Summary
We are now coming to the end of this book spanning the breadth of machine learning – from foundational concepts to cutting-edge generative AI. We started the book by covering core ML techniques, algorithms, and industry applications to provide a strong base. We then progressed to data architectures, ML tools like TensorFlow and PyTorch, and engineering best practices to put skills into practice. Architecting robust ML infrastructure on AWS and optimization methods prepared you for real-world systems.
Securing and governing AI responsibly is critical, so we delved into risk management. To guide organizations on the ML journey, we discussed maturity models and evolutionary steps.
Closing the chapter by looking at generative AI and AGI, we explored the immense possibilities of the most disruptive new capability currently. Specifically, we delved into the intricacies of generative AI platforms, RAG architecture, and considerations for generative AI production deployment...
