Designing ML Solutions on Azure & Preparing for DP-100 Exam

Design, Train & Deploy ML Models on Azure using AutoML, Pipelines, MLOps, and LLMs with Prompt Engineering & RAG

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What you'll learn

  • Learn how to architect ML workflows using Azure services, from data ingestion to model deployment.
  • Create, configure, and manage workspaces, datastores, compute targets, and environments.
  • Use Azure Notebooks and Synapse Spark to clean, transform, and explore datasets.
  • Train models automatically for tabular, vision, and NLP tasks while applying responsible AI guidelines.
  • Perform hyperparameter tuning using Bayesian optimization, random search, and early stopping.
  • Record model training runs, metrics, parameters, and artifacts for robust experimentation tracking.
  • Design modular ML pipelines that can be automated, reused, and scaled in production.
  • Serve real-time and batch predictions using Azure endpoints with appropriate compute configurations.
  • Apply fairness, explainability, and model management best practices throughout the ML lifecycle.
  • Fine-tune, prompt-engineer, and deploy LLMs using Azure OpenAI, Prompt Flow, and Retrieval Augmented Generation (RAG).

Requirements

  • Familiarity with supervised and unsupervised learning, algorithms (e.g., regression, classification), and model evaluation metrics.
  • Ability to write and understand basic Python code, especially using data science libraries like pandas, scikit-learn, numpy, and matplotlib.
  • Experience with data preprocessing, feature engineering, model training, and validation.
  • General understanding of cloud concepts and services, particularly within the Azure ecosystem.
  • Basic experience using notebooks for exploratory data analysis and model training.
  • Basic knowledge of Git for managing code and experiments is helpful for working in collaborative environments.
  • Understanding of concepts like mean, variance, correlation, and statistical significance will help in model evaluation and feature analysis.
  • Familiarity with metrics like accuracy, precision, recall, F1 score, and ROC-AUC, especially for classification and regression problems.
  • Knowledge of REST APIs can be helpful when deploying and interacting with machine learning models via endpoints.
  • Some tasks may require basic use of the terminal (e.g., starting compute instances, navigating directories).
  • Machine learning is iterative—students should be ready to test, fail, and improve their models continuously.
  • Critical thinking skills are important for choosing algorithms, designing experiments, and interpreting results.

Description

Build and Deploy Intelligent Machine Learning Solutions Using Microsoft Azure

This course is your complete guide to mastering data science workflows in the cloud. Designed for professionals who want to go beyond experimentation and take their machine learning models into production, it covers every stage of the ML lifecycle using Azure’s powerful suite of tools.

Whether you're looking to scale your data science capabilities, prepare for the DP-100 certification, or enhance your organization’s AI capabilities, this course delivers hands-on experience with the platforms and practices used in real-world enterprise environments.

You will gain hands-on expertise in:

  1. Designing effective ML architectures on Azure
    • Choosing the right dataset formats and compute targets
    • Structuring experiments for scalability and performance
    • Integrating Git and CI/CD pipelines for streamlined collaboration
  2. Preparing and managing data at scale
    • Wrangling and transforming data using notebooks and Synapse Spark
    • Accessing and versioning datasets via Azure ML datastores
    • Building and sharing environments across workspaces
  3. Training models using both automated and custom approaches
    • Leveraging AutoML for classification, regression, vision, and NLP
    • Developing custom training scripts using Python and MLflow
    • Tuning hyperparameters for optimal model performance
  4. Building and managing reproducible ML pipelines
    • Creating modular training components
    • Passing and transforming data between pipeline steps
    • Scheduling, monitoring, and debugging workflows
  5. Deploying models for real-time and batch inference
    • Configuring online endpoints for scalable predictions
    • Setting up batch endpoints for large-scale processing jobs
    • Implementing secure and compliant deployment workflows
  6. Optimizing advanced AI models and LLMs
    • Selecting and fine-tuning large language models
    • Designing prompt engineering strategies for accuracy and context
    • Implementing Retrieval Augmented Generation (RAG) systems
  7. Ensuring responsible AI and operational excellence
    • Applying fairness, transparency, and explainability principles
    • Using MLflow for experiment tracking and model governance
    • Automating retraining and monitoring in production

      If you’re ready to move beyond theory and start building machine learning systems that solve real business problems, this course is designed for you. It’s perfect for learners who want structured guidance, practical tools, and hands-on labs that mirror what professionals do in industry every day.

Who this course is for:

  • Data Scientists Seeking to scale their machine learning workflows using Azure Machine Learning and automate model deployment.
  • Machine Learning Engineers Interested in operationalizing models using pipelines, endpoints, and Azure DevOps integration.
  • AI Engineers and Researchers Working with large-scale models (LLMs) and looking to apply prompt engineering, RAG, and fine-tuning in production.
  • MLOps Professionals Focused on implementing CI/CD pipelines, model versioning, and lifecycle management using Azure services.
  • Developers with a Data Focus Transitioning into AI/ML roles and looking to gain hands-on experience with real-world projects in the cloud.
  • Cloud Architects and Solution Engineers Wanting to design scalable and secure ML architectures using Azure services and tools.
  • IT Professionals Preparing for the Microsoft DP-100 Certification Aiming to validate their skills in designing and implementing data science solutions on Azure.
  • University Students and Bootcamp Graduates With basic ML and Python knowledge, looking to build portfolio-ready projects and gain practical industry exposure.


Your Instructor


Anand Nednur
Anand Nednur

Anand Rao is a senior technical instructor and cloud consultant. He has worked with large enterprises for about 15 years and has a wide range of technologies in his portfolio. Anand is adept at not just cloud platforms (Azure , AWS and GCP) but also well-versed with IAM, security and automation with powershell and python.

In addition, he has been developing and updating the content for various courses. He has been assisting many engineers in the lab examinations and securing certifications.

Anand Rao has delivered instructor led trainings in several states in India as well as several countries like USA, Bahrain, Kenya and UAE. He has worked as a Microsoft Certified Trainer globally for Corporate Major Clients.

Anand is also a Certified seasoned professional holding certifications in following platforms:

Microsoft Certified Trainer ( MCT )
SY0-401 : CompTIA Security +
Scrum Certified master ( SCRUM )
ITIL V3
Certified Network Defender ( CND – EC-Council )

Certified Ethical hacker ( CEH – EC-Council )
70-640 MS Active Directory
70-533 MS Azure Administration
70-534 MS Azure Architecture
AWS certified solutions Architect – Associate
AWS certified sysops administrator – Associate
Google Cloud Platform-Cloud Architect (GCP)
Certified Cloud Security Knowledge ( CCSK )


Join Today & Get Access To This Course & Every Resource You Need Grow Your Cyber Skills & Advance Your Career. Beginner & Expert Training.

Course Curriculum


  Course Overview
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  Module 1 - Lesson 1 - What is Azure Machine Learning
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  Module 1 - Chapter 2 - Azure ML Architecture Deep Dive
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  Module 1 - Chapter 3 - Navigating the Azure ML Studio Interface
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  Module 1 - Chapter 4 - Workspace Resources and Asset Types
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  Module 1 - Chapter 5- Working with Visual Studio Code & Azure ML
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  Module 1 - Chapter 6 - Understanding Workspace Editions
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  Module 1 - Chapter 7 - Creating an Azure ML Workspace
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  Module 1 - Chapter 8 - Creating Compute Resources in Azure ML
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  Module 1- Chapter 9 - Exploring Azure ML with the CLI
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  Module 2 - Chapter 1- Introduction to Azure ML Designer
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  Module 2 - Module 2 - Exploring the Designer Interface
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  Module 2- Module 3 - No-Code vs Code-Based Machine Learning
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  Module 2 - Module 4 - Building a Training Pipeline with Designer
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  Module 2 - Module 5 - Interpreting Experiment Results in Designer
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  Module 2 - Module 6 - Creating an Inference Pipeline from Training Pipeline
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  Module 2- Module 7 - Real-Time vs. Batch Inference in Designer
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  Module 2 - Module 8 - Deploying a Model with Designer to ACI or AKS
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  Module 3 - Section 1. What Are Experiments and Runs in Azure ML?
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  Module 3 - Section 2- Anatomy of a Training Run in Azure ML
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  Module 3 -Section 3- Logging Metrics and Monitoring Runs
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  Module 3 - Section 4. Using Compute Targets: Local vs. Remote
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  Module 3 - Section 5. Experimentation Best Practices
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  Module 4 -Section 1 - Introduction to Data Management in Azure ML
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  Module 4 -Section 2 - Understanding Datastores in Azure ML
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  Module 4 - Section 3. Registering and Using Datastores
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  Module 4 - Section 4 - Creating and Registering Datasets in Azure ML
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  Module 4 - Section 5 - Mounting vs. Downloading Data
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  Module 4 -Section 6 - Best Practices for Managing Data in Azure ML
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  Module 5: Section 1. Introduction to Compute in Azure ML
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  Module 5: Section 2. Compute Instances vs. Compute Clusters
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  Module 5: Section 3. Attached Compute (Advanced Concepts)
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  Module 5: Section 4. Environments in Azure ML: What and Why
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  Module 5: Section 5 -Creating Custom Environments
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  Module 5- Section 6- Submitting Jobs to Compute Clusters
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  Module 6- Section 1- What Is an ML Pipeline in Azure ML?
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  Module 6 - Section 2. Components of a Pipeline Step
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  Module 6 - Section 3. Creating a Simple Two-Step Pipeline
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  Module 6 - Section 4 - Passing Data Between Pipeline Steps
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  Module 6 - Section 5. Publishing Pipelines for Reuse
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  Module 6 - Section 6. Pipeline Scheduling and Automation Options
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  Module 6 - Section 7. Best Practices for Pipelines
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  Module 7 - Overview of Deployment Targets in Azure ML
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  Module 7 - Creating a Real-time Inference Endpoint
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  Module 7 - Consuming Real-time Endpoints via REST API
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  Module 7 - Creating a Batch Inference Pipeline
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  Module 7- Versioning and Updating Deployments
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  Module 8 - Hyperparameters vs. Model Parameters
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  Module 8 - Azure ML Hyperparameter Tuning (HyperDrive / SweepJob)
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  Module 8 - Performing Hyperparameter Tuning in Azure ML
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  Module 8 - Introduction to Automated Machine Learning (AutoML) Type
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  Module 8 - Running an AutoML Experiment in Azure ML
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  Module 8 - Understanding AutoML Output & Explainability
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  Module 8 - Responsible AI Features in AutoML
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  Module 9 - Why Model Interpretability Matters ?
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  Module 9 - Model Explanation Techniques in Azure ML
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  Module 9 -Reviewing AutoML Explanations
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  Module 9 - Using the Explanation Client and SDK
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  Module 9 - Responsible AI & Fairness: What Azure ML Covers
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  Module 10 - Why Monitor ML Models in Production?
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  Module 10 - Overview of Monitoring Tools in Azure ML
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  Module 10 - Monitoring Model Services with Application Insights
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  Module 10 - Logging Custom Metrics in score.py
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  Module 10 - Monitoring Data Drift in Azure ML
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  Audio Version of Training
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

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