Azure Machine Learning & MLOps : Beginner to Advance

This hands-on video shows you how to apply responsible AI toolkits to assess and interpret your machine learning models with explainability, fairness, error analysis, and what if (counterfactuals)scenario analysis dashboards using Microsoft Azure Machine Learning.

This part 2 hands-on video shows you recent updates on how to apply responsible AI toolkits to assess and interpret your machine learning models with explainability, fairness, error analysis, and what if (counterfactuals)scenario analysis dashboards using Microsoft Azure Machine Learning.

This Video contains an end-to-end hands-on example of how to leverage the Azure Machine learning pipeline to manage, orchestrate and schedule all machine learning steps properly.

This video shows how to leverage feast as a feature store in Azure. It contains a hands-on example of enabling and utilizing feature store in Azure Machine Learning to build, manage and share features. It uses SQL database or Synapse as an offline store, Azure Redis as an online store, blob storage as feature registry and uses Azure Machine Learning for model training (using historical features registered by feature store), deploying the model and real-time inferencing (using online feature store).

This video shows how to leverage Ray and Dask in Azure Machine Learning over compute clusters for distributed and parallelized processing. It contains a hands-on example of enabling and utilizing Ray cluster with Dask in Azure Machine Learning for data processing, hyperparameter tuning and model training.

This video shows how to leverage Auto ML for Computer Vision in Azure Machine Learning over a GPU compute cluster. It contains a hands-on example of using Auto ML for object detection and deploying the model on a Kubernetes cluster for real-time inferencing.

This hands-on video shows you how to apply Differential Privacy for training Machine Learning models with sensitive(private) data through training a deep learning model for a healthcare example.

This hands-on video shows you how to Integrate Azure Databricks with Azure Machine Learning for big data Machine Learning jobs through orchestrating and executing Azure Databricks notebooks in Azure Machine Learning pipeline using Databricks Step.

This video shows how to leverage Auto ML for Natural Language Processing (NLP) tasks in Azure Machine Learning over a GPU compute cluster. It contains a hands-on example of using Auto ML for multi-class text classification.

This video contains a hands-on example of how to leverage Many Model Step in Azure Machine Learning Pipeline to train and score Hundreds of thousands of machine learning models in Parallel over a CPU compute cluster. (e.g. separate ML models for: each patient for healthcare status prediction | each store for product demand prediction | each wind turbine to predict power generation | etc.)

This video explains and shows you how to deploy an automated and scheduled data drift monitoring solution in Azure Machine Learning.

This hands-on video shows you how to link Azure Synapse Analytics and Azure Machine Learning workspaces and attach Apache Spark pools so that you can use this attached Apache Spark pool powered by Azure Synapse Analytics, as a dedicated compute for data wrangling at scale or conduct model training all from the same Python notebook

This video shows how to deploy a web service with multiple models in a step-by-step fashion in Azure Machine Learning:

.Register Models

.Deploy Models as Webservice

You have an existing model deployed in production and you want to deploy a new version of the model. How do you roll out your new ML model without causing any disruption? A good answer is blue-green deployment, an approach in which a new version of a web service is introduced to production by rolling out the change to a small subset of users/requests before rolling it out completely

This video shows you how you can consume your real-time deployed Azure machine learning model in Power BI for creating predictions over your table in your Power BI dashboard. So you can :

Score machine learning models (deployed using Azure Machine Learning) in Power BI.

Connect to an Azure Machine Learning model in the Power Query Editor.

Create a report with a visualization based on that model.

In this video, we train and deploy a real-time machine learning model using Azure ML designer. Then, we use the user interface for this machine learning model, created in Power Apps by using the low-code interface that Power Apps provide.

Realizing Machine Learning anywhere with Azure Kubernetes Service and Arc-enabled Machine Learning. With Arc Kubernetes cluster, you can train or deploy models in any infrastructure on-premises, across multi-cloud, or the edge. This video showcases how you attach your on-prem Kubernetes to Azure Machine Learning and training your models.

This hands-on video goes through the Azure Machine Learning component, a self-contained piece of code that does one step in a machine learning pipeline. A component is analogous to a function - it has a name, inputs, outputs, and a body. Components are the building blocks of the Azure Machine Learning pipelines.

MLflow is an open-source framework, designed to manage the complete machine learning lifecycle. Its ability to train and serve models on different platforms allows you to use a consistent set of tools regardless of where your experiments are running: locally on your computer, on a remote compute target, a virtual machine or an Azure Machine Learning compute instance. This hands-on video shows you how to use MLflow for tracking experiments using MLflow in Azure ML

This video shows you how you can deploy your model for batch prediction in Azure ML. Batch endpoints are endpoints that are used to do batch inferencing on large volumes of data over a period of time. Batch endpoints receive pointers to data and run jobs asynchronously to process the data in parallel on compute clusters.

Optimizing machine learning models for inference (or model scoring) is difficult if you want to get optimal performance on different kinds of platforms (cloud/edge, CPU/GPU, etc.), since each one has different capabilities and characteristics. A solution to train once in your preferred framework and run anywhere on the cloud or edge is needed. This is where ONNX comes in and this video will show you a hands-on example in azure machine learning using PyTorch and ONNX.

This video explains and shows you how to deploy networking end to end for Azure Machine Learning for securing your ML environment access. For Azure machine learning behind the virtual network, it will explain how to get access to your workspace using: 1) Jumpbox VM ( Azure BAstion) 2) Point to site VPN

This video shows how to build an end-to-end lineage in Purview for Machine Learning scenarios (Using Synapse or Azure Machine Learning)

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