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?
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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.
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MLOps toolbox.We will learn how to apply MLOps tools to implement an end-to-end project.
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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.
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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.
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Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently.
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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.
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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.
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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.
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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
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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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