Study stack
#Study stack code
It offers the distributed version control and source code management (SCM) functionality of Git, plus its own features. Every lazy data scientist should try this early on in the project. See my 12-Hour ML Challenge article for more details.
#Study stack manual
We should be able to iterate fast with minimum manual processing. Pipeline tools are critical to the speed and quality of development. The Countertop: Deployment Pipeline Tools It is designed to handle a range of workloads, from single machines to data warehouses or Web services with many concurrent users.Īlternatives: MySQL, SAS, IBM DB2, Oracle, MongoDB, Cloudera, GCP, AWS, Azure, PaperSpaceĢ. I haven’t tested them with other programming languages, such as R or Java.Ī free and open-source relational database management system (RDBMS) emphasizing extensibility and technical standards compliance. So the tools work well with or are built with native Python. Work for big or small projects at start-up or large enterprisesĬaveat: I use Python 99% of the time.Future proved (adoption & tool maturity).My list prioritizes the following (not in order): It all depends on your needs and constraints. There are (too) many tools out there the possible combination is infinite. If you search “ML tools” in Google or ask a consultant, you are likely to get something like this: It highlights the most useful tools to design, develop, and deploy full-stack Machine Learning applications - solutions that integrate with systems or serve human users in Production environments. So, this article walks through my kitchen. Like many others, I like to use the analogy of cooking in a kitchen: there is the ingredient (data), the recipe (design), the process of cooking (well, your unique approach), and finally, the actual kitchen (tools). There are many aspects of delivering a professional data science project. This question came up many times and in various forms during chats with aspiring data scientists in schools, professionals who are looking to switch, and team managers. “How do I build good Machine Learning applications?”
![study stack study stack](https://i.ytimg.com/vi/zYqM61nE1XI/maxresdefault.jpg)
I use the term Data Science and ML interchangeably. Disclaimer : This post is not endorsed or sponsored.