Deep dive into the three fundamental paradigms of machine learning. Explore supervised learning for predictions, unsupervised learning for pattern discovery, and reinforcement learning for decision optimization with practical Python examples.
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Machine Learning Fundamentals: A Comprehensive Guide to Enterprise AI Foundations
Discover the foundations of machine learning from an enterprise architect’s perspective. Learn core ML concepts, the ML workflow, and practical Python implementations to kickstart your AI journey.
Read more →Google Gemini API: Building Multimodal AI Applications with 2M Token Context
Introduction: Google’s Gemini API represents a significant leap in multimodal AI capabilities. Launched in December 2023, Gemini models are natively multimodal, trained from the ground up to understand and generate text, images, audio, and video. With context windows up to 2 million tokens and native Google Search grounding, Gemini offers unique capabilities for building sophisticated […]
Read more →Tips and Tricks – Implement Retry Logic for LLM API Calls
Handle rate limits and transient failures gracefully with exponential backoff.
Read more →Tips and Tricks – Implement Domain Events for Loose Coupling
Use domain events to decouple components and enable reactive architectures.
Read more →The Complete Guide to RAG Architecture: From Fundamentals to Production
Master Retrieval-Augmented Generation (RAG) with this expert-level guide. Learn about RAG types (Naive, Advanced, Modular, Agentic), chunking strategies, embedding models, vector databases, hybrid retrieval, and production best practices with high-quality architecture diagrams.
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