Are you wrestling with the complexities of deploying and managing large language models? The rapid evolution of AI technologies demands robust solutions that can streamline development, enhance security, and scale effectively. However, the lack of clear guidance can make navigating this landscape daunting.
Enter this much needed book by Abi Aryan—a vital resource poised to transform your approach to MLOps. This comprehensive guide equips you with the essential techniques and tools to develop, deploy, and manage large language models efficiently. Whether you're a seasoned AI practitioner or just stepping into the field, this book is your gateway to mastering LLMOps, ensuring your projects are not just functional but flourishing.
By reading, you will:
- Gain a robust understanding of data versioning, experiment tracking, and model deployment
- Understand the architectures of models like OpenAI ChatGPT and how to fine-tune them
- Learn how to implement critical security measures and comply with privacy regulations
- Explore using Flask and Kubernetes to deploy models, optimizing for both performance and cost
- Discover how to integrate cutting-edge tools like ChatGPT and Whisper
Abi Aryan is the founder of Abide AI (www.abideai.com) and a machine learning research engineer with nearly a decade of experience building production-level ML systems. A mathematician by training, she previously served as a visiting research scholar at the Cognitive Systems Lab at UCLA, under Dr. Judea Pearl, where she focused on developing intelligent agents.
Abi has authored research papers in AutoML, multi-agent systems, and large language models, and actively reviews for leading research conferences and workshops, including NeurIPS, ACL (Association for Computational Linguistics), EMNLP (Empirical Methods in Natural Language Processing), and AABI (Advances in Approximate Bayesian Inference). She is currently advancing research in reflective intelligence in AI agents, distributed self-healing protocols for multi-agent systems, and GPU engineering for very large-scale AI systems.
