Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Platforms and infrastructure sessions

11:05am11:45am Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Mathew Salvaris (Microsoft), Fidan Boylu Uz (Microsoft)
Average rating: ***..
(3.33, 3 ratings)
Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster. Read more.
1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Average rating: ****.
(4.75, 8 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency. Read more.
1:00pm1:40pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Cibele Halasz (Twitter), Satanjeev Banerjee (Twitter)
Average rating: *****
(5.00, 1 rating)
Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient. Read more.
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
YU DONG (Facebook)
Average rating: ***..
(3.50, 2 ratings)
Yu Dong offers an overview of the why, what, and how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions. Read more.
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
Jeremy Lewi (Google), Hamel Husain (GitHub)
Average rating: ****.
(4.00, 1 rating)
Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products. Read more.
1:50pm2:30pm Wednesday, April 17, 2019
Models and Methods
Location: Grand Ballroom West
vinay rao (RocketML Inc), Santi Adavani (RocketML)
Average rating: *****
(5.00, 2 ratings)
The AI industry needs new software architectures for distributed systems to solve critical problems. Vinay Rao and Santi Adavani explain why software architectures will lead the next generation of machine learning approaches and how RocketML has built logistic regression models on the KDD12 dataset with ~150 million samples on an eight-Intel Xeon-node cluster in under a minute. Read more.
2:40pm3:20pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
Average rating: ****.
(4.00, 3 ratings)
Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools. Read more.
4:05pm4:45pm Wednesday, April 17, 2019
Sarah Aerni (Salesforce Einstein)
Average rating: *****
(5.00, 1 rating)
How does Salesforce make data science an Agile partner to over 100,000 customers? Sarah Aerni shares the nuts and bolts of the platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology. Read more.
4:55pm5:35pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Scott Clark (SigOpt), Matt Greenwood (Two Sigma Investments)
Average rating: **...
(2.00, 1 rating)
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Read more.
4:55pm5:35pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave. Read more.
9:35am9:50am Thursday, April 18, 2019
Implementing AI
Location: Grand Ballroom West
Kim Hazelwood (Facebook)
Average rating: ****.
(4.60, 5 ratings)
Applied Machine Learning at Facebook Read more.
11:05am11:45am Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Diego Oppenheimer (Algorithmia), Brendan Collins (Algorithmia)
Average rating: ****.
(4.00, 1 rating)
Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science. Read more.
1:00pm1:40pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Evan Sparks (Determined AI)
Average rating: *****
(5.00, 1 rating)
Building deep learning applications is hard. Building them repeatably is harder. Maintaining high computational performance during a repeatable deep learning development process is borderline impossible. Evan Sparks describes the key pitfalls associated with fast, repeatable model development and details what practitioners can do to avoid them and maintain a supercharged AI development workflow. Read more.