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With Amazon SageMaker Canvas, data and business analysts can prepare data, train, and deploy machine learning (ML) models without any ML experience or writing a single line of code. You will learn to build ML models for tabular and time series…

Learn how to instrument distributed applications with OpenTelemetry and analyze telemetry data with Azure Monitor to observe and troubleshoot AI solutions on Azure.

Modules in this learning path:

• Instrument App Opentelemetry
• Analyze Telemetry Logs Metrics

Products: Azure Monitor

Roles: Developer

Level: Intermediate

Subjects: Artificial Intelligence, App Development

Duration: 161 minutes

The Official Practice Question Set: AWS Certified AI Practitioner (AIF-C01) consists of 20 questions. This question set aligns with the AIF-C01 version of the exam and exam guide. If you are looking for an assessment that is the same length as the…

The Official Practice Question Set: AWS Certified Machine Learning - Specialty (MLS-C01) consists of 20 questions. This question set aligns with the MLS-C01 version of the exam and exam guide. Official Practice Question Sets feature 20 questions…

The Official Practice Question Set: AWS Certified Machine Learning Engineer - Associate (MLA-C01) consists of 20 questions. This question set aligns with the MLA-C01 version of the exam and exam guide. If you are looking for an assessment that is…

Machine learning (ML) and Artificial Intelligence (AI) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate…

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey. We are aware some…

Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.

Modules in this learning path:

• Plan Prepare Genaiops
• Prompt Versioning Genaiops
• Evaluate Optimize Agents
• Automated Evaluation Genaiops
• Monitor Generative AI App
• Tracing Generative AI App

Products: Foundry Tools, Microsoft Foundry, Github

Roles: Data Scientist, AI Engineer, Devops Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing, Devops

Duration: 364 minutes

Learn the full MLOps lifecycle for machine learning models, from experimentation and pipeline automation to CI/CD, automated testing, and model deployment in production.

Modules in this learning path:

• Experiment Azure Machine Learning
• Perform Hyperparameter Tuning Azure Machine Learning Pipelines
• Run Pipelines Azure Machine Learning
• Trigger Azure Machine Learn Jobs Github Actions
• Trigger Github Actions Trunk Based Development
• Work Environments Github Actions
• Deploy Model Github Actions

Products: Azure Machine Learning, Github

Roles: Data Scientist

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning

Duration: 291 minutes

Learn how to select fine-tuning methods (SFT, RFT, DPO), recognize agent quality problems, prepare training data, and design optimization strategies using hyperparameter configuration and iterative evaluation.

Units in this learning path:

• Introduction
• Select Finetune Method
• Scenarios
• Prepare Data
• Optimize Finetune
• Exercise
• Knowledge Check
• Summary

Products: Microsoft Foundry

Roles: Data Scientist, AI Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing

Duration: 89 minutes

Learn how to optimize business processes using Microsoft 365 Copilot.

Modules in this learning path:

• Unlock Productivity Unleash Creativity AI Powered Chat
• Draft Impactful Documents Using AI
• Present Copilot Microsoft Powerpoint
• From Inbox Impact Improve Your Email Workflows AI
• Uncover New Data Insights AI

Products: M365, Office 365

Roles: Business User

Level: Beginner

Subjects: Artificial Intelligence, Business Applications, Compliance, Productivity, Security

Duration: 193 minutes

Explore complementary strategies to optimize generative AI model performance, including prompt engineering, system messages, model parameters, Retrieval Augmented Generation (RAG), and fine-tuning. Learn when to use each strategy and how to combine them.

Units in this learning path:

• Introduction
• Prompt Engineering
• Retrieval Augmented Generation
• Fine Tune Model
• Compare Combine Strategies
• Exercise
• Knowledge Check
• Summary

Products: Microsoft Foundry

Roles: Data Scientist, AI Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing

Duration: 131 minutes