Best & Worst SE Practices for ML

This was all action no fluff. Hands down the most useful talk about software engineering best practices for ML. The talk aggregated the data of over 500 practitioners surveyed.

I heard a choir of angels singing while Alex presented. One question that I have been asking lots to different guests (and anyone who will listen) is what best practices are baked into the new MLOps tools. He made a cool website that lays out all 45 best practices so you can see them in a list. It was inspired by the ML test score and sighted as a large inspiration.

Along with a few colleagues, Alex Serban put out a few papers that highlights the highs and lows of current ML practices throughout various industries. The good, the bad, and the ugly. Read a few of my favorites here and here.

For the quick ultra version, check out the slide deck of the meetup here. For the more entertaining version click the button below.

I am warning you though. This presentation will only leave you with more questions.

Building a Bootstrapped MLOps Services Startup

Soumanta Das of Yugen.AI joined us this time round to discuss his lessons about the MLOps industry.

These hard earned lessons Somanta has picked up as he's worked on building out Yugen. We ranged far and wide when it came to topics, from discussions about how to manage ML teams to building high performing software delivery groups.

Soumanta's perspective as an entrepreneur with a truly technical mindset is pretty rare. I encourage you to listen in and share on the knowledge we got to access!

Till next time,
Vishnu

Launchable.AI

Korey MacDougall built out FastServe, a service to convert your pre-trained fast.ai models into API endpoints. You upload a model (e.g., export.pkl), and FastServe generates an API endpoint you can integrate into your applications.

Intrigued when I saw this come through, I wanted to get the skinny on the creation from Korey himself.

"I was inspired to start Launchable.AI when I worked through the fastai course, 'Practical Deep Learning for Coders'. I was impressed with how much they (Jeremy Howard and Rachel Thomas) had lowered the barrier to entry to applied machine learning, with both the fastai library and their courses (and especially the top-down pedagogical approach they have adopted from David Perkins). I loved their mission of democratizing access to AI. I wanted to contribute to that mission of making AI more accessible.

"With Launchable, we are particularly interested in the intersection of accessible AI and low-code entrepreneurship. I think that each of these 2 forces are going to empower folks without traditional tech backgrounds to build products, services, and companies with new perspectives and values, and will hopefully lead, in the next few years, to a richer technology landscape and entrepreneurial climate. Combining these two developments can provide enormous leverage for individuals and small teams to build products that a few years ago would have required mid-size teams with multiple skill sets (data science & engineering, cloud infrastructure, and UI/UX, at least). Now, with things like Bubble (on the front-end) and Peltarion (on the back-end), a single maker/hacker can build an AI-powered web-app in a day, and we think that is going to be game-changing.

"We're trying to empower folks to build businesses and products that take advantage of these two sources of leverage. We do that partly through educational content (e.g., our YouTube channel), partly through consulting, and partly through product development.

"Our most recent product development efforts have been focused on FastServe, which is a service that simplifies the deployment of fastai models. It allows data scientists to upload a trained model and get back an API endpoint, which can then be plugged into any application.

"The idea came out of our work training and deploying models, both internally and for clients. We would train a model, spin up some infrastructure (a web server and a web application, typically FastAPI or Flask) to serve predictions from the models as an API, and then build a front-end to consume that API. As we repeated this process, again and again, we started automating some portions of it. We got to where we could deploy models very quickly, using templates and resources we'd built, but were finding that clients, especially small data science teams, didn't have the time or expterise to maintain the infrastructure (things like SSH-ing into servers to apply updates, updating serverless functions, modifying web applications, and so on). So we built some custom dashboards and workflows for clients, to do things like update their model or spin up a new endpoint. That allowed the clients to iterate more easily and took away some of the ops headaches. We've had some positive feedback from this approach, and thought it worth exploring whether other folks would see value in a similar offering.

"So we built FastServe, and we're hoping this will be of particular use to data scientists who want to leverage the development speed of fastai on the backend and of No-Code platforms like Bubble on the front-end. We think of it as the missing link for low-code AI.

"There are several other services that allow data scientists to deploy models as API endpoints, like HuggingFace's Accelerated Inference API and the Peltarion platform (both amazing, BTW), but we're focused specifically on simplifying the model -> API step for fastai developers. Making that as seamless as possible especially in the context of low-code application development. We are working now on gathering feedback from the community to see what would be the most valuable additions to the platform.

"So if anyone reading this is a fastai developer and would like to try to platform, please check out our free private beta."