Masterclass in Azure AI and Cloud Machine Learning Solutions for Cloud Engineers

Module 1: Introduction to Azure AI Services and Infrastructure

ü  Overview of the Azure AI portfolio (Services vs. Platforms)

ü  Understanding AI-specific compute (GPU vs. FPGA)

ü  Introduction to Azure Applied AI Services

ü  Planning an AI project in the cloud

ü  Practical Session: Navigating the Azure Portal to provision an AI-ready resource group and Cognitive Services account.

 

Module 2: Managing Azure Machine Learning Workspaces

ü  Workspace structure: Assets, Compute, and Data

ü  RBAC roles for Data Scientists and Engineers

ü  Integrating with Azure DevOps and GitHub

ü  Managing workspace security and secrets

ü  Practical Session: Creating and configuring an Azure ML Workspace with enterprise-grade security settings.

 

Module 3: Data Engineering and Storage for ML Workloads

ü  Azure Data Lake Storage Gen2 for ML

ü  Connecting Datastores and Data Assets

ü  Data labeling and versioning strategies

ü  Using Azure Data Factory for ML pipelines

ü  Practical Session: Registering a Datastore and creating a versioned Data Asset from a remote CSV source.

 

Module 4: Harnessing Azure Cognitive Services for Vision and Language

ü  Computer Vision: Image analysis and OCR

ü  Natural Language Processing (NLP) with Language Service

ü  Speech-to-Text and Text-to-Speech integrations

ü  Customizing models with Custom Vision

ü  Practical Session: Building a Python application that uses Azure Language Service to perform sentiment analysis on live text.

 

Module 5: Designing Conversational AI with Azure Bot Service

ü  Bot Framework Composer essentials

ü  Integrating Language Understanding (LUIS/CLU)

ü  Deploying bots to web and mobile channels

ü  Managing state and dialogue flow

ü  Practical Session: Designing and deploying a basic FAQ bot integrated with a QnA Maker (Language Service) knowledge base.

 

Module 6: Automated Machine Learning (AutoML) for Engineers

ü  When to use AutoML vs. Custom Training

ü  Supported tasks: Classification, Regression, and Time-series

ü  Managing AutoML runs via the UI and SDK

ü  Analyzing AutoML guardrails and results

ü  Practical Session: Running an AutoML experiment to predict housing prices and identifying the best-performing algorithm.

 

Module 7: Building and Managing Azure ML Designer Pipelines

ü  Drag-and-drop model development

ü  Data transformation components

ü  Evaluating model performance in the Designer

ü  Publishing pipelines as endpoints

ü  Practical Session: Creating a visual training pipeline in the Designer to predict customer churn.

 

Module 8: Running Experiments with the Azure ML SDK

ü  Introduction to the Azure ML Python SDK v2

ü  Tracking experiments with MLflow

ü  Logging metrics, logs, and artifacts

ü  Working with environments and curated Docker images

ü  Practical Session: Writing a Python script to submit a local training run to a remote Azure ML compute cluster.

 

Module 9: Training Deep Learning Models on Azure Compute

ü  Understanding Compute Instances vs. Compute Clusters

ü  Distributed training with PyTorch and TensorFlow on Azure

ü  Using Azure Batch for high-volume scoring

ü  Scaling compute based on workload demand

ü  Practical Session: Configuring a multi-node GPU cluster to train a neural network model.

 

Module 10: Hyperparameter Tuning and Model Optimization

ü  Defining search spaces and sampling methods

ü  Early termination policies (Bandit, Median, Truncation)

ü  Using HyperDrive to automate tuning

ü  Model compression techniques

ü  Practical Session: Running a HyperDrive job to find the optimal learning rate and batch size for an ML model.

 

Module 11: Deploying Models to Azure Container Instances (ACI)

ü  Real-time inference requirements

ü  Creating entry scripts (score.py) and environment files

ü  Deploying to ACI for low-cost testing

ü  Consuming endpoints via REST

ü  Practical Session: Packaging a trained model and deploying it as a web service on ACI.

 

Module 12: Enterprise Deployment with Azure Kubernetes Service (AKS)

ü  Architecting high-availability inference clusters

ü  Autoscaling and GPU support in AKS

ü  Blue/Green and Canary deployment strategies

ü  Authenticating with Key Vault

ü  Practical Session: Deploying a production model to an AKS cluster and configuring auto-scaling based on traffic.

 

Module 13: Implementing MLOps: CI/CD for Machine Learning

ü  Principles of the MLOps maturity model

ü  Creating Azure DevOps pipelines for ML

ü  Triggering retraining based on new data

ü  Versioning models and deployment metadata

ü  Practical Session: Setting up a GitHub Action to automatically trigger a model training run on code push.

 

Module 14: Security and Compliance for AI in Azure

ü  Virtual Network (VNet) isolation for ML

ü  Using Private Endpoints for data and compute

ü  Threat protection for AI with Microsoft Defender

ü  Auditing AI activities with Azure Monitor

ü  Practical Session: Securing an Azure ML Workspace by disabling public access and enabling Private Link.

 

Module 15: Monitoring Model Performance and Data Drift

ü  Identifying data drift in production

ü  Setting up monitoring schedules

ü  Alerting on performance degradation

ü  Analyzing feature importance over time

ü  Practical Session: Configuring a Data Drift monitor and analyzing the impact of changing data distributions on model accuracy.

 

Module 16: Cost Management and Performance Tuning for AI

ü  Analyzing AI resource costs with Azure Cost Management

ü  Optimizing compute usage with Low-Priority VMs

ü  Caching data for faster training

ü  Profiling models for latency and throughput

ü  Practical Session: Implementing a cost-saving schedule for compute clusters and analyzing a model’s latency profile.

 

Module 17: Responsible AI: Bias Detection and Interpretability

ü  Using Fairlearn to detect demographic bias

ü  Model interpretability with InterpretML (SHAP/LIME)

ü  Generating Responsible AI dashboards

ü  Mitigating unfairness in model outcomes

ü  Practical Session: Generating an interpretability report to explain the top features driving a model's prediction.

 

Module 18: Capstone: Architecting an End-to-End Azure AI Solution

ü  Requirement analysis and architecture design

ü  Building the data and training pipelines

ü  Deploying a secure, monitored endpoint

ü  Presenting the final solution architecture

ü  Practical Session: A comprehensive 4-hour challenge to build, deploy, and secure a complete AI application on Azure.

About Our Trainers

 

Our trainers are Microsoft Certified Azure Solutions Architects and AI Engineers with years of field experience in deploying large-scale AI infrastructures for telecommunications, finance, and logistics sectors. They bring real-world "war stories" from the trenches of MLOps, ensuring that the training is not just theoretical but deeply rooted in production reality.

 

Quality Statement

 

Phoenix Training Center is committed to providing training that is always in sync with the latest Azure updates. Our labs are refreshed monthly to reflect changes in the Azure SDK and Portal, ensuring that participants learn on the most current version of the technology. We prioritize practical outcome-based learning over passive lecture attendance.

 

Tailor-Made Courses

We understand that every organization has unique challenges and opportunities as well as unique training needs. Phoenix Training Center offers tailor-made courses designed to address specific requirements and challenges faced by your team or organization. Whether you need a customized curriculum, a specific duration, or on-site delivery, we can adapt our expertise to provide a training solution that perfectly aligns with your objectives. We can customize this Course to focus on your industry, specific risk profile, or internal stakeholder dynamics. Contact us to discuss how we can create a bespoke training program that maximizes value and impact for your team. For further inquiries, please contact us on Tel: +254720272325 / +254737296202 or Email training@phoenixtrainingcenter.com

Admission Criteria

ü  Participants should be reasonably proficient in English. 

ü  Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

Terms and Conditions

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

ü  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.

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

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

ü  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@phoenixtrainingcenter.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 / +254737296202

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:

ü  Groups of 5 People and Above – Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, 5 days to the training.

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

ü  Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

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

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

ü  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.

Accommodation and Airport Transfer

For physical training attendees, we can assist with recommendations for accommodation near the training venue. Airport pick-up services can also be arranged upon request to ensure a smooth arrival. Please inform us of your travel details in advance if you require these services. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737296202