Noah Gift - Practical MLOps

Weve both spent most of our careers automating things. When we first met and Alfredo didnt know Python, Noah suggested automating one task per week. Automation is a core pillar for MLOps, DevOps, and this book throughout. You should take all the examples and opinions in this book in the context of future automation.

If Noah could summarize how he spent from 2000-2020, it was automating just about anything he could, from Film Pipelines to software installation to Machine Learning pipelines. As an engineering manager and CTO at startups in the Bay Area, he built many data science teams from scratch. As a result, he saw many of the core problems in getting machine learning to production in the early stages of the AI/ML revolution.

Noah has been an adjunct professor at Duke, Northwestern, and UC Davis in the last several years, teaching topics that primarily focus on Cloud Computing, Data Science, and machine learning engineering. This teaching and work experience gives him a unique perspective about the issues involved in the real-world deployment of machine learning solutions.

Alfredo has a heavy ops background from his System Administrator days, with a similar passion for automation. It is not possible to build resilient infrastructure without push-button automation. There is nothing more gratifying when disaster situations happen than re-running a script or a pipeline to recreate what crashed.

When Covid-19 hit, it accelerated a question we both had, which was, why arent we putting more models into production? Noah touched on some of these issues in an article he wrote for Forbes https://www.forbes.com/sites/forbestechcouncil/2019/02/04/why-there-will-be-no-data-science-job-titles-by-2029/?sh=ffbf7e13a8f6. The summarized premise of the article is something is wrong with data science because organizations are not seeing returns on their investments.

Later at OReillys Foo Camp, Noah led a session on Why cannot we be 10X faster at ML in Production?, where we had a great discussion with many people, including Tim OReilly, Mike Loukides, Roger Magoulas, and others. The result of that discussion was: yes; we can go 10X faster. So thanks to Tim and Mike for stirring such as fascinating discussion and getting this book on its way.

Machine Learning feels a lot like many other technologies in the last several decades. At first, it takes years to get results. Steve Jobs talks about how NeXT wanted to make it 10X faster to build software (and he did): Watch The Steve Jobs Interview On YouTube Here. What are some of the problems with Machine Learning currently?

• Focus on the code and technical details vs. the business problem

• Lack of Automation

• HiPPO (Highest Paid Persons Opinions)

• Not cloud-native

• Lack of urgency to solve solvable problems

Quoting one of the things Noah brought up in the discussion: Im anti-elitism across the board. Programming is a human right. The idea that there is some priesthood that is only allowed to do it is just wrong. Similar to machine learning, it is too crucial for technology to only lie in the hands of a select group of people. With MLOps and AutoML, these technologies can go into the publics hands. We can do better with machine learning and artificial intelligence by democratizing this technology. Real AI/ML practitioners ship models to production, and in the real future, people from doctors, lawyers, mechanics, and teachers will use AI/ML to help them do their jobs.