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Upskilling in Generative AI: A Data Scientist’s Comprehensive Roadmap to Building Compound Systems
- Generative AI offers an opportunity to solve challenges once deemed impossible. Building AI agents that leverage pre-trained models and integrate custom ML models to solve complex problems; we can design systems that transcend traditional capabilities.
- Generative AI should not replace traditional ML methods but rather enhance them, enabling the creation of compound AI systems.
- Across the 10-part series, it explores foundational topics like neural networks and transformers while diving into cutting-edge trends such as retrieval-augmented generation (RAG) workflows and agentic AI.
- A multi-paradigm system combines diverse methodologies (statistical models, traditional machine learning, modern ML models, algorithms, and symbolic reasoning) into a cohesive solution.
- Generative AI refers to deep neural networks capable of creating new content like text, images, audio, and beyond. Utilizing generative AI isn’t just a technological innovation – it’s a paradigm shift for data scientists, enabling the creation of powerful, multi-paradigm systems.
- The series provides a structured roadmap to provide clarity and actionable insights for data scientists to navigate 10 critical areas of modern generative AI. From working with APIs to Ethics in AI.
- Fine-tuning customizes pre-trained models using domain-specific data, while RAG enhances generative outputs by integrating external knowledge dynamically.
- Agentic AI systems are grounded in theoretical models like the Rational Agent framework and BDI architecture, leveraging techniques such as goal decomposition, memory management, and task orchestration to enable robust multi-agent workflows.
- Deploying generative AI systems involves transforming conceptual models into production-ready applications by integrating scalable infrastructure, secure workflows, and advanced optimization techniques.
- Ethics in generative AI addresses critical concerns like data bias, transparency, and accountability, ensuring that systems align with societal values.
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