• Course
  • Vendor

Learn to Design and Implement a Data Science Solution on Azure. GK# 100327

  • Course Start Date: 2021-09-27
  • Time: 09:00:00 - 17:00:00
  • Duration: 3 days 09:00 AM - 05:00 PM
  • Location: Virtual
  • Delivery Method(s): Virtual Instructor Led

Course Outline

Pre-Requisites

Before attending this course, students must have:

A fundamental knowledge of Microsoft Azure
Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Lessons

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

WHAT YOU'LL LEARN

  • Doing Data Science on Azure
  • Doing Data Science with Azure Machine Learning service
  • Automate Machine Learning with Azure Machine Learning service
  • Manage and Monitor Machine Learning Models with the Azure Machine Learning service

OUTLINE

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

  • Training Models with Designer
  • Publishing Models with Designer

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

  • Introduction to Experiments
  • Training and Registering Models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

  • Working with Datastores
  • Working with Datasets

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

  • Working with Environments
  • Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.

Lessons

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

  • Real-time Inferencing
  • Batch Inferencing

Module 8: Training Optimal Models

By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

  • Hyperparameter Tuning
  • Automated Machine Learning

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons

  • Introduction to Model Interpretation
  • Using Model Explainers

Module 10: Monitoring Models

After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

LABS

  • Lab : Creating an Azure Machine Learning Workspace
  • Lab : Working with Azure Machine Learning Tools
  • Lab : Creating a Training Pipeline with the Azure ML Designer
  • Lab : Deploying a Service with the Azure ML Designer
  • Lab : Running Experiments
  • Lab : Training and Registering Models
  • Lab : Working with Datastores
  • Lab : Working with Datasets
  • Lab : Working with Environments
  • Lab : Working with Compute Targets
  • Lab : Creating a Pipeline
  • Lab : Publishing a Pipeline
  • Lab : Creating a Real-time Inferencing Service
  • Lab : Creating a Batch Inferencing Service
  • Lab : Tuning Hyperparameters
  • Lab : Using Automated Machine Learning
  • Lab : Reviewing Automated Machine Learning Explanations
  • Lab : Interpreting Models
  • Lab : Monitoring a Model with Application Insights
  • Lab : Monitoring Data Drift

Cancellation Policy

We require 16 calendar days notice to reschedule or cancel any registration. Failure to provide the required notification will result in 100% charge of the course. If a student does not attend a scheduled course without prior notification it will result in full forfeiture of the funds and no reschedule will be allowed. Within the required notification period, only student substitutions will be permitted. Reschedules are permitted at anytime with 16 or more calendar days notice. Enrollments must be rescheduled within six months of the cancel date or funds on account will be forfeited.

Training Location

Online Classroom
your office

your city, your province
your country   

About Global Knowledge

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Global Knowledge is the world's leading learning services and professional development solutions provider. We deliver learning solutions to support customers as they adapt to key business transformations and technological advancements that drive the way that organizations around the world differentiate themselves and thrive. Our learning programs, whether designed for a global organization or an individual professional, help businesses close skills gaps and foster an environment of continuous talent development.

Training Provider Rating

This vendor has an overall average rating of 4.38 out of 5 based on 431 reviews.

I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from ... Read more
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I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from the capturing of what was presented as it was 5-6 hours of watching a screen before getting to the actual labs. There has to be a better way to lay out this particular course. In my previous course, the lectures were broken up by labs which worked out fantastic and kept you engaged in the course. There were days when in order to actually complete the labs, would go over the 9PM day end time frame. Was able to get the primary labs done, but if you want to get all the content completed, you cannot complete it in the window of this course, you will need to come back on your own time.

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Instructor was great
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Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platfo ... Read more
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Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platform.. from adobe to something called zoom; I had to call support line cause it stated our instructor wasn’t present. Thankfully I called cause everyone online was in the adobe virtual classroom waiting for what looked like a teacher who didn’t show up for class (IT didn’t get anything resolved until 10mins after start time). I felt like he was really getting hung up on very basic knowledge for the first half of the course (talking about how to create tabs and drag formulas as an example). I completed files a few times before he was done explaining. There was a scheduled fire drill for them (roughly 30mins)that also cut into our time, which wasn’t deducted from the hour lunch break or the two, fifteen min breaks. I also really wish he touched base more on the automating workbook functions portion which we barely did. I'm happy there were/are those study guides (learning videos) and exams to take on my own time that I hope after I've had the class are still available for me to learn from.

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