DP-100T01: Designing and Implementing a Data Science Solution on Azure Training

DP-100T01: Designing and Implementing a Data Science Solution on Azure Training is based on providing technical knowledge of machine learning and Python. This leads to managing data preparations and ingestions, model training, and launching machine learning solutions inspection in Microsoft Azure.

📘 Azure 🎓 Certification: YES 👥 1478 Enrolled ⏱️ 3 Days 💼 Intermediate Level ⭐ 4.8 | 113 Reviews

Why Microtek Learning?

500+

Courses

10+ Years

Experience

95K+

Global Learners

Virtual Instructor-Led Training

$1834

📄 Download PDF

Course Overview

DP-100T01: Designing and Implementing a Data Science Solution on Azure Training teaches is focused on proving skills in working with machine learning solutions at the cloud scale. This technical course enhances and supports professionals’ current knowledge of machine learning and Python. Further, using this upgrade for managing data preparations and machine learning solutions with Microsoft Azure.

This course consists of an in-depth understanding of concepts from Data preparation, and model training to management. Throughout the course, participants will be engaging in hands-on labs that include creating Azure Machine Learning workspace, workflows, and pipelines.

Our enterprise training program is best for organizations and companies. Its prominent focus is on automation, management, and monitoring of machine learning by using Azure Learning Service.

This training program is ideal for data scientists with experience in machine learning frameworks and python, who want to develop and work with machine learning solutions in the cloud.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Dec 03, 2025	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Dec 17, 2025	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Jan 07, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Jan 21, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Feb 04, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Feb 18, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Mar 11, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR
Mar 25, 2026	9:00 am - 5:00 pm EST	3 Days	online	$1834	GTR

+ View more schedules

What you will learn

Doing Data Science on Azure
Doing Data Science with Azure Machine Learning service
Automating Machine Learning with Azure Machine Learning service
Managing and Monitoring Machine Learning Models with the Azure Machine Learning service
Exploring and configuring machine learning workspace
Optimizing model training with Azure Machine Learning
Deploy and consumer models with Azure Machine Learning

Who Should Attend This Course?

Data Scientists who are experienced in Python and have an understanding of how machine learning works. This can include TensorFlow, PyTorch, and Scikit-Learn.
These experienced professionals who are willing to create and manage machine learning solutions in the cloud.
Given below are professionals who can use Designing and Implementing a Data Science Solution on Azure Training to upskill their current positions:
- Data Scientist
- AI Engineers
- DevOps Engineers
- Software Developers
- Machine Learning Engineers
- IT professionals
- Cloud Solution Architects
- Technical Team Leads

Prerequisites

One should have a fundamental knowledge of Microsoft Azure
One should understand the basics of data science and machine learning concepts
Familiarity with common science processes which include data exploration, feature engineering, model training, and evaluation.
Required experience with Python programming
Basic fundamental knowledge of cloud computing fundamentals, specifically with Microsoft Azure ecosystem

📞 Talk to a Learning Advisor

📘 DP-100T01: Designing and Implementing a Data Science Solution on Azure Outline

Design a data ingestion strategy for machine learning projects

Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution

Design a machine learning model training solution

Identify machine learning tasks
Choose a service to train a model
Choose between compute options

Design a model deployment solution

Understand how a model will be consumed.
Decide whether to deploy your model to a real-time or batch endpoint.

Design a machine learning operations solution

Explore an MLOps architecture.
Design for monitoring.
Design for retraining.

Explore Azure Machine Learning workspace resources and assets

Create an Azure Machine Learning workspace.
Identify resources and assets.
Train models in the workspace.

Explore developer tools for workspace interaction

The Azure Machine Learning studio.
The Python Software Development Kit (SDK).
The Azure Command Line Interface (CLI).

Make data available in Azure Machine Learning

Access data by using Uniform Resource Identifiers (URIs).
Connect to cloud data sources with datastores.
Use data asset to access specific files or folders.

Work with compute targets in Azure Machine Learning

Choose the appropriate compute target.
Work with compute instances and clusters.
Manage installed packages with environments.

Work with environments in Azure Machine Learning

Understand environments in Azure Machine Learning.
Explore and use curated environments.
Create and use custom environments.

Find the best classification model with Automated Machine Learning

Prepare your data to use AutoML for classification.
Configure and run an AutoML experiment.
Evaluate and compare models.

Track model training in Jupyter notebooks with MLflow

Configure to use MLflow in notebooks
Use MLflow for model tracking in notebooks

Run a training script as a command job in Azure Machine Learning

Convert a notebook to a script.
Test scripts in a terminal.
Run a script as a command job.
Use parameters in a command job.

Track model training with MLflow in jobs

Use MLflow when you run a script as a job.
Review metrics, parameters, artifacts, and models from a run.

Perform hyperparameter tuning with Azure Machine Learning

Define a hyperparameter search space.
Configure hyperparameter sampling.
Select an early-termination policy.
Run a sweep job.

Run pipelines in Azure Machine Learning

Create components.
Build an Azure Machine Learning pipeline.
Run an Azure Machine Learning pipeline.

Log models with MLflow.
Understand the MLmodel format.
Register an MLflow model in Azure Machine Learning.

Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Understand Azure Machine Learning's built-in components for responsible AI.
Create a Responsible AI dashboard.
Explore a Responsible AI dashboard.

Deploy a model to a managed online endpoint

Use managed online endpoints.
Deploy your MLflow model to a managed online endpoint.
Deploy a custom model to a managed online endpoint.
Test online endpoints.

Deploy a model to a batch endpoint

Create a batch endpoint.
Deploy your MLflow model to a batch endpoint.
Deploy a custom model to a batch endpoint.
Invoke batch endpoints.

❓ Frequently Asked Questions

DP-100 exam comprises professionals who have experience in applying data science and machine learning to run machine learning workloads by leveraging Microsoft Azure.

DP-100 is considered to be doable with the right preparation. One needs to have experience in Python and have a basic understanding of machine learning.

?One needs to wait at least 24 hours for their second attempt. In order to renewal of the certification there is no limit. The only requirement is it should be cleared before the certification expires.

One should least score 700 out of 1000 to pass the DP-100 examination.