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Python: Bridge Between Data Science and Engineering to Reach Machine Learning

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Python: Bridge Between Data Science and Engineering to Reach Machine Learning

Understand technical considerations for ML including the business aspects of ML... and even more importantly, how to bridge the worlds of lab and production.

· AI Zone ·
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ActiveState has been betting on Python for a while. We’ve been providing companies with Python language-builds since 1999 — since before it became the language of choice for data science projects. So, we get why Python would be the means to bridge the worlds of data science and engineering.

Bridge Between Lab and Production

What we realized is we need another bridge for the enterprise: one between the lab to production, from the technical side to the business side. So, that’s what we’re setting out to do. We’re kicking off with a strong push to make ML accessible.

This is something new for us — new because up until now, we’ve been focused on building distros for different open-source languages. Companies engage with us throughout the software development lifecycle (SDLC) because of our expertise in build engineering, embedding deploying, and upgrading open source and open source as it relates to languages, specifically Python, Perl, Tcl, Go, and Ruby.

Recently, we realized our customers need something more from us: lessons we’ve gathered from working with companies as they move from the lab to production with ML.

Python Adoption

The pace of adoption of Python for ML is growing and accelerating. Learning Python, experimenting with algorithms, or using Python to try out a handful of models... that’s moving fast. But the pace to take the models from the lab and push them into production at the scale to provide business impact... well, that’s something we’ve seen move much more slowly.

Data and People

And we’ve found there are two key, but completely unrelated, reasons for this: data and people.

On the data front, it’s about getting the right data, and getting it into the shape you need (read cleaning, organizing, labeling it). On the other hand, on the people front, it’s a cultural shift driven by business questions. Questions like:

  • How do I even start?
  • Why would I even start?
  • What data do I use?
  • Don’t I have all the data I need already?
  • How will this change people’s jobs?
  • What’s the business case?
  • How quickly will I see a return?
  • How do I scale up the technical chops and resources?

There’s a lot of learning required, a lot of bridging between tech and business, and a lot of understanding what can work for a particular business and even who are the appropriate stakeholders to involve.

ActiveState’s mandate is evolving beyond just helping companies understand technical considerations for ML but to include the business aspects of ML... and even more importantly, one could argue, how to bridge between these two worlds.

Have a Story to Share?

We’re sharing use cases from companies who have successfully moved from lab to production in a series on the “why” and “how” of ML. In other words, what’s the business rationale for implementing ML, and what are the technical chops you need for implementing ML? If you have one you'd like to share, please let us know!

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ai ,machine learning ,data science ,python ,engineering

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