There are no items in your cart. Continue shopping on our Course Directory page.
Pay now -or- pay later
Pay by credit card at checkout
-or- pay later by invoice, as always.
Prepare for the official Microsoft Azure Data Scientist Associate certification exam DP-100 in this Designing and Implementing a Data Science Solution on Azure course. Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
LIVE, INSTRUCTOR-LED
TRAINING AT YOUR SITE
Customize Your Team Training Experience
CONTACT USOur FlexVouchers help you lock in your training budgets without having to commit to a traditional 1 voucher = 1 course classroom-only attendance. FlexVouchers expand your purchasing power to modern blended solutions and services that are completely customizable. For details, please call 888-843-8733 or chat live.
*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.
Aug 5 - 7 9:00 AM - 4:30 PM EDT Online (AnyWare) Reserve Your Seat
Aug 12 - 14 9:00 AM - 4:30 PM EDT Online (AnyWare) Reserve Your Seat
Aug 26 - 28 9:00 AM - 4:30 PM CDT Online (AnyWare) Reserve Your Seat
Sep 9 - 11 9:00 AM - 4:30 PM EDT Toronto / Online (AnyWare) Reserve Your Seat
Oct 7 - 9 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Reserve Your Seat
Oct 14 - 16 9:00 AM - 4:30 PM EDT Rockville, MD / Online (AnyWare) Reserve Your Seat
Oct 28 - 30 9:00 AM - 4:30 PM EDT New York / Online (AnyWare) Reserve Your Seat
Nov 18 - 20 9:00 AM - 4:30 PM EST Herndon, VA / Online (AnyWare) Reserve Your Seat
Dec 9 - 11 9:00 AM - 4:30 PM CST Austin / Online (AnyWare) Reserve Your Seat
Dec 16 - 18 9:00 AM - 4:30 PM EST Toronto / Online (AnyWare) Reserve Your Seat
Jan 13 - 15 9:00 AM - 4:30 PM EST Rockville, MD / Online (AnyWare) Reserve Your Seat
Jan 20 - 22 9:00 AM - 4:30 PM EST Ottawa / Online (AnyWare) Reserve Your Seat
Feb 3 - 5 9:00 AM - 4:30 PM EST New York / Online (AnyWare) Reserve Your Seat
Feb 17 - 19 9:00 AM - 4:30 PM EST Herndon, VA / Online (AnyWare) Reserve Your Seat
Mar 10 - 12 9:00 AM - 4:30 PM CST Austin / Online (AnyWare) Reserve Your Seat
Mar 17 - 19 9:00 AM - 4:30 PM EDT Toronto / Online (AnyWare) Reserve Your Seat
Guaranteed to Run
When you see the "Guaranteed to Run" icon next to a course event, you can rest assured that your course event — date, time — will run. Guaranteed.Partial Day Event
Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.Before attending this course, students must have:
This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.
This course can help you prepare for the following Microsoft role-based certification exam — DP-100: Designing and Implementing a Data Science Solution on Azure
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.
>LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to
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.
LessonsAfter completing this module, you will be able to