Things should have known before attending to Microsoft AI Day, Thailand (1 May 2024)

be prepared

Microsoft Fabric

Microsoft Fabric is an all-in-one analytics solution for enterprises. It covers everything from data movement to data science, real-time analytics, and business intelligence.

Components:

• Data Lake: A storage repository that holds vast amounts of raw data in its native format.
• Data Engineering: Provides a Spark platform for large-scale data transformation.
• Data Integration: Facilitates seamless data movement and integration.
• Data Science: Supports building and deploying machine learning models.
• Real-Time Analytics: Enables real-time insights from streaming data.
• Power BI Integration: Integrates with Power BI for interactive visualizations.

Advantages:

• Simplifies analytics needs by offering an integrated environment.
• Centralized administration and governance.
• Unified data lake for preferred analytics tools.
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Vector Search

Vector search uses mathematical vectors to represent and efficiently search through complex, unstructured data. It focuses on discovering linked concepts rather than just keywords.

Working Principle:

• Represents data points as vectors in a high-dimensional space.
• Calculates similarity between query vectors and possible vector paths.
• Uses techniques like cosine similarity or Euclidean distance.

Benefits:

• Enhanced semantic relationships and contextual meaning.
• Efficiently finds similar items in datasets.
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RAG (Retrieval-Augmented Generation)

RAG optimizes large language models (LLMs) by referencing authoritative knowledge bases outside their training data. It supplements prompts with external information.

Use Cases:

• Improves factual reliability without extensive model fine-tuning.
• Enhances LLM output for specific domains.

Components:

• Prompt Engineering: Skillful creation of text prompts.
• Fine-Tuning: Adapting pre-trained LLMs for specific use cases.
• RAG (Knowledge Augmentation): Incorporating external sources into prompts.
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LLMOps (Large Language Model Operations)

LLMOps manages the lifecycle of LLMs and LLM-powered applications.

Key Concepts:

• Collaboration Environment: Provides tools for AI developers and IT administrators.
• Prompt Engineering, Fine-Tuning, and RAG are part of LLMOps.
• Central Setup and Management: Security configuration, cost control, and governance.
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Azure AI Studio

Azure AI Studio is a generative AI development hub. It provides tools for building, deploying, and managing AI applications.

Capabilities:

• Collaboration Environment: Allows teams to build and manage AI applications.
• Prompt Engineering, Fine-Tuning, and RAG can be used within Azure AI Studio.
• Central Management: Security settings, project organization, and governance controls.
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Prompt Flow

Prompt Flow involves skillfully creating text prompts to guide LLMs toward producing desired outputs. It’s crucial for achieving accurate and contextually relevant responses.

• Techniques: Few-shot, chain-of-thought (CoT) prompting.
• Use Cases: Interaction with LLMs via API calls or user-friendly platforms.
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