Complete MLOps Bootcamp - From Zero to Hero in Python 2022

Complete MLOps Bootcamp | From Zero to Hero in Python 2022, available at $64.99, has an average rating of 3.6, with 121 lectures, based on 707 reviews, and has 5170 subscribers.

You will learn about 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 This course is ideal for individuals who are Machine Learning engineers and Data Scientists interested in MLOps or Machine Learning professionals who want to deploy models to production or Anyone interested in developing APIs in FastAPI or Flask or Anyone who wants to learn Docker, Azure, DVC o MLFlow It is particularly useful for Machine Learning engineers and Data Scientists interested in MLOps or Machine Learning professionals who want to deploy models to production or Anyone interested in developing APIs in FastAPI or Flask or Anyone who wants to learn Docker, Azure, DVC o MLFlow.

Enroll now: Complete MLOps Bootcamp | From Zero to Hero in Python 2022

Summary

Title: Complete MLOps Bootcamp | From Zero to Hero in Python 2022

Price: $64.99

Average Rating: 3.6

Number of Lectures: 121

Number of Published Lectures: 121

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 121

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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

Target Audiences

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

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:

? MLOps Training Guide (PDF e-book)

? Downloadable files, codes, and resources

? Laboratories applied to use cases

? Practical exercises and quizzes

? Resources such as Cheatsheets

? 1 to 1 expert support

? Course question and answer forum

? 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.

Course Curriculum

Chapter 1: Introduction to this course

Lecture 1: How to get the most out of the course

Lecture 2: Course material

Chapter 2: Challenges and evolution of Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Benefits of Machine Learning

Lecture 3: MLOps Fundamentals

Lecture 4: DevOps and DataOps Fundamentals

Chapter 3: MLOps Fundaments

Lecture 1: Problems that MLOps solves

Lecture 2: MLOps Components

Lecture 3: MLOps Toolbox

Lecture 4: MLOps stages

Chapter 4: Installation of tools and libraries

Lecture 1: How to install libraries and prepare the environment

Lecture 2: Jupyter Notebook Basics

Lecture 3: Installing Docker and Ubuntu

Chapter 5: Productivization and structuring of ML projects

Lecture 1: Cookiecutter for managing the structure of the Machine Learning model

Lecture 2: Libraries and tools for project management from start to finish

Lecture 3: Poetry for dependency management

Lecture 4: Makefile for automated task execution

Lecture 5: Hydra to manage YAML configuration files

Lecture 6: Hydra applied to a Machine Learning project

Lecture 7: Automatically check and fix code before commit in Git

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

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

Lecture 10: Automatically generate documentation for ML project

Chapter 6: MLOps Phase 1: Solution Design

Lecture 1: Volere design and implementation

Chapter 7: MLOps Phase 2: Automating the ML Model Cycle

Lecture 1: AutoML Basics

Lecture 2: Building a model from start to finish with Pycaret

Lecture 3: EDA and Advanced Preprocessing with Pycaret

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

Lecture 5: Production deployment with Pycaret

Chapter 8: MLOps phase 2: Model versioning and registration with MLFlow

Lecture 1: Model registry and versioning with MLFlow

Lecture 2: Registering a Scikit-Learn model with MLFlow

Lecture 3: Registering a Pycaret model with MLFlow

Chapter 9: Versioning dataset with DVC

Lecture 1: Introduction to DVC

Lecture 2: DVC commands and process

Lecture 3: Hands-on lab with DVC

Lecture 4: DVC Pipelines

Chapter 10: Code repository with DagsHub, DVC, Git and MLFlow

Lecture 1: Introduction to DagsHub for the code repository

Lecture 2: EDA and data preprocessing

Lecture 3: Training and evaluation of the prototype of the ML model

Lecture 4: DagsHub account creation

Lecture 5: Creating the Python environment and dataset

Lecture 6: Deployment of the model in DagsHub

Lecture 7: Training and versioning the ML model

Lecture 8: Improving the model for a production environment

Lecture 9: Using DVC to version data and models

Lecture 10: Sending code, data and models to DagsHub

Lecture 11: Experimentation and registration of experiments in DagsHub

Lecture 12: Using DagsHub to analyze and compare experiments and models

Chapter 11: Automated registration and versioning with Pycaret and DagsHub

Lecture 1: Pycaret and Dagshub integration

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

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

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

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

Lecture 6: Solution. Generating a repository with DagsHub

Lecture 7: Hands-on exercise. Data versioning with DVC

Lecture 8: Solution. Data versioning with DVC

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

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

Chapter 12: Model interpretability

Lecture 1: Basics of interpretability with SHAP

Lecture 2: Interpreting Scikit Learn models with SHAP

Lecture 3: Interpreting models with SHAP in Pycaret

Chapter 13: Putting models into production

Lecture 1: Deploying Models in Production

Chapter 14: MLOps phase 3: Model serving through APIs

Lecture 1: Fundamentals of APIs and FastAPI

Lecture 2: Functions, methods and parameters in FastAPI

Lecture 3: POST Method, Swagger and Pydantic in FastAPI

Lecture 4: API development for Scikit-learn model with FastAPI

Lecture 5: Automated API development with Pycaret

Chapter 15: MLOps Phase 3: Model serving with Web Applications

Lecture 1: Serve the model through a Web Application

Lecture 2: Basic Gradio commands

Lecture 3: Development of a Gradio web application for Machine Learning

Lecture 4: Automated web application development with Pycaret

Lecture 5: Web application development with Streamlit

Lecture 6: Laboratory_ Web application development with Streamlit and Altair

Lecture 7: Laboratory_ Streamlit and Pycaret to develop a ML web service

Chapter 16: Flask for application development

Lecture 1: Flask Fundamentals

Lecture 2: Building a project from start to finish with Flask

Lecture 3: Back-end development with Flask and front-end development with HTML and CSS

Chapter 17: Docker and containers in Machine Learning

Lecture 1: Containers to isolate our applications

Lecture 2: Docker and Kubernetes Basics

Lecture 3: Generating a container for an ML API with Docker

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

Chapter 18: BentoML for automated development of ML services

Lecture 1: Introduction to BentoML for generating ML services

Instructors

Data Bootcamp
data scientist

Rating Distribution

1 stars: 35 votes

2 stars: 36 votes

3 stars: 116 votes

4 stars: 235 votes

5 stars: 285 votes

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