Machine Learning

Past Presentations

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
Building Bots and Conversational AI

People are spending a lot of time on messaging and voice conversational mediums such as Facebook Messenger and Amazon Alexa, which have opened up for building bots. These bots are allowing services and businesses to connect with users on these conversational interfaces. Conversational bots...

Mitul Tiwari CTO @PassageAI
CI/CD for Machine Learning

Machine Learning is now widely used across our industry, yet we have very limited tooling when it comes to automating the ML model versioning, testing, and release. We will show how a CI/CD pipeline for ML can greatly improve both your productivity and the reliability of your software.

Sasha Rosenbaum Program Manager on the Azure DevOps Engineering Team @Microsoft
Automating Netflix ML Pipelines With Meson

In this talk we discuss the evolution of ML automation at Netflix and how that lead us to build Meson, an orchestration system used for many of the personalization/recommendation algorithms. We will talk about challenges we faced, and what we learned automating thousands of ML pipelines with Meson.

Davis Shepherd ML Management @Netflix
Eugen Cepoi Senior Software Engineer @Netflix
Machine Learning 101

Today’s world generates different kinds of data at unbelievably rapid rates. This has resulted in a shift away from traditional software development towards fields like Artificial Intelligence and Machine Learning. Many are saying that Machine Learning is changing the world - but what...

Grishma Jena Data Scientist @IBM


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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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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