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Machine Learning

Past Presentations

AI & Security: Lessons and Challenges

In this talk, I will first present recent results in the area of secure deep learning, in particular, adversarial deep learning---how deep learning systems could be easily fooled and what we need to do to address the issues. I will also talk about how AI and deep learning can help enable new...

Prof. Dawn Song Professor @UCBerkeley, Researching Deep Learning & Security
Enabling ML with Apache Beam and Event Mesh

We'll talk about how you can enable cloud-based ML, fueled by events fired from across your enterprise (IoT, mobile devices, legacy apps, private cloud, public cloud), in real-time. We’ll introduce the concept of an event mesh (think service mesh for asynchronous, event-based interactions). And...

Ken Overton Sales Engineer @solacedotcom
Machine Learning on Mobile and Edge Devices With TensorFlow Lite

Machine learning enables some incredible applications, from human-centric user interfaces to generative art. But the traditional machine learning architecture is server-based, with data being sent from users' devices to the cloud, and users are rightly concerned about privacy, safety, and...

Daniel Situnayake Developer Advocate for TensorFlow Lite @Google and Co-Author of TinyML
Continuous Optimization of Microservices Using ML

Performance tuning of microservices in the data center is hard because of the multitude of available knobs, the large number of microservices and variation in work loads, all of which combine to make the problem combinatorially intractable. Maintaining optimal performance in the face of...

Ramki Ramakrishna Staff Software Engineer @Twitter
Self-Driving Cars as Edge Computing Devices

Every Uber self-driving car runs advanced software, often for hours on end, which requires a powerful compute stack. In this talk, we’ll explain the architecture of Uber ATG’s self-driving cars and have a look at how the software is developed, tested, and deployed.

Matt Ranney Sr. Staff Engineer @UberATG


Justin Basilico Machine Learning Research/Engineering Director @Netflix

Artwork Personalization @Netflix

What work do you do at Netflix?

I lead one of the Machine Learning and Recommendation teams at Netflix. We're responsible for the end-to-end machine learning that decides what shows up on the Netflix homepage across all our different experiences. When you log into Netflix, my team is responsible for what rows of TV shows and movies you see on the homepage. We select...

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Sasha Rosenbaum Program Manager on the Azure DevOps Engineering Team @Microsoft

CI/CD for Machine Learning

What do you want people to leave the talk with?

If I had to summarize it in one line it would be any CI/CD pipeline is better than none. If you're going to automate major key pieces of this process will make your life a lot easier, simplify it and add speed to your deployments.

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Pamela Gay Senior Scientist @planetarysci (Planetary Science Institute)

When Machine Learning Can't Replace the Human

What is the work that you're doing today?

I'm working to find ways to help us use technology to map other worlds. At this particular moment in time, computer vision isn't quite ready to mark the hazards our spacecraft face day today. I'm trying to figure out how to integrate in humans in my algorithm and do it in a way that's ethical and can, where possible,...

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Sarah Aerni Senior Manager, Data Science @Salesforce

Models in Minutes not Months: AI as Microservices

I cannot go to any Data Conference and not hear about the Einstein Platform. Why?

Salesforce is democratizing AI with Einstein. Any company and any business user should be able to use AI, regardless of size.

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Ville Tuulos Machine Learning Infrastructure Engineer @Netflix

Human-Centric Machine Learning Infrastructure @Netflix

Can you give an example of some of the questions you get from data scientists when you are trying to deploy models?

When it comes to common questions, as boring as it may sound, my experience is that machine learning infrastructure is much more about data than science. Most questions we get are related to data: how do I find the data I need, how do I set up the data pipeline, how do I handle the somewhat non-trivial amounts of data in python and R,...

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