Applied Data Science with Azure Machine Learning in a day

Title: Applied Data Science with Azure Machine Learning in a day

The announcement by Hollywood voice. "From the creators of Crazy Data science sessions, comes and spend a full-day hands-on training with Azure Machine Learning and see how we use Azure Machine Learning and come up with crazy experiments" :-)

Applied data science with Azure machine learning (AML) full-day training will deliver a thorough look into the world of data scientists using Azure Machine Learning. Training will give data scientists orientation on where and how to use AML, and how to build, deploy, and maintain predictive models with dedicated built-in tools, open-source frameworks and powerful Python SDK. Training will provide attendees with knowledge and power to use the platform for finding and making better decisions and models in your organisation.

With a fully-managed and enterprise-ready platform, Azure machine learning as a service is a powerful platform, that will suit every data science project and address every framework flavour.

Training consists of 7 modules and will explore all the important assets, tools and machine learning frameworks.

Short description
Applied data science with Azure machine learning (AML) full-day training will deliver a thorough exploration and look into to use AML, how to build, deploy, and maintain predictive models with dedicated built-in tools, open-source frameworks and powerful Python SDK and PyTorch.

What you will learn at this full-day training:
Understand the basic concepts of data science processes and what cloud services bring to the table
Get on board with designing and preparing Machine Learning solutions with AML
Explore data, train the models and use tune your prediction models
Learn how to prepare the model for deployment by creating pipelines and use of MLFlow
Deploy monitor and retrain your models
Learn how to use Python SDK and PyTorch
Use your models with other Azure Services (Synapse, SQL Database)
Training modules
The time outline for the training is designed in 7 modules each for 55minutes. Times are displayed in UTC. Coffee and lunch breaks will be aligned with the organisers on the day of the workshop.

08.45 – Gathering and preps
09.00 - 09.55 - Module 1
10.00 - 10.50 - Module 2
10.50 - 11.00 – Coffee Break
11.00 - 11.50 - Module 3
12.00 - 12.50 - Module 4
12.50 - 13.50 - LunchTime
14.00 - 15.00 - Module 5
15.00 - 15.50 - Module 6
15.50 - 16.00 – Coffee Break
16.00 - 17.00 - Module 7
17.00 + Gathering and wrap-up

Module 1
Module 1 - Starting with Azure Portal, Azure Machine Learning Services (AML) and Azure resources for AML will make a gentle segue to Azure Machine Learning services, azure subscription, key vaults, and services for storing data (Azure data store), Azure SQL Database or any other data source. We will also create the Azure roles and memberships so that we can use them in the upcoming modules. Get familiar with Azure CLI and Python SDK.

Module 2
Module 2 - Create, and manage your workspace, data, and compute for your experiments will be focusing on creating, configuring and managing your machine learning workspace, attaching data from Azure data store (created in Module 1), registering datastores and creating datasets. Furthermore, we will be exploring and configuring compute assets for optimal training workload.

Module 3
Module 3 - Explorative data analysis with Python and notebooks will dive into exploring approaches to data analysis, statistics and getting the most information and insights from your data. Using the instantiated workspace, attached with compute and datastores, we will be performing univariate, and multivariate statistics, exploring data using Python visualisation packages and taking advantage of notebooks.

Module 4
Module 4 - Preparing the models, running experiments, and training the model will teach you how to configure a job run, configure the compute and consume data from a job. With the help of notebooks, we will evaluate a model, and train and track the model using MLflow. After the process will be completed, we will implement a training pipeline, learn how to pass data between the steps and also take a look into using custom components and component-based pipelines.

Module 5
Module 5 - Using MLflow output, deploying, and retraining a model will explore the registered models in MLflow and how to use the relevant model, retrain and watch the model performance. We will also set both real-time and batch deployment and explore endpoints and how to use them using Azure ML CLI. The last part of this module will be focused on integrating the solution with Github and learning how to retrain the model with event-based triggers or scheduled triggers.

Module 6
Module 6 - Building end-to-end solution will deliver the complete experience for an end-to-end solution. This module will wrap up the previous four models, by using an MNIST model with an Azure ML job. We will deploy a model by using an online managed endpoint with the help of Azure ML CLI, register and track the model using MLflow, and create YAML for sweeping the model and instancing the inferring cluster for model consumption.

Module 7
Module 7 - Connecting with Azure Synapse, Azure Databricks, Azure Data Factory and SQL Server 2022 will look into consuming the AML solutions or predictive models (as API) with other Azure services. Using Azure Data Factory to orchestrate your Machine learning solution, enhance the use of big data with Azure Synapse and predictive models and use trained models with on-prem SQL Server 2022 as an external procedure.

Key takeaways
Learn how to use Azure Machine Learning service and be able to build a Machine Learning solution in a day from a scratch.

Target Audience
Data Scientists, Statisticians, Machine Learning Engineers

Broader Audience
Data Analysts, BI Analysts, Big Data analysts, Data engineers, Data architects, Tech Leader, DevOps Engineers, and Business Leader.

Prerequisite knowledge for attendees
Some background in Machine learning or statistics. Any additional knowledge of AML or EDA is a benefit for the workshop

Technical prerequisite for attendees
Working laptop with admin access (Win or Mac)
Installed Visual Studio Code and Python Environment
Conda environment and additional Python packages installed (Pytorch, ONNX, ...)
Access to the internet
Credentials and credit (free credit) for accessing the Azure portal
SQL Server 2022 developer edition (optional)
Material and demos

All materials (Markdown, iPynb, Bicep, Pytorch, Py) and accompanying materials will be handed to attendees before the workshop. Material is prepared for self-paced learning.
Tuesday 14 March 2023