Deploying ML in Production through APIS
Deploying ML in Production through web applications
MLOps in Azure Cloud

Who is this for?

Machine Learning engineers and Data Scientists interested in MLOps

Machine Learning professionals who want to deploy models to production

Anyone interested in developing APIs in FastAPI or Flask

Anyone who wants to learn Docker, Azure, DVC o MLFlow

What You Need to Know?

Python fundamentals

More details

Description
If you're looking for a comprehensive, hands-on, and project-based guide to learning MLOps (Machine Learning Operations), you've come to the right place.
According to an Algorithmia survey, 85% of Machine Learning projects do not reach production. In addition, the MLOps have exponentially grown in the last years. MLOPS was estimated at $23.2 billion for 2019 and is projected to reach $126 billion by 2025. Therefore, MLOps knowledge will give you numerous professional opportunities.
This course is designed to teach everything related to MLOps, from model development, model registration, and model versioning; model performance monitoring, CI/CD, cloud deployment, model serving and APIs, and web applications development to punt into production the model.
We will guide you through the MLOps skills, sharing clear explanations and valuable professional advice.

With visual training, downloadable study guides, hands-on exercises, and real-world labs, this is the only course you'll need to learn how to implement an end-to-end MLOps project. By the end of this course, not only will you have developed an entire MLOps project from the ground up, but you will also gain the knowledge and confidence to apply these same concepts to your projects.

What does the course include?

MLOps fundamentals. We will learn about the Basic Concepts and Fundamentals of MLOps. We will look at traditional ML model management challenges and how MLOps addresses those problems to offer solutions.
MLOps toolbox. We will learn how to apply MLOps tools to implement an end-to-end project.
Model versioning with MLFlow. We will learn to version and register machine learning models with MLFlow. MLflow is an open source platform for managing the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
Auto-ML and Low-code MLOps. We will learn to automate the development of machine learning models with Auto-Ml and Low-code libraries such as Pycaret. Pycaret automates much of the MLOps cycle, including model versioning, training, evaluation, and deployment.
Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently.
Containerized Machine Learning WorkFlow With Docker. Docker is one of the most used tools to package the code and dependencies of our application and distribute it efficiently. We will learn how to use Docker to package our Machine Learning applications.
Deploying ML in Production through APIS. We will learn about deploying models to production through API development with FastAPI and Flask. We will also learn to deploy those APIs in the Azure Cloud using Azure containers.
Deploying ML in Production through web applications. We will learn to develop web applications with embedded machine learning models using Gradio. We will also learn how to develop an ML application with Flask and HTML, distribute it via a Docker container, and deploy it to production in Azure.
MLOps in Azure Cloud. Finally, we will learn about the development and deployment of models in the Cloud, specifically in Azure. We will learn how to train models on Azure, put them into production, and then consume those models.

Join today and get instant and lifetime access to:
â€¢ MLOps Training Guide (PDF e-book)
â€¢ Downloadable files, codes, and resources
â€¢ Laboratories applied to use cases
â€¢ Practical exercises and quizzes
â€¢ Resources such as Cheatsheets
â€¢ 1 to 1 expert support
â€¢ Course question and answer forum
â€¢ 30 days money back guarantee

If you are ready to improve your MLOps skills, increase your job opportunities and become a data science professional, we are waiting for you.
Who this course is for:
Machine Learning engineers and Data Scientists interested in MLOps
Machine Learning professionals who want to deploy models to production
Anyone interested in developing APIs in FastAPI or Flask
Anyone who wants to learn Docker, Azure, DVC o MLFlow

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