Machine Learning Operations (MLOps) with AWS

In this course, you will learn different aspects of MLOps including model development, model registry, versioning, model deployment, monitoring, and model serving. We will also introduce the concepts of CI/CD.

Intermediate 90 Days Weekends
  • Challenges and Evolution of Machine Learning
    • Introduction to Machine Learning
    • Benefits of Machine Learning
    • MLOps Fundamentals
    • DevOps and DataOps
  • MLOps Fundamentals
    • Problems that MLOps solves
    • MLOps Components
    • MLOps Toolbox
    • MLOps Stages
  • Installation of tools and Libraries
    • How to install libraries and prepare the environment
    • Installing Docker and Ubuntu
  • Automating ML Model Life Cycle
    • AutoML Basics
    • Building a model from start to finish with Pycare
    • EDA and Advanced Preprocessing with Pycare
    • Development of advanced models (XGBoost, CatBoost, LightGBM) with Pycaret)
    • Production deployment with Pycaret
  • Model versioning and registration with MLFlow
    • Model registry and versioning with MLFlow
    • Registering a Scikit-Learn model with MLFlow
    • Registering a Pycaret model with MLFlow
       
  • Versioning dataset with DVC
    • Introduction to DVC
    • DVC commands and proces
    • Hands-on lab with DVC
    • DVC Pipelines
  • Code repository with DagsHub, DVC, Git, and MLFlow
    • Introduction to DagsHub for the code repository
    • EDA and data preprocessing
    • Training and evaluation of the prototype of the ML model
    • DagsHub account creation
    • Creating the Python environment and dataset
    • Deployment of the model in DagsHub
    • Training and versioning the ML model
    • Improving the model for a production environment
    • Using DVC to version data and models
    • Sending code, data and models to DagsHub
    • Experimentation and registration of experiments in DagsHub
    • Using DagsHub to analyze and compare experiments and models
  • Automated versioning and registration with Pycaret and DagsHub
    • Pycaret and Dagshub integration
    • Hands on laboratory of registering a model and dataset with Pycaret and DagsHub
    • Hands-on Exercise: Development of a model with Pycaret and registration in MLFlow
    • Hands-on exercise: Generating a repository with DagsHub
    • Hands-on exercise: Data versioning with DVC
    • Hands-on exercise: Registering the model on a shared MLFlow server
  • Model Interpretability
    • Basics of interpretability with SHAP
    • Interpreting Scikit-learn models with SHAP
    • Interpreting models with SHAP in Pycaret
  • Model Serving through API
    • Fundamentals of APIs and FastAPI
    • Functions, Methods, and Parameters in FastAPI
    • POST Method and Pednatic in FastAPI
    • API development for Scikit-learn model WITH FastAPI
  • Introduction to FLASK
    • FLASK Fundamentals
    • Building a project from start to finish with FLASK
  • Dockers and Containers in Machine Learning
    • Containers to isolate our applications
    • Docker Basics
    • Generating a container for an ML API with Docker
    • Docker to generate a container of a web application from Flask
  • Introduction to AWS Sagemaker
    • Setting up AWS
    • AWS Cli and IAM Setup
    • Introduction to AWS S3
    • AWS IAM roles
    • Data Processing with AWS Sagemaker
    • Model Training with AWS Sagemaker
    • Model Deployment 
  • Continuous Integration and Delivery (CI/CD) with GitHub Actions and CML
    • Introduction to GitHub Actions
    • GitHub Actions basic workflow
    • GitHub Actions hands-on lab
    • CI with Continuous Machine Learning (CML)
    • CML Use Cases
    • Hands-On Lab: Applying GitHub Actions and CML to MLOps
    • Hands-On Lab: Tracking Performance with GitHub Actions and CML
  • Model Monitoring with Evidently AI
    • Introduction to monitoring ML models and services
    • Data Drift, Concept Drift, and Model Performance
    • ML model and service monitoring tools
    • Evidently AI Fundamentals
    • Drift and data quality, target drift and model quality

Our course covers all aspects of MLOps, ranging from model development, model registry, and versioning, to model performance monitoring, cloud deployment, CI/CD, model serving, and APIs, enabling you to deploy your model into production. We offer expert guidance and valuable insights into MLOps, providing clear explanations and professional advice to enhance your skills.

What you will learn

  • 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 AWS Sagemaker.
  • MLOps with AWS Sagemaker: Finally, we will learn about the development and deployment of models in the Cloud, specifically in AWS Sagemaker. We will learn how to train models on AWS, put them into production, and then consume those models.

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Transcript from the "Introduction" Lesson

Course Overview [00:00:00]

My name is John Deo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.

Introduction [00:00:16]

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

Why Take This Course? [00:00:37]

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

A Look at the Demo Application [00:00:54]

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

Summary [00:01:31]

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

Course - Frequently Asked Questions

How this course help me to design layout?

My name is Jason Woo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.

What is important of this course?

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

Why Take This Course?

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

Is able to create application after this course?

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.

$220
Installments
Enroll Now Starts May 1, 2023

What's included

  • Certificate
  • 15 Modules
  • Live Classes
  • Lifetime access
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