Machine Learning Operations (MLOps) with Azure

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 60 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
  • Automating ML Model Life Cycle
    • AutoML Basics
    • Building a model from start to finish with Pycaret
    • EDA and Advanced Preprocessing with Pycaret
    • Development of advanced models (XGBoost, CatBoost, LightGBM) with Pycaret)
    • Production deployment with Pycaret
  • Installation of tools and Libraries
    • How to install libraries and prepare the environment
    • Installing Docker and Ubuntu
  • 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
       
  • Introduction to Streamlit
    • Streamlit Fundamentals
    • Buidling Data Exploratory App with Streamlit
    • Building a project from start to finish with Streamlit
    • Deploying a machine learning model with Streamlit App
  • Introduction to Git and Dockers in Machine Learning
    • What is Git?
    • Advantages of Git
    • Introduction to Git Commands
    • Demo of Git Commands on ML files
    • What is Docker?
    • Key Concepts: Docker Image, Container, Dockerfile, Docker Hub
    • Introduction to Docker Commands
    • Creating a Dockerfile
    • Building a Docker Image for an ML application
    • Pushing the Docker Image to Docker Hub
    • Creating a Container to isolate our applications
    • Docker to generate a container of a web application from Streamlit
    • Sharing a Docker Image with Other users
  • Model Deployment to Cloud Service - Render
    • Introduction to Render - Platform as a service
    • Connecting Render to GitHub
    • Building a web app usng Dockerfile in Git repository
    • Serving your web app to users
  • Model Interpretability
    • Basics of interpretability with SHAP
    • Interpreting Scikit-learn models with SHAP
    • Interpreting models with SHAP in Pycaret
  • Introduction to Cloud Computing with Microsoft Azure
    • What is Cloud Computing
    • Advantages of Cloud Computing
    • Introduction to Microsoft Azure
    • Common Azure Services
    • Why Microsoft Azure
    • Creating Azure Free Account
    • Creating Azure Resource Group
    • Creating Azure Workspace
    • Creating Azure Storage Account
    • Provisioning Azure Synapse Analytics Workspace
  • Introduction to Azure Machine Learning
    • Overview of Azure Machine Learning Studio
    • Creating Azure ML Workspace
    • Provisioning Azure Compute Cluster/Instance
    • Building Classification models with Azure Automated ML
    • Explaining Best Model on Azure ML Studio
    • Working with Notebooks on Azure ML Studio
  • Model Deployment with Azure
    • Introduction to Machine Learning in Cloud
    • Putting the ML application into production in Azure Container with Docker
    • SDKs and Azure Blob Storage for model deployment to Azure
    • Model training and production deployment in Azure Blob Storage
    • Download the Azure Blob Storage model and get predictions
  • 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
  • Code repository with 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
  • Introduction to Flask
    • What is Flask?
    • Uses of Flask
    • Difference between Flask and Streamlit for ML Applications
    • How to create a Flask Application for Computer Vision Tasks
    • How to create a Flask Application for NLP Tasks
    • Introduction to HTML 
    • Introduction to HTML Tags
    • Creating HTML Forms
  • Introduction to Generative AI For NLP
    • Introduction to Generative AI
    • Introduction to Large Language Models (LLMs)
    • Introduction to Langchain 
    • Buidling a PDF Application with LLMs
  • 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 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.
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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 September 9, 2023

What's included

  • Certificate
  • 17 Modules
  • Live Classes
  • Lifetime access
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