Mlops With Ray Best Practices And Strategies For Adopting Machine Learning Operations Hien Luu by Hien Luu, Max Pumperla, Zhe Zhang 9798868803758, 8868803755 instant download after payment.

Machine learning (ML) has proven to be a very powerful tool to learn and extract patterns from data. The ability to generate, store, and process a large amount of data, and easily access computing power in the last decade has contributed to many advancements in the ML field, such as image recognition, language translation, and large language models (LLMs), that is, BERT, DALLE, ChatGPT, and more. ML has finally graduated from the academia lab and has been embraced with both open arms by the business world to help with solving real-world business problems and transforming industries by improving customer experience, reducing cost, improving business efficiency, and ultimately increasing their competitive advantage. According to McKinsey’s “The state of AI in 2021”  report, the findings from the survey indicate that AI/ML adoption is continuing its steady rise across many companies in many regions of the world. One of the reasons for this rise is due to the impact that AI has on the business bottom line.

How are ML projects different from the software engineering projects? What’s unique about ML projects? Can DevOps methodologies help with ML projects? Let’s examine these questions to deeply understand MLOps and the benefits it provides.