Becoming a Hybrid AI Developer/Scientist

One of the most popular discussion topics these days is AI developer vs AI scientist. Rather than switching from one to the other, I offer simple advice to complement your current skillset to be more polyvalent. For the developer who wants to become a scientist, there a few trade secrets you can easily learn so that your team does not need to hire a data scientist and can rely on you instead. For the scientist, I explain what to learn to have much more efficient and positive interactions with developers, for the benefit of your company. Up to taking full ownership on some development projects. In both cases, I focus on the minimum to become a functional hybrid developer/scientist.

Some suggestions for data scientists:

• Learn database branching, a technique for testing, fine-tuning, backup and recovery. See how it works here.
• Create a public Web API or SDK that accepts datasets as inputs, process them, and return results to the user. Design your app to support 100k users per day. Write good documentation so that users don’t need to contact you for help. See examples here.
• Develop and maintain your own Python library on PyPi. Again, with good documentation.
• Write a smart crawler to parse millions of webpages. Design it so that it can resume from where it stopped in case of crash, and revisit URLs that failed on the first pass. Optimize speed. Use distributed architecture. Work on maintenance and augmentation.
• Work with IT in your company to get permission to create your own, local production environment. Or do it at home as a hobby. Automate your tasks.
• Teach classes on programming languages. Learn good programming practices while preparing your classes and offer a collaborative environment to students.
• Find enterprise datasets to work with or create your own (synthetic data). Stress-test your algorithms on these complex datasets. Identify bottlenecks in your algorithms and fix them.
• Test your code in multiple environments. Be aware of the version of each library that you use, and dependencies. Master versioning, git, virtual environments, and Docker.
• Learn how to automate data cleaning and deal with missing values. Master error handling.

?? Read full article here. Including the section for developers.