Learn Data Management, Building and Training Models, Model Optimization and Evaluation, Deploying ML models, MLOps
β±οΈ Length: 12.4 total hours
π₯ 13 students
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Course Overview
- Master Machine Learning on Microsoft Azure, gaining practical expertise for robust ML solution development and deployment.
- Explore the complete ML project lifecycle: from strategic data ingestion and advanced feature engineering to MLOps-driven model operationalization.
- Leverage Azure ML services for scalable computations, automated model development, and secure, high-performance model deployments.
- Navigate the Azure ecosystem tailored for ML, optimizing resource provisioning, managing costs, and fostering collaborative project management.
- Understand responsible AI: including model optimization, ethics, explainability, and continuous monitoring for drift and bias.
- Implement MLOps practices to automate and streamline ML pipelines, ensuring reliable and scalable deployments via CI/CD.
- Prepare for an Azure ML certification, positioning yourself at the forefront of cloud-powered AI innovation.
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Requirements / Prerequisites
- Foundational programming knowledge, ideally Python, beneficial for practical code examples within Azure ML.
- Basic statistical understanding and linear algebra concepts aid in comprehending ML algorithms.
- Prior high-level cloud computing exposure is helpful but not strictly required.
- A Microsoft Azure subscription (free trial sufficient) is essential for hands-on exercises.
- A keen interest in data-driven problem-solving and proactive learning are crucial.
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Skills Covered / Tools Used
- Azure Machine Learning Studio: Master designing, building, and managing ML workflows, automated ML, and designer pipelines.
- Cloud-Native Data Engineering: Expertise in Azure data services (Synapse Analytics, Databricks conceptually) for large-scale data preparation.
- Model Deployment & Inferencing: Deploy trained models to Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) for predictions.
- MLOps with Azure DevOps: Implement CI/CD pipelines for ML models, ensuring automated testing, versioning, and strategic deployment.
- Responsible AI & Governance: Interpret predictions, identify biases, and apply explainability techniques (SHAP, LIME) within Azure ML.
- Advanced Feature Engineering: Develop sophisticated techniques for creating impactful features and selecting optimal ones.
- Hyperparameter Optimization: Utilize Azure ML for automated hyperparameter tuning and model selection via grid search and Bayesian optimization.
- Model Monitoring & Retraining: Implement Azure Monitor for tracking production model performance, detecting data drift, and triggering automated retraining.
- Azure Compute Targets: Manage various Azure ML compute options (instances, clusters) for optimized cost and performance.
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Benefits / Outcomes
- Accelerated Career Growth: Position yourself as a skilled professional in end-to-end Azure ML solution implementation.
- Certification Exam Readiness: Be fully prepared to pass the relevant Microsoft Azure ML certification exam, boosting credibility.
- MLOps Mastery: Acquire critical abilities to operationalize ML models at scale with continuous integration and delivery.
- Cloud ML Project Confidence: Gain practical experience and assurance to manage complex ML projects using Azure services.
- Enhanced Problem-Solving: Develop a strategic approach to applying ML techniques for real-world business challenges.
- Portfolio-Ready Projects: Build a strong portfolio showcasing robust model development and effective deployment on Azure.
- Azure ML Community Access: Connect with practitioners, fostering collaboration and staying updated on cloud AI advancements.
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Pros
- Industry-Relevant Skills: Focuses on highly demanded cloud-based ML and MLOps skills, aligning directly with current job market trends.
- Practical, Hands-On: Emphasizes direct application through extensive exercises and real-world scenarios for deep understanding.
- Certification Pathway: Designed to prepare learners for a valuable Azure certification, offering tangible professional credentials.
- Comprehensive Coverage: Covers the entire ML project lifecycle, from data management to deployment and monitoring.
- Leading Cloud Platform: Equips learners with expertise on Microsoft Azure, a dominant and growing cloud platform for AI/ML.
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Cons
- Intensive Learning Pace: Comprehensive scope and short duration may challenge learners without prior ML or cloud experience.
Learning Tracks: English,IT & Software,Other IT & Software
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