Azure Machine Learning & MLOps : Beginner to Advance

Azure Machine Learning & MLOps : Beginner to Advance, available at $79.99, has an average rating of 4.55, with 41 lectures, based on 5467 reviews, and has 17273 subscribers.

You will learn about How to use Azure Machine Learning from Development to Production How to Use Azure DevOps for Continuous Integration, continuous deployment on Machine Learning How to automate End 2 End machine Learning Solution on Azure How to Deploy Machine learning Models on Azure (Azure Container Instances, Azure Kubernetese Services, managed endpoints) Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning Bests practices and highly demanded capabilities of machine learning on Azure Cloud This course is ideal for individuals who are Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud or Data scientists who want to earn DP-100 Certification or Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path or Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud It is particularly useful for Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud or Data scientists who want to earn DP-100 Certification or Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path or Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud.

Enroll now: Azure Machine Learning & MLOps : Beginner to Advance

Summary

Title: Azure Machine Learning & MLOps : Beginner to Advance

Price: $79.99

Average Rating: 4.55

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: CA$239.99

Quality Status: approved

Status: Live

What You Will Learn

How to use Azure Machine Learning from Development to Production
How to Use Azure DevOps for Continuous Integration, continuous deployment on Machine Learning
How to automate End 2 End machine Learning Solution on Azure
How to Deploy Machine learning Models on Azure (Azure Container Instances, Azure Kubernetese Services, managed endpoints)
Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning
Bests practices and highly demanded capabilities of machine learning on Azure Cloud

Who Should Attend

Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud
Data scientists who want to earn DP-100 Certification
Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path
Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud

Target Audiences

Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud
Data scientists who want to earn DP-100 Certification
Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path
Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud

A course instructed by me and my digital twin if:

You are looking for a comprehensive, engaging, and fun course for mastering Azure Machine learning ( up to even advanced industry-required topics) plus fully hands-on end-to-end implantation of MLOps ( DevOps for Machine learning on Azure). If yes, then this is the right and very unique course for you!

Machine Learning Operations (MLOps) is a rapidly growing culture nad highly demanded in the industry with a set of principles, and guidelines defined in the machine learning world to deploy a machine learning model into production.

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.

You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models.

Key points about this course

Very detailed in-depth and comprehensive coverage
This course will help you prepare for entry into this hot career path of Machine Learning and MLOps
The course is regularly updated with recent features
Best practices and impactful features of Azure ML (e.g, Explainable AI) with its tricks are all covered
Contains some extra videos relevant to Azure Machine Learning and Databricks (Apache Spark)

Course Curriculum

Chapter 1: Azure MLOps

Lecture 1: Complete Intro to Azure Machine Learning Service

Lecture 2: Intro to Azure DevOps

Lecture 3: Setting up Azure DevOps Configurations

Lecture 4: Create & Deploy Infrastructure as Code Pipeline

Lecture 5: CI Pipeline ( Continuous Integration) for ML

Lecture 6: CI Pipeline ( Continuous Integration) for ML

Lecture 7: Automated Training with CI Pipeline

Lecture 8: CD Pipeline (Continuous Deployment) for Staging

Lecture 9: CD Pipeline(Continuous Deployment)for Production

Lecture 10: Testing End to End MLOps Pipelines

Lecture 11: Extra 1 : Azure MLOps with GitHub Actions & Azure Machine Learning

Lecture 12: Extra 2: Databricks MLOps With GitHub Actions & MLflow

Chapter 2: Azure ML SDK V2

Lecture 1: Azure MLOps V2 Accelerator

Chapter 3: Azure Machine Learning | Highly Demanded Capabilities

Lecture 1: Responsible AI in Action – Part 1

Lecture 2: Responsible AI in Action – Part 2

Lecture 3: Azure Machine Learning Pipeline

Lecture 4: Feature Store (Feast) in Azure (Machine Learning)

Lecture 5: Ray and Dask in Azure Machine Learning

Lecture 6: Auto ML for Computer Vision in Azure Machine Learning

Lecture 7: Differential Privacy For Machine Learning In Action (Sensitive Data)

Lecture 8: Integrate Azure Databricks with Azure Machine Learning

Lecture 9: AutoML for Natural Language Processing (NLP) in Azure Machine Learning

Lecture 10: Train and Score Thousands of Machine Learning Models in Parallel ( Many Models i

Lecture 11: Why do Machine learning models fail? (Data Drift Monitoring in Azure)

Lecture 12: Integrating Azure Synapse with Azure Machine Learning

Lecture 13: Deploy Multi Model Endpoint in Azure Machine Learning

Lecture 14: Rollout (Blue-Green) Real-Time Machine Learning Model Deployment in Azure

Lecture 15: Consume Azure Machine Learning models in Power BI

Lecture 16: Use PowerApps to Create Apps for Deployed Azure Machine Learning Models

Lecture 17: Attach Your OnPrem Kubernetes to Azure Machine Learning Using Azure Arc

Lecture 18: Components in Azure Machine Learning Pipelines

Lecture 19: Being Platform Agnostic with MLflow in Azure Machine Learning

Lecture 20: Deploy Spark ML model as Web Service Using Azure Machine Learning

Lecture 21: Azure Machine Learning Environments

Lecture 22: Scalable & Managed Batch Prediction with Azure Machine Learning

Lecture 23: Execute Azure Machine Learning pipelines in Azure Data Factory or Synapse

Lecture 24: Extra: Train Machine learning model once and deploy it anywhere with ONNX

Lecture 25: Extra: Azure Machine Learning Networking (Hands-on)

Lecture 26: Extra: AutoML Comparison in Databricks VS Azure Machine Learning

Lecture 27: Extra: Machine Learning Lineage with Microsoft Purview

Lecture 28: Extra: Azure + Open AI = The Future is Here

Instructors

Mohammad Ghodratigohar (MG)
Data Scientist and AI Cloud Solution Architect

Rating Distribution

1 stars: 33 votes
2 stars: 57 votes
3 stars: 448 votes
4 stars: 2031 votes
5 stars: 2898 votes

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