Low-Code ML with Azure Machine Learning
Kevin Feasel (@feaselkl)https://csmore.info/on/lowcodeaml
Who Am I? What Am I Doing Here?
Motivation
My goals in this talk:
- Provide an overview of Azure Machine Learning.
- Introduce Automated Machine Learning (AutoML).
- Train an ML model through the designer.
- Deploy an ML model and score data, individually and in bulk.
What We'll Do
What We'll Do
What We'll Do
What We'll Do
What We'll Do
Agenda
- A Brief Primer on Azure ML
- Automated Machine Learning (AutoML)
- Training a Model
- Deploying a Model
What is Azure Machine Learning?
Azure Machine Learning is Microsoft's primary offering for machine learning in the cloud.
Starting Out: Workspaces
A workspace is necessary to get started with Azure ML.
Workspace Basics
Advanced Workspace Settings
Key Components
There are several major components which make up Azure ML.
- Datastores
- Datasets
- Compute instances
- Compute clusters
- Azure ML Studio Designer
- Experiments and Runs
- Models
- Endpoints
- Inference clusters
Datastores
Datastores are connections to where the data lives, such as Azure SQL Database or Azure Data Lake Storage Gen2.
Datasets
Datasets contain the data we use to train models.
Compute instances
Compute instances are hosted virtual machines which contain a number of data science and machine learning libraries pre-installed. You can use these for easy remote development.
Compute clusters
Sometimes, you want something a bit more powerful to perform training. This is where compute clusters can help: spin them up for training and let them disappear automatically afterward to save money.
Designer
The Azure ML Studio Designer allows you to create training and scoring pipelines using a drag-and-drop interface reminiscent of SQL Server Integration Services.
Experiments and Runs
Experiments allow you to try things out in a controlled manner. Each Run of an experiment is tracked separately in the experiment, letting you see how well your changes work over time.
Models
The primary purpose of an experiment is to train a model.
Endpoints
Once you have a trained model, you can expose it as an API endpoint for scoring new data.
Inference clusters
Inference clusters are an easy method to host endpoints for real-time or batch scoring.
Demo Time
Agenda
- A Brief Primer on Azure ML
- Automated Machine Learning (AutoML)
- Training a Model
- Deploying a Model
What is AutoML?
Automated Machine Learning (AutoML) is a process intended to simplify the machine learning process. It provides two benefits:
- It reduces the level of skill needed to run a machine learning project.
- It provides us a minimum baseline of model quality--skilled practitioners can beat AutoML but it provides a nice "first approach" score.
Performing AutoML
Select a Dataset
Configure Model Parameters
Configure Features
Configure Features
Configure Features
Configure Features
Review Results
Demo Time
Agenda
- A Brief Primer on Azure ML
- Automated Machine Learning (AutoML)
- Training a Model
- Deploying a Model
Using the Designer
The Azure Machine Learning process:
- Get some data
- Clean up that data
- Choose an algorithm
- Train the model
- Evaluate the model
Demo Time
Agenda
- A Brief Primer on Azure ML
- Automated Machine Learning (AutoML)
- Training a Model
- Deploying a Model
Deploying a Model
Once we have trained a model, our next goal is to make it available.
Build a Pipeline
The first step is to build an inference pipeline. This is different from a training pipeline: we don't use the training dataset; instead, we accept user input and return a scored response.
Two Approaches
There are two approaches to inference pipelines:
- Real-time inference -- best for generating a few predictions
- Batch inference -- best for scoring large numbers of records
Let's look at real-time inference today.
Demo Time
Wrapping Up
Over the course of this talk, we have gained an understanding of the basics of low-code machine learning with Azure ML. This includes using the UI-heavy pipeline designers as well as taking advantage of AutoML.
Wrapping Up
To learn more, go here:
https://csmore.info/on/lowcodeaml
The next talk in the series:
https://csmore.info/on/amlindepth
And for help, contact me:
feasel@catallaxyservices.com | @feaselkl
Catallaxy Services consulting:
https://CSmore.info/on/contact