Daily Dose of Data Science | Avi Chawla | Substack·1yBuilding a Real-time Voice RAG Agent
Real-time voice interactions are becoming increasingly popular. This post provides a detailed, step-by-step guide on building a real-time Voice RAG Agent. Key components include using AssemblyAI for speech-to-text transcription, LlamaIndex for document-based answers, and Cartesia for generating seamless speech. The post includes a video and open-source code for easy implementation.
Daily Dose of Data Science | Avi Chawla | Substack·1y12 Powerful Tools For AI Agents
A comprehensive guide listing 12 powerful tools included in the CrewAI framework for building AI agents. The tools range from file reading and writing, code interpreting, and web scraping to advanced functionalities like RAG-powered searches and natural language to SQL conversion. Additionally, the post highlights a full crash course on AI agents, covering everything from fundamentals to production optimization.
Daily Dose of Data Science | Avi Chawla | Substack·1y5 Levels of Agentic AI Systems
Agentic AI systems are capable of making decisions, calling functions, and running autonomous workflows. The levels of AI agency include basic responders, router patterns, tool calling, multi-agent patterns, and fully autonomous patterns. Each level indicates a different degree of independence and capability of the AI system.
Daily Dose of Data Science | Avi Chawla | Substack·1yTime Complexity of 10 ML Algorithms
Understanding the run-time complexity of machine learning algorithms is essential for efficient model implementation. Popular algorithms like SVM and t-SNE have limitations with large datasets due to their cubic and quadratic time complexities, respectively. Accurate knowledge of these complexities helps in selecting the right algorithm and optimizing performance.
Daily Dose of Data Science | Avi Chawla | Substack·1yAI Agent Crash Course—Part 7
The AI Agent Crash Course Part 7 focuses on providing knowledge for building Agentic systems, covering various aspects such as memory, agentic flows, and design patterns. It aims to fully equip readers with the ability to build autonomous AI systems that can reason, plan, take actions, and correct themselves when necessary. The course is practical and beginner-friendly, covering the fundamentals and beyond.