Azure Machine Learning Studio Training Course

Module 1: Introduction to Azure Machine Learning Ecosystem

• Overview of Azure ML Studio vs. Azure ML Classic and other AI services
• Understanding the Workspace: Assets, Environments, and Management tools
• Introduction to the Studio UI: Home, Author, Assets, and Manage sections
• Role-based Access Control (RBAC) and workspace permissions setup
• Understanding the pricing tiers and cost management for ML resources
• Practical session: Setting up an Azure ML Workspace and exploring the Studio interface

Module 2: Managing Data and Compute Resources

• Creating and managing Datastores for secure connection to Azure Storage
• Registering and versioning Data Assets (Files and Tables) for reproducibility
• Provisioning Compute Instances for development and Compute Clusters for scaling
• Configuring Kubernetes clusters for high-performance model inference
• Data labeling projects and managing labeling workforces within the studio
• Practical session: Connecting to an Azure Blob Storage datastore and creating a registered data asset

Module 3: Visual Model Building with Azure ML Designer

• Introduction to the drag-and-drop interface for building ML pipelines
• Using pre-built modules for data transformation, cleaning, and joining
• Implementing regression and classification modules without writing code
• Model evaluation using the Score Model and Evaluate Model components
• Publishing Designer pipelines as web services for easy accessibility
• Practical session: Building a complete end-to-end "Automobile Price Prediction" pipeline using Designer

Module 4: Accelerated Modeling with Automated ML

• Understanding the Automated ML (AutoML) engine for classification and forecasting
• Configuring primary metrics, exit criteria, and concurrency limits
• Featurization settings and automatic handling of missing values/scaling
• Analyzing the Best Model dashboard and exploring individual run metrics
• Direct deployment of models generated through the AutoML interface
• Practical session: Running an AutoML experiment to find the best model for a credit risk dataset

Module 5: Code-Based Experimentation with Python SDK

• Setting up the Azure Machine Learning Python SDK v2 in Jupyter Notebooks
• Creating and running Scripts as Command Jobs in the cloud
• Logging metrics and artifacts using MLflow integration in Azure
• Using Environments to manage dependencies and Docker images for runs
• Debugging remote runs using local compute and diagnostic logs
• Practical session: Submitting a local Python training script to run on an Azure ML Compute Cluster

Module 6: Advanced Model Training and Hyperparameter Tuning

• Implementing Hyperdrive for automated hyperparameter optimization
• Configuring sampling methods: Random, Grid, and Bayesian sampling
• Defining early termination policies to save compute costs (Bandit, Median)
• Distributed training concepts for large datasets using PyTorch or TensorFlow
• Managing model checkpoints and resuming interrupted training jobs
• Practical session: Using Hyperdrive to optimize the learning rate and batch size of a deep learning model

Module 7: Creating and Managing ML Pipelines

• Building modular and reusable workflows using the Pipeline component
• Passing data between pipeline steps using Input and Output definitions
• Scheduling pipeline runs for recurring data processing or model retraining
• Using the CLI v2 to trigger and manage pipeline executions
• Visualizing pipeline graphs and tracking lineage across multiple steps
• Practical session: Constructing a two-step pipeline consisting of data preparation and model training

Module 8: Model Registration and Deployment

• Registering models in the Model Registry with tags and properties
• Deploying models to Managed Online Endpoints for real-time inferencing
• Configuring Batch Endpoints for high-volume, asynchronous data processing
• Testing endpoints directly within the Studio UI using JSON requests
• Managing blue/green deployments and traffic mirroring for safe updates
• Practical session: Deploying a trained model as a REST API and testing it with sample data

Module 9: Responsible AI and Model Explainability

• Using the Responsible AI (RAI) dashboard to assess model fairness
• Identifying features that contribute most to model predictions (Global vs. Local)
• Detecting and mitigating data bias across different demographic groups
• Understanding Error Analysis to identify specific cohorts where models fail
• Generating PDF reports for model governance and stakeholder transparency
• Practical session: Generating an explanations report to see why a model made specific predictions

Module 10: MLOps and Workspace Security

• Implementing CI/CD pipelines for machine learning using GitHub Actions or Azure DevOps
• Monitoring model performance and data drift in production environments
• Securing the workspace with Private Links and Virtual Networks (VNet)
• Managing secrets and credentials using Azure Key Vault integration
• Best practices for model versioning and artifact tracking across environments
• Practical session: Setting up a data drift monitor to alert on changes in incoming production data

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588