Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Schedule: Platforms and infrastructure sessions

9:00am-12:30pm Wednesday, September 5, 2018
Mary Wahl (Microsoft), Banibrata De (Microsoft)
High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN—both on single VMs and at scale on GPU clusters. Read more.
9:00am-12:30pm Wednesday, September 5, 2018
Implementing AI, Interacting with AI
Location: Continental 4
Daniel Whitenack (Pachyderm)
Average rating: ****.
(4.75, 4 ratings)
Kubernetes—the container orchestration engine used by all of the top technology companies—was built from the ground up to run and manage highly distributed workloads on huge clusters. Thus, it provides a solid foundation for model development. Daniel Whitenack demonstrates how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes. Read more.
11:05am-11:45am Thursday, September 6, 2018
Sponsored
Location: Yosemite A
Jayanti Murty (Digitate)
Average rating: *****
(5.00, 1 rating)
Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms? Do you struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Jayanti Murty explains how and shares real-world examples of companies that have reduced operational risks and outages and technology and labor costs. Read more.
11:55am-12:35pm Thursday, September 6, 2018
Implementing AI
Location: Imperial A
Magnus Hyttsten (Google), Priya Gupta (Google)
Average rating: ***..
(3.00, 1 rating)
Magnus Hyttsten and Priya Gupta demonstrate how to perform distributed TensorFlow training using the Keras high-level APIs. They walk you through TensorFlow's distributed architecture, how to set up a distributed cluster using Kubeflow and Kubernetes, and how to distribute models created in Keras. Read more.
11:55am-12:35pm Thursday, September 6, 2018
Sponsored
Location: Franciscan BCD
Liran Zvibel (WekaIO)
Artificial intelligence requires a low-latency, high-throughput storage system to keep the compute layer fully saturated with data. Liran Zvibel demonstrates why NVMe-optimized, distributed filesystems are ideal storage solutions to support AI applications and introduces a next-gen massively parallel shared filesystem that's NAND flash and NVMe optimized, built to solve the I/O starvation problem. Read more.
1:45pm-2:25pm Thursday, September 6, 2018
Implementing AI, Models and Methods
Location: Imperial A
Evan Sparks (Determined AI), Ameet Talwalkar (Carnegie Mellon University | Determined AI)
Average rating: *****
(5.00, 1 rating)
In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them. Read more.
4:00pm-4:40pm Thursday, September 6, 2018
Rachael Rekart (Autodesk )
Average rating: ***..
(3.00, 2 ratings)
Rachael Rekart offers an overview of Autodesk Virtual Agent (AVA), which has revolutionized the way Autodesk approaches customer service. Customers chat with AVA as they would a human, in natural language, and AVA processes transactions quickly, returns accurate answers, or gathers information to pass to a human counterpart to resolve the query. Read more.
4:00pm-4:40pm Thursday, September 6, 2018
Implementing AI
Location: Franciscan BCD
David Martinez (MIT Lincoln Laboratory)
David Martinez discusses an AI canonical architecture suitable for a number of different classes of applications and shares examples focused on cybersecurity to illustrate an application area that benefits from an end-to-end AI architecture. Read more.
4:50pm-5:30pm Thursday, September 6, 2018
Implementing AI
Location: Yosemite BC
Ramesh Sridharan (Captricity)
Captricity has deployed a machine learning pipeline that can read handwriting at human-level accuracy. Ramesh Sridharan discusses the big ideas the company learned building and deploying this system, using data to identify specific problems to solve using AI and to evaluate and validate the algorithm itself and the overall system once deployed. Read more.
11:05am-11:45am Friday, September 7, 2018
Implementing AI
Location: Yosemite BC
Brian Dalessandro (SparkBeyond), Chris Smith (Zocdoc)
With the help of better software, cloud infrastructure, and pretrained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. Brian Dalessandro and Chris Smith share their experience and lessons learned while building a computer vision and OCR app for reading and classifying insurance cards. Read more.
1:45pm-2:25pm Friday, September 7, 2018
Location: Yosemite BC
Joel Hestness (Baidu)
Deep learning (DL) creates impactful advances following a virtuous recipe: a model architecture search, creating large training datasets, and scaling computation. Joel Hestness discusses research done by Baidu Research's Silicon Valley AI Lab on new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing. Read more.
2:35pm-3:15pm Friday, September 7, 2018
Sharad Gupta (Blue Shield of California)
Average rating: *****
(5.00, 1 rating)
AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots. Read more.
2:35pm-3:15pm Friday, September 7, 2018
Implementing AI, Models and Methods
Location: Yosemite BC
Abhishek Tayal (Twitter)
Average rating: *****
(5.00, 1 rating)
Abhishek Tayal offers insight into how Twitter's ML platform team, Cortex, is developing models, related tooling, and infrastructure with the objective of making entity embeddings a first-class citizen within Twitter's ML platform. Abhishek also shares success stories on how developing such an ecosystem increases efficiency and productivity and leads to better outcomes across product ML teams. Read more.
4:00pm-4:40pm Friday, September 7, 2018
Implementing AI, Interacting with AI
Location: Continental 1-3
Labhesh Patel (Jumio)
Labhesh Patel explains how deep learning is informing Jumio's computer vision through smarter data extraction, fraud detection, and risk scoring and how Jumio is leveraging massive datasets and human review to dramatically improve the accuracy of its machine learning algorithms to detect bogus IDs and streamline the verification process of legitimate documents. Read more.
4:50pm-5:30pm Friday, September 7, 2018
Mayank Kejriwal (USC Information Sciences Institute)
Average rating: ****.
(4.00, 2 ratings)
Human trafficking is a form of modern-day slavery. Online sex advertisement activity on portals like Backpage provide important clues that, if harnessed and analyzed at scale, can help resource-strapped law enforcement crack down on trafficking activity. Mayank Kejriwal details an AI architecture called DIG that law enforcement have used (and are using) to combat sex trafficking. Read more.