Microsoft Course: Explore and configure the Azure Machine Learning workspace
Azure ML Workspace Configuration & Resource Management: Build Your ML Foundation!
- Create and configure Azure ML workspaces for team collaboration
- Utilize Studio, Python SDK, and CLI for workspace operations
- Establish datastores and manage data assets effectively
- Optimize compute instances/clusters for ML workloads
- Prepare infrastructure for scalable model development
- Align with industry
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Microsoft certifications in AI
What you will learn
This 3-hour 37-minute Azure Machine Learning course provides a comprehensive exploration of workspace configuration and management. Through 5 hands-on modules, learners will master workspace creation, developer tool integration (Studio, Python SDK, CLI), data asset management, compute resource optimization, and environment configuration. Designed for data professionals, it combines theoretical concepts with practical exercises for deploying scalable ML workloads in Azure.
Microsoft's Explore and configure the Azure Machine Learning workspace course Outline
Learn the Explore and configure the Azure Machine Learning workspace
This expertly crafted Microsoft Learn path guides you through workspace configuration essentials - from initial setup to advanced resource management. Whether you're learning to configure compute clusters, manage data assets, or implement reproducible environments, the course offers practical exercises and real-world scenarios. Discover how to streamline ML operations using Studio, Python SDK, and CLI tools. Click to start building professional-grade ML infrastructure while earning certification credits!
Start your journey today:
Course Structure Includes:
Module 1: Workspace Fundamentals (42 min)
- Workspace creation & resource identification
- Asset management & model training basics
Module 2: Developer Tools Mastery (57 min)
- Studio interface navigation
- Python SDK/CLI integration techniques
Module 3: Data Management (39 min)
- URI concepts & datastore configuration
- Data asset creation & accessibility
Module 4: Compute Optimization (37 min)
- Instance/cluster configuration
- Target selection strategies
Module 5: Environment Setup (42 min)
- Curated vs custom environments
- Dependency management & reproducibility
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Master Azure ML workspace setup, data/compute configuration, and environment management. Learn to use Studio, Python SDK, and CLI tools while building enterprise-ready ML infrastructure through hands-on exercises.
Pre-requisites
None
SPOTO Empowers You to Earn Your Certification.
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Microsoft Certified: Azure AI Fundamentals (AI-900) is an entry-level certification designed to validate foundational knowledge of AI and machine learning concepts, including computer vision, natural language processing (NLP), generative AI, and responsible AI principles. Ideal for both technical and non-technical professionals, it requires no prior coding experience and offers free, flexible learning resources via Microsoft Learn. The certification enhances career prospects in high-demand roles like AI Engineer or Data Scientist, with potential salary increases up to 20%, while serving as a gateway to advanced Azure certifications (e.g., AI-102). Cost-effective and time-efficient (preparation in 10–30 hours), the exam focuses on real-world applications using Azure Cognitive Services, OpenAI APIs, and Azure Machine Learning. With permanent validity and global recognition across 90+ countries, it equips professionals to drive AI innovation in industries such as healthcare and finance. Start your AI journey with Microsoft Learn today!
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SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. The materials include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on designing, implementing, and deploying machine learning solutions with Microsoft services and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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