دانلود کتاب طراحی سیستم یادگیری ماشین با مثال‌های انتها به انتها

Machine learning (ML) system design is a relatively new term that often gets people from the industry puzzled. Many find it hard to compile a certain scope of responsibilities behind this term, not to mention trying to find a proper name for a respective role or position. The job may be done with various efficiency by ML engineers, software engineers, or even data scientists, depending on the scope of their role. While all of the positions are valid, we believe that to become a seasoned expert in ML system design, you have to encapsulate expertise from each of the backgrounds. Note that while some of the things we discuss in this book are specific to ML systems, others will be familiar to those readers who have already built non-ML software systems (you will find this information in chapters 2, 13, and 16). This is because ML system design, although a new phenomenon, is still based on the classic fundamentals of software development. But first we need to discover what ML system design is as a whole. In this opening chapter, we will suggest our take on the definition of ML system design and support it with examples from personal experience, both our own and those of our colleagues; we will describe the perfect persona for the position and share cases from our personal experience of why a coherent and consistent approach to designing an ML system will save you a lot of time in the long run and will help in delivering short-term business wins, which is crucial to gain the trust of colleagues in this new working method from the early stages. ML system design might sound familiar if you have ever tried interviewing at deep tech/big tech companies (the first term commonly stands for startups or R&D units within large corporations that either work with or develop cutting-edge technology, and the second term refers to the largest and most dominant tech companies of the world that are often known for their high bar in talent acquisition and advanced engineering culture) for ML engineer/manager positions. Both of us have vast, deep tech experience, so while planning to write this book, we were convinced the definition was clear enough to everyone, and there was no reason to dwell on it. However, after reaching out to a variety of people for their opinions on the outline, we saw that the term itself caused discord in opinions and interpretations. Perhaps this is due to the fact that the industry has long had a definite list of job titles, which gives candidates a relatively clear understanding of what set of functions and responsibilities they are applying for. The positions of software engineer, research engineer, ML engineer, and so on each entail a certain classic set of functions enshrined in textbooks and eloquently stated in job descriptions.