Microsoft 
AWS 
EC-Council 
CompTIA 
PECB 
Artificial Intelligence 
Python ProgrammingDP-100: Designing and Implementing a Data Science Solution on Azure
Learn new skills and discover the power of Microsoft Azure with step-by-step guidance.
ML Workflow Training
Hands-On Learning
Learn from AI Experts
Instructor-led
Official Courseware
Flexible Schedule
Guaranteed To Run
DP-100T01: Designing and Implementing a Data Science Solution on Azure is an intermediate-level course designed for data scientists who want to build and manage machine learning solutions using Azure Machine Learning. Learners will gain hands-on experience with data ingestion, preparation, model training, deployment, and monitoring at cloud scale. The course covers working with Python and popular machine learning frameworks, and teaches how to configure workspaces, run experiments, optimize models, and deploy them as scalable services. By mastering these skills, participants can turn raw data into actionable insights and operationalize machine learning workflows within the Azure cloud ecosystem.
Course : Designing and Implementing a Data Science Solution on Azure
Level : Intermediate
Type : Instructor led Live online or Classroom
Length : 4 days
Labs : Included
Practice Test : Included
Intermediate data scientists using Azure tools
Python developers into machine learning projects
AI professionals transitioning to cloud ML operations
Cloud engineers expanding ML deployment skills
Developers working with Scikit-Learn, PyTorch, TensorFlow
Analysts turning data into predictive models
Tech professionals building scalable AI solutions
Learners aiming for Azure AI & ML job roles
Good knowledge of Python programming
Understanding of data science fundamentals
Configure and explore Azure Machine Learning workspaces
Perform data ingestion and preparation
Train and evaluate machine learning models
Run and optimize experiments and pipelines
Deploy models to real-time and batch endpoints
Monitor and manage model performance
Use MLflow integration for tracking models
Automate workflows and model lifecycle in Azure
Instructor-led Virtual online live Training.
Weekend Classes
Evening Classes
Weekdays Classes
Course Outline
Module 1: Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
Module 2: Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI
Module 3: Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset
Module 4: Work with compute targets in Azure Machine Learning
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
Module 5: Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments
Module 6: Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
Module 7: Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
Module 8: Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
Module 9: Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models
Module 10: Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
Module 11: Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job
Module 12: Register an MLflow model in Azure Machine Learning
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
Module 13: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard
Module 14: Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
Module 15: Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints
Module 16: Introduction to Azure AI Foundry
What is Azure AI Foundry?
How does Azure AI Foundry work
When to use Azure AI Foundry
Module 17: Explore and deploy models from the model catalog in Azure AI Foundry portal
Explore the language models in the model catalog
Deploy a model to an endpoint
Improve the performance of a language model
Module 18: Get started with prompt flow to develop language model apps in the Azure AI Foundry
Understand the development lifecycle of a large language model (LLM) app
Understand core components and explore flow types
Explore connections and runtimes
Explore variants and monitoring options
Module 19: Build a RAG-based agent with your own data using Azure AI Foundry
Understand how to ground your language model
Make your data searchable
Build an agent with prompt flow
Module 20: Fine-tune a language model with Azure AI Foundry
Understand when to fine-tune a language model
Prepare your data to fine-tune a chat completion model
Explore fine-tuning language models in Azure AI Studio
Module 21: Evaluate the performance of generative AI apps with Azure AI Foundry
Assess the model performance
Manually evaluate the performance of a model
Assess the performance of your generative AI apps
Module 22: Responsible generative AI
Plan a responsible generative AI solution
Identify potential harms
Measure potential harms
Mitigate potential harms
Operate a responsible generative AI solution
Best Value offer
Official Courseware
Instructor-led live online / Classroom
Flexible Schedule
Course Completion Certification
US$ 2350
Our upcoming class schedule is currently being finalized.
Let us know your preferred start date, and we'll update you.
We’re Here to Assist You
1 day
Fundamental
4 days
Intermediate
Some courses may require basic knowledge of IT infrastructure. Check the specific course details for prerequisites.
Yes, our expert instructor will help you from scratch, the course is designed in a way that you will get understanding of Cloud.
Sure, we would happy to assist you. Please direct your queries to info@infoventure.com
AWS Authorized Partner
Microsoft Authorized Partner
CompTIA Authorized Partner
EC-Council Authorized Partner
PECB Authorized Partner
Newsletter
Technology moves fast. Make sure you are up to speed with IT Trainings.
All rights reserved. © 2016 - 2026 - Infoventure Technologies Inc.
The expert in anything was once a beginner.
—Helen Hayes
Fill the Form
We HATE spam. Your email will never be shared
Preferred class : "Learning should never be limited by financial constraints. There are always ways to access education and gain knowledge, no matter your financial situation."
Save more,Learn more.
Take advantage of these opportunities and don't let financial constraints limit your education."
AWS Official Courseware
AWS Authorized Instructor
Course Completion Certification
We HATE spam. Your email address is 100% secure
Self paced Digital Training
After submitting, review your inbox and junk mail.