I attended my first AI-focused conference in several years at MLConf. It was great catching up on the state-of-the-art in machine learning and data science. While much of the conference was focused on deep learning, there were broader lessons to takeaway from the event. Here’s my key takeaways from the conference:

Building data products is much more than building models Tensors are the new primitive for ML work Be ready to trade off accuracy for space and time constraints Deep learning is interdisciplinary Deep learning is impacting more and more of our everyday lives Provide notebooks if you want to drive adoption ML companies are hiring!

I’ll discuss each of these points in more detail below. In the coming weeks, all of the slides will be available on SlideShare.

Building data products is much more than building models

The highlight of the conference for me was June Andrews’ talk on building intuitive machine learning systems. She presented a framework where a number of disciplines where involved in determining what kinds of data products to build and how to launch products into the wild. She presented a process that detailed which teams should be responsible for various tasks including: how to gather requirements for models, ways to research model proposals, how to evaluate models for deployment, how to determine whether to rollout models, how to scale up to full deployment of a model, and how to maintain models over time. She acknowledged that smaller teams won’t have such strict rules about which teams are responsible for different stages of a data product, but made it clear that releasing AI into the world is much more complicated than just training a model on a training data set. She provided examples from the aviation domain, where AI systems needs to be robust and highly accurate in order to be adopted.

June’s slide on the many stages of releasing Data Products.

Tensors are the new primitive for ML work

One of the themes of the conference was the advocacy for the use of tensors in ML work. Tensors are a natural extension to matrices, where instead of representing data in two dimensions, the data is represented in 3+ dimensions. Tamara Kolda mentioned that it’s use goes back to the early 90’s in the domain of chemistry, but it’s only recently that tensors have been widely adopted as a data structure for ML work. Anima Anadkumar also advocated for the use of tensors as primitives, and mentioned that adopting new primitives will require authoring new libraries.

Be ready to trade off accuracy for space and time constraints

Several of the talks at MLconf discussed the trade-offs involved in solving for optimal solutions. Sometimes optimal solutions are intractable and teams needed to identify novel ways of reducing the space or time complexities of the algorithms used. For example, Tamara Kolda talked about sampling approaches that could be used to solve for fitting a model without using the complete data set during each iteration, Anima Anadkumar talked about dropping ranks from a tensor representation for space savings, and Rushin Shah talked about the fastText algorithm that Facebook uses for accurate language understanding that is significantly faster than prior approaches.

Deep learning is interdisciplinary

One of the novelties of the conference was seeing how researchers outside of tech companies are using deep learning to create novel findings. There was a really inspiration talk by Ted Willke on how deep learning is being applied to the field of marine biology, and enabling scientists to track whale migrations with much better accuracy than prior approaches, and much less invasive approaches. There was also talks on applying deep learning to other domains including chemistry, medicine, and dating.

Deep learning is impacting more and more of our everyday lives

12% of emails sent on mobile are using gmail’s new auto-replay feature. This was just one example of many at the conference where deep learning is being used to impact more of our day to day activities. There was June’s talk about how GE is improving the safety of airline travel for any participants that flew to the event, there was Xavier Amatriain’s presentation on the impact of deep learning on diagnosis in medicine, and Rushin Shah’s talk about how FB posts about travel recommendations are being used to influence decisions.

Provide notebooks if you want to drive adoption

Some of the speakers today, including Doug Eck, talked about the earlier days of AI, where all of the code was written in C++ and nobody wanted to touch existing code bases. We now have much better tools available for democratizing ML, and notebook environments, such as Jupyter as useful for for providing examples of how to use ML frameworks. Anima Anadkumar talked about how Jupyter is being used to make ML libraries such as Gluon more approachable.

ML companies are hiring!

Most speakers at the event today mentioned that their companies are hiring for ML positions. MLconf has a listing of jobs, and Windfall Data is also hiring for a data scientist position focused on data governance.