There’s No Such Thing As The Machine Learning Platform
In the past few years, you might have noticed the increasing pace at which vendors are rolling out “platforms” that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The “Data Science Platform” and “Machine Learning Platform” are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If you’re a major technology vendor and you don’t have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on?
The core of this insight is the realization that ML and data science projects are nothing like typical application or hardware development projects. Whereas in the past hardware and software development aimed to focus on the functionality of systems or applications, data science and ML projects are really about managing data, continuously evolving learning gleaned from data, and the evolution of data models based on constant iteration. Typical development processes and platforms simply don’t work from a data-centric perspective.
It should be no surprise then that technology vendors of all sizes are focused on developing platforms that data scientists and ML project managers will depend on to develop, run, operate, and manage their ongoing data models for the enterprise. To these vendors, the ML platform of the future is like the operating system or cloud environment or mobile development platform of the past and present. If you can dominate market share for data science / ML platforms, you will reap rewards for decades to come. As a result, everyone with a dog in this fight is fighting to own a piece of this market.
However, what does a Machine Learning platform look like? How is it the same or different than a Data Science platform? What are the core requirements for ML Platforms, and how do they differ from more general data science platforms? Who are the users of these platforms, and what do they really want? Let’s dive deeper.
What is the Data Science Platform?
Data scientists are tasked with wrangling useful information from a sea of data and translating business and operational informational needs into the language of data and math. Data scientists need to be masters of statistics, probability, mathematics, and algorithms that help to glean useful insights from huge piles of information. A data scientist creates data hypothesis, runs tests and analysis of the data, and then translates their results for someone else in the organization to easily view and understand. So it follows that a pure data science platform would meet the needs of helping craft data models, determining the best fit of information to a hypothesis, testing that hypothesis, facilitating collaboration amongst teams of data scientists, and helping to manage and evolve the data model as information continues to change.
Furthermore, data scientists don’t focus their work in code-centric Integrated Development Environments (IDEs), but rather in notebooks. First popularized by academically-oriented math-centric platforms like Mathematica and Matlab, but now prominent in the Python, R, and SAS communities, notebooks are used to document data research and simplify reproducibility of results by allowing the notebook to run on different source data. The best notebooks are shared, collaborative environments where groups of data scientists can work together and iterate models over constantly evolving data sets. While notebooks don’t make great environments for developing code, they make great environments to collaborate, explore, and visualize data. Indeed, the best notebooks are used by data scientists to quickly explore large data sets, assuming sufficient access to clean data.
This article originally appeared on forbes.com To read the full article and see the images, click here.
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