Designing and Implementing a Data Science Solution on Azure

4 Days

Intermediate

Kuala Lumpur

Program Overview

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Programme Module

Getting Started with Azure Machine Learning

No-Code Machine Learning

Running Experiments and Training Models

Working with Data

Working with Compute

Orchestrating Operations with Pipelines

Deploying and Consuming Models

Training Optimal Models

Responsible Machine Learning

Programme Objectives

Operate machine learning solutions at cloud scale using Azure Machine Learning
Use Python and ML frameworks (Scikit-Learn, PyTorch, TensorFlow) to build ML workflows in Azure
Manage the full ML lifecycle: data ingestion, preparation, model training, validation, and deployment
Create and manage Azure ML resources including workspaces, datasets, datastores, and compute targets
Run and manage experiments for training and tracking machine learning models
Build and automate ML workflows using pipelines (MLOps practices)
Deploy machine learning models for real-time and batch inference
Improve model performance using hyperparameter tuning and Automated Machine Learning (AutoML)
Implement CI/CD practices for machine learning solutions
Apply responsible AI principles including fairness, interpretability, and differential privacy
Monitor deployed models using Application Insights and data drift detection
Ensure continuous model performance through post-deployment monitoring and maintenance

Who Should Attend

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

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