In October 2025 - I attended these two courses
Python + AI: Level Up (Microsoft Reactor 9 Series)Explored how to build AI-powered apps using Python — from understanding large language models and embeddings to implementing Retrieval-Augmented Generation (RAG), multimodal inputs, and tool calling. Practiced creating small agents that can retrieve data, reason, and act. Learned safe design patterns for deploying AI systems responsibly.
AI-102: Designing and Implementing an Azure AI Solution (udemy)
Gained hands-on exposure to Azure AI Foundry — including model deployment, prompt flow design, and evaluation. Covered computer vision, document intelligence, natural language processing, and RAG pipelines. Focused on how to integrate AI models securely within enterprise data systems and build evaluation harnesses for quality and safety.
📘 AI-102: Azure AI Engineer Associate (AI-102) — In Progress
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Building the skills to design, build, and operationalize AI solutions using Azure AI services. ​Through hands-on learning with Azure AI Foundry, Cognitive Services, and OpenAI models, I’m strengthening my ability to connect language, vision, and search intelligence with institutional concepts.
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📘 AI-102 Study Guide:
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Plan & Manage Azure AI Solutions (15-20%)
• Selecting appropriate Azure AI services (vision, language, decision, etc.)
• Creating, deploying, and configuring AI services (endpoints, containers)
• Securing and monitoring AI workloads (authentication, keys, logging, diagnostics)
• Integrating AI into CI/CD and applying Responsible AI principles -
Implement Generative AI Solutions (10-15%)
• Provisioning and using Azure OpenAI / generative models
• Prompt engineering and controlling model parameters
• Fine-tuning / customizing models with domain data -
Implement an Agentic / Conversational AI Solution (5-10%)
• Building conversational agents (bots) that integrate with language services
• Managing conversational flows, context, and transforms -
Implement Vision & Computer Vision Solutions (15-20%)
• Image analysis, object detection, classification, tagging
• OCR / text extraction from images
• Video analysis & spatial analytics Microsoft Learn -
Implement Natural Language Processing (NLP) Solutions (30-35%)
• Text analysis: key phrases, sentiment, entity detection
• Speech-to-text, text-to-speech, and translation
• Language understanding models: intents, entities, Q&A modules Tutorials -
Implement Knowledge Mining / Document Intelligence (10-15%)
• Azure AI Search: indexing, skillsets, data ingestion
• Document Intelligence (prebuilt & custom models)
• Integrating document extraction into search pipelines​​
