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the newsletter  

Highlighting the achievements of female and non-binary leaders in enterprise and deep tech

Justine and Brittany here, coming at you with the december edition 

the highlights

Enterprise technology reads 

Deep technology breakthroughs

Women making news 

the insights: ML Ops

Each month we hold a curated roundtable discussion on a topic, then share our insights, relevant resources, and an interview with a prominent leader in the space with the newsletter. Suggestions for future topics? We’d love to hear them! Want the invite to our next curated event? Hit the reply button 👇

For our first focus area, we chose ML Ops—it’s an area that’s getting a lot of attention lately.  Justine has broken down the toolchain and the value chain for those looking to learn more about this emerging ecosystem.  

"The ML Ops ecosystem is emerging at the same time as the production ML market. Looking at the toolchain, the pain points are clear and it’s an exciting time as the canonical stack (ie. MEAN) for production ML deployments has not yet been established. The corresponding value chain is still forming, and the market opportunity is still materializing. This article provides a high level overview of both." Read Justine’s perspective on ML Ops here.

the interview
Stephanie Sher 

This month, we were lucky enough to have the opportunity to interview Stephanie Sher. Stephanie started her tech career in software, as an early employee at NYC-based infrastructure monitoring company Datadog. She has been a product manager in robotics/manufacturing automation as well as led growth in the ML devtool space, and now serves as GM at a stealth startup. She enjoys organizing communities of founders, executives, ML engineers/data engineers/data scientists at some of the world's leading deeptech companies, and also advises and invests in applied AI/ML companies.  Select excerpts below, read the full interview here.

  • Which part of the ML toolchain do you think is the most painful or most lacking in supporting infrastructure?  

    At the moment, most blockers still lie in data preparation. 90% of what is preventing people from successfully deploying ML in production is related to data management. The next part of the chain is training tools, but those are pretty good already; the space is nearly saturated. But pretty soon we'll see a shift towards the rest of the ML toolchain: monitoring the health and integrity of models in production; understanding why we're getting the results we're getting; being able to identify issues early and react to them in a timely manner.
     
  • What lessons did you learn during your time at Datadog that you think are transferable to the ML Ops ecosystem? What similarities and differences do you see between APM and monitoring of ML workflows?

    So much, and nothing at all. There are of course similarities in the sense of knowing how a SaaS B2B tool comes into being, how to guide product development alongside initial market research, how to distribute effectively. But while we do see a lot of the same terminology being thrown around, ie monitoring and observability - at the same time, I think it is a very different technology that looks more like the discipline of software engineering integrated with the art of machine learning rather than just SWE, which on its own is a bit more straightforward.

    In terms of the market, something I learned at Datadog is that you can't guess based on first principles how the ecosystem will evolve. For example, people like to write about Datadog's "strategy" in the beginning but it was much less intrinsically directed, and perhaps had more to do with landscape and positioning, than people like to think. So I suppose the "lesson" here is mostly an excitement to see how the different players in the ecosystem approach the MLOps challenge, and how users react to different tools that surface.

    One thing I'll call out here: Datadog has a world-class solutions engineering team that has been crucial to the company's success. I expect to see more of this from companies, whether APM or ML offerings - diligent investment into highly competent onboarding and implementation support.
     
  • You have been involved at the forefront of several emerging industries. What research or technical developments in the industry are you the most excited about as you think about the next 2-5 years? 

    In the MLOps world, ideally we'll one day be able to see models in production that are updating in real-time. We're not quite there just yet due to ramped up operational infrastructure needs (more computing power, better monitoring/alerting, attention to failover responsibility) that most companies aren't operationally prepared for yet, but as with APM monitoring and alerting, I hope we'll soon see latency improvements across the board, driving towards more timely and effective decision-making.
     
  • What work are you most proud of? 

    I'm proud of every student that has come through the Full Stack Deep Learning program. I think it takes a special kind of chutzpah to explore the boundaries of your field, and to do that outside of your normal delineated work hours. In the FSDL community, I've seen people complete our curriculum while taking on full time jobs, internalize and leverage our curriculum to land jobs at, for example, NASA's Frontier Development Lab, and push each other within the community to work through both tactical and strategic, organizational and technical problems while trying to deploy machine learning in production at an industry level.

    For myself, I'm not sure "proud" is the word I want to use, but there are two experiences that I'm especially grateful for. First, being the second marketing hire at Datadog and demoing our product across the United States and Europe alongside AWS as it scaled up, back in 2014 - that was an incredible opportunity and something I learned a lot from. It was very exciting to be showing the world what was coming their way - this incredibly pragmatic tool that would streamline their workflows - I remember telling people it'd give them more time to spend with their friends and family, haha. Second, because I care a lot about advancement in software/AI/tech in general and even moreso in conjunction with community and education - I'm grateful to Josh and Sergey and Pieter to have brought me onto the Full Stack Deep Learning team, to push forward a curriculum that teaches ML practitioners how to deploy machine learning in production. Again I'm not sure "proud" is the right word, but working with them on this community has definitely been, and continues to be, a most rewarding and worthwhile endeavor, full stop.

    There is a third piece of work that's very exciting to me: I'm working with some friends in the space on an initiative to make hands-on experience with robotics and machine learning more accessible. It is still in early stages, but I'm very excited for its potential to put research into practice. Troubleshooting, debugging, monitoring live progress.. For me that's where the fun begins.

    Read the rest of the interview here.

the resources: ML Ops 

the talent   

the end


The january edition will focus on the Future of Work

about the table

Women are still severely underrepresented in founding enterprise technology companies—only 2% of women start B2B companies, compared to 13% of men (according to MIT). the table is a community of women in enterprise technology, including founders, operators, academics, and investors, driven to change that statistic. 

the table hosts curated monthly events and publishes this monthly newsletter discussing trends in enterprise technology, sharing insights, and featuring interviews with experts across various sub-sectors. By joining the table, you can be a part of our efforts to create a more inclusive enterprise technology community. 

Have feedback for us? Get in touch at thetable.inbox@gmail.com, we read everything you send! 

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