وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

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
  • 139,000 تومان
    بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
    خرید دانلودی فوری

    در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

    ایمیل شما:
    تولید کننده:
    مدرس:
    شناسه: 40898
    حجم: 4008 مگابایت
    مدت زمان: 623 دقیقه
    تاریخ انتشار: 2 آبان 1403
    طراحی سایت و خدمات سئو

    139,000 تومان
    افزودن به سبد خرید