Free Download Designing ML Solutions on Azure & Preparing for DP-100 Exam
Published 6/2025
Created by Cyberdefense Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English + subtitle | Duration: 195 Lectures ( 22h 42m ) | Size: 17.7 GB
Design, Train & Deploy ML Models on Azure using AutoML, Pipelines, MLOps, and LLMs with Prompt Engineering & RAG
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 AzureThis 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
esigning effective ML architectures on AzureChoosing the right dataset formats and compute targetsStructuring experiments for scalability and performanceIntegrating Git and CI/CD pipelines for streamlined collaborationPreparing and managing data at scaleWrangling and transforming data using notebooks and Synapse SparkAccessing and versioning datasets via Azure ML datastoresBuilding and sharing environments across workspacesTraining models using both automated and custom approachesLeveraging AutoML for classification, regression, vision, and NLPDeveloping custom training scripts using Python and MLflowTuning hyperparameters for optimal model performanceBuilding and managing reproducible ML pipelinesCreating modular training componentsPassing and transforming data between pipeline stepsScheduling, monitoring, and debugging workflowsDeploying models for real-time and batch inferenceConfiguring online endpoints for scalable predictionsSetting up batch endpoints for large-scale processing jobsImplementing secure and compliant deployment workflowsOptimizing advanced AI models and LLMsSelecting and fine-tuning large language modelsDesigning prompt engineering strategies for accuracy and contextImplementing Retrieval Augmented Generation (RAG) systemsEnsuring responsible AI and operational excellenceApplying fairness, transparency, and explainability principlesUsing MLflow for experiment tracking and model governanceAutomating retraining and monitoring in productionIf 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.
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