Master the Microsoft Azure AI ecosystem. Build with Azure ML Studio, leverage Azure OpenAI Service, deploy with AKS, and prepare for Azure AI certification exams.
End-to-end ML with Azure Machine Learning. Designer for no-code ML, Automated ML, compute clusters, datasets, experiments, and pipeline authoring.
Build Gen AI applications with Azure OpenAI. Deploy GPT-4 and other models, implement on-your-data features, content filtering, and enterprise-grade AI with Azure security.
Pre-built AI APIs for vision, speech, language, and decision. Computer Vision, Form Recognizer, Text Analytics, Translator, and custom model training with minimal data.
Build data pipelines for ML workloads. Data ingestion, transformation, orchestration, and integration with Azure ML for automated training data preparation.
Implement MLOps practices using Azure DevOps and GitHub Actions. CI/CD for ML, model versioning, automated testing, and deployment pipelines with approval gates.
Deploy ML models at scale with Azure Kubernetes Service. Real-time and batch endpoints, auto-scaling, blue-green deployments, and monitoring production models.
Microsoft Responsible AI tools: Fairlearn for bias detection, InterpretML for model explanability, error analysis, and implementing responsible AI practices in production.
Targeted preparation for Azure AI Engineer Associate (AI-102) and Azure Data Scientist Associate (DP-100). Exam strategies, practice questions, and study planning.
Cloud engineers and architects working in Microsoft/Azure environments who want to add AI capabilities
Data scientists and ML engineers who need to deploy and manage models on Azure
IT professionals preparing for Microsoft Azure AI and Data Science certification exams
Basic cloud knowledge required. You should be familiar with Azure fundamentals (VMs, Storage, Azure AD) and have basic Python skills. Some ML understanding is helpful but not strictly required.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
Yes. You will need an Azure subscription. New accounts get 200 USD in free credits which covers most of the course. Plan for approximately 50-100 pounds in additional costs over the 6 weeks.
Yes. The final module targets the AI-102 (Azure AI Engineer) and DP-100 (Azure Data Scientist) exams. The course content maps directly to the certification domains.
Azure OpenAI Service requires an application for access. We help you with the application process in the first week. Most applications are approved within a few business days.
Both are comprehensive ML platforms. Azure ML excels in integration with the Microsoft ecosystem (Office 365, Power BI, Azure DevOps) and is often preferred in enterprises already invested in Microsoft technologies.