Sponsored Sessions: Artificial Intelligence & Machine Learning Conference: O'Reilly AI

9:25am–9:35am Wednesday, September 11, 2019

Location: Hall 2

Unlocking the value of your data (sponsored by IBM Watson)

Dinesh Nirmal (IBM)

Average rating:

(4.20, 5 ratings)

Dinesh Nirmal examines how, with a unified, prescriptive information architecture, organizations can successfully unlock the value of their data for AI as well as trust and control the business impact and risks of AI while coexisting in a multicloud world. Read more.

9:50am–9:55am Wednesday, September 11, 2019

Location: Hall 2

The moral responsibility of AI builders (sponsored by Dataiku)

Triveni Gandhi (Dataiku)

Average rating:

(3.25, 4 ratings)

With the adoption of AI in the enterprise accelerating, its impacts—both positive and negative—are rapidly increasing. Triveni Gandhi explores why the builders of these new AI capabilities all bear some moral responsibility for ensuring that their products create maximum benefit and minimal harm. Read more.

10:10am–10:15am Wednesday, September 11, 2019

Location: Hall 2

AI for ophthalmology: Doing what doctors can’t (sponsored by Dell Technologies)

Daniel Russakoff (Voxeleron)

Average rating:

(3.50, 4 ratings)

The emphasis in AI is on replicating human performance. Examples abound: ImageNet, self-driving cars, etc. It’s the same in medicine. Daniel Russakoff explains how Voxeleron LLC is working on what’s next—AI algorithms that do things that humans can’t, such as the prediction of age-related macular degeneration (AMD) progression, critical to successful treatment of this leading cause of vision loss. Read more.

11:05am–11:45am Wednesday, September 11, 2019

Location: 230 B

Blending AI disciplines and human experts to build smart assistants of the future (sponsored by Intuit)

Bharath Kadaba (Intuit)

Average rating:

(4.40, 5 ratings)

To unleash the full potential of AI, Intuit envisions a future that melds the best capabilities of machines and humans to deliver personalized customer experiences, all on one secure platform. Bharath Kadaba examines how Intuit combines rules-based knowledge engineering with data-driven machine learning and natural language processing to build the human-expert-in-the-loop AI systems of the future. Read more.

11:05am–11:45am Wednesday, September 11, 2019

Location: 231

Operationalize AI at scale: From drift detection to monitoring the business impact of AI (sponsored by IBM Watson)

Manish Bhide (IBM Watson), Rohan Vaidyanathan (IBM Watson)

Average rating:

(3.40, 5 ratings)

With the potential to transform businesses, AI has become a strategic imperative for most enterprises. A lot of investment is toward machine learning and deep learning models to support business applications. However, as Manish Bhide and Rohan Vaidyanathan explain, these models bring about risks and uncertainties that are difficult to manage. Read more.

11:05am–11:45am Wednesday, September 11, 2019

Location: Santa Clara Room (Hilton)

The race to 10,000 data scientists deploying 1,000,000 models (sponsored by Dataiku)

Kurt Muehmel (Dataiku)

We're rapidly closing in on a future where large companies across different sectors will be enriching every business process and decision with AI and gaining a sustained competitive edge as a result. Join Kurt Muehmel on a forward-looking exploration of companies that are already well on their way toward this target. He details Dataiku's vision of the journey ahead. Read more.

11:55am–12:35pm Wednesday, September 11, 2019

Location: 231

From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies)

Ramesh Radhakrishnan (Dell Technologies), John Zedlewski (NVIDIA)

Average rating:

(5.00, 1 rating)

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs. Read more.

11:55am–12:35pm Wednesday, September 11, 2019

Location: Santa Clara Room (Hilton)

Making reinforcement learning practical for real-world developers (sponsored by Amazon Web Services)

Sunil Mallya (Amazon Web Services)

Average rating:

(4.67, 3 ratings)

Sunil Mallya walks you through how to build complex ML-enabled products using reinforcement learning (RL), explores hardware design challenges and trade-offs, and details real-life examples of how any developer can up level their RL skills through autonomous driving. Read more.

1:45pm–2:25pm Wednesday, September 11, 2019

Location: 231

Getting through the ground truth grind (sponsored by iMerit)

Sina Bari (iMerit)

Sina Bari explores how to overcome obstacles to creating high-quality ground truth data for ML applications. Read more.

1:45pm–2:25pm Wednesday, September 11, 2019

Location: Santa Clara Room (Hilton)

Build, train, and deploy predictive maintenance models at industrial scale (sponsored by Amazon Web Services)

Sunil Mallya (Amazon Web Services)

Average rating:

(5.00, 1 rating)

Sunil Mallya explores how to use data from equipment to build, train, and deploy predictive models. You'll dive deep into the architecture, deployment guide, and development resources for using the turbofan degradation simulation dataset to train the model to recognize potential equipment failures. Read more.

2:35pm–3:15pm Wednesday, September 11, 2019

Location: 231

Unlock your data's value with AI (sponsored by HPE)

Pankaj Goyal (Hewlett Packard Enterprise), Nanda Vijaydev (Hewlett Packard Enterprise)

Average rating:

(5.00, 1 rating)

Join Pankaj Goyal and Nanda Vijaydev to learn how HPE put AI into action and helps enterprises unlock the value of their data with a proven, practical approach to AI. Read more.

2:35pm–3:15pm Wednesday, September 11, 2019

Location: Santa Clara Room (Hilton)

The holy grail of data science: Rapid model development and deployment (sponsored by Zepl)

Moon soo Lee (Zepl | Apache Zeppelin), Louis Huard (Zepl)

Average rating:

(5.00, 3 ratings)

A key step in the data science workflow is rapid model development; however, gaps still exist. Teams are moving from siloed to sharing and reusing models, code, and results. There are also in challenges deploying models into production using tools like Kubeflow and TensorFlow. Moon Soo Lee and Louis Huard examine how leading companies solve these issues, and how you can improve your workflow. Read more.

4:00pm–4:40pm Wednesday, September 11, 2019

Location: 231

Talent for AI transformation: Building a strong AI team for the future (sponsored by TalentSeer)

Margaret Laffan (TalentSeer | BoomingStar Ventures)

With the rapid advancement of AI technology and commercial breakthroughs, building a strong AI team becomes increasingly critical for business success in the high-tech era. Margaret Laffan helps tech and talent leaders to better understand the AI talent market and explores best practices for building, nurturing, and retaining the right team to accelerate their business growth. Read more.

4:00pm–4:40pm Wednesday, September 11, 2019

Location: Santa Clara Room (Hilton)

Human-centered machine learning (sponsored by H2O.ai)

Navdeep Gill (H2O.ai)

Average rating:

(5.00, 1 rating)

Navdeep Gill takes a deep dive into how to combine innovations from several subdisciplines of machine learning research to train understandable, fair, trustable, and accurate predictive modeling systems. Read more.

4:50pm–5:30pm Wednesday, September 11, 2019

Location: 231

The challenges and opportunities of augmented intelligence at scale (sponsored by Jumio)

Labhesh Patel (Jumio)

Average rating:

(4.00, 1 rating)

Labhesh Patel explores how deep learning informs computer vision through smarter data extraction, fraud detection, and risk scoring. Labhesh details what it takes to put AI in production and how a machine learning infrastructure needs to be fundamentally thought out to allow for better human-in-the-loop AI workflows. Read more.

9:20am–9:30am Thursday, September 12, 2019

Location: Hall 2

Safe and smarter driving, powered by AI (sponsored by Amazon Web Services)

Lei Pan (Nauto)

Average rating:

(4.00, 3 ratings)

Lei Pan examines how Nauto uses Amazon SageMaker and other AWS services, including Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to continually evolve smarter data for driver behavior. Read more.

11:05am–11:45am Thursday, September 12, 2019

Location: Santa Clara Room (Hilton)

Framing business problems as machine learning (ML) problems (sponsored by Amazon Web Services)

Carlos Escapa (Amazon Web Services)

Average rating:

(4.75, 4 ratings)

Carlos Escapa takes a deep dive into how to identify use cases for ML, acquire cutting-edge best practices to frame problems in a way that key stakeholders and senior management can understand and support, and set the stage for delivering successful ML-based solutions for your business. Read more.

1:45pm–2:25pm Thursday, September 12, 2019

Location: Santa Clara Room (Hilton)

A practical guide to responsible AI (sponsored by PwC)

Anand Rao (PwC)

Anand Rao provides an overview from the practitioner’s perspective on addressing ethics within businesses. Anand explores PwC’s responsible AI toolkit, which enables businesses to identify and contextualize relevant ethical AI principles and provides tools for evaluating interpretability of systems. You'll see example applications that illustrate model interpretability. Read more.

2:35pm–3:15pm Thursday, September 12, 2019

Location: Santa Clara Room (Hilton)

Automatic machine learning for the enterprise with H2O.ai Driverless AI (sponsored by H2O.ai)

Arno Candel (H2O.ai)

Driverless AI is H2O.ai’s latest flagship product for automatic machine learning for the enterprise. Arno Candel outlines Driverless AI, explores customer use cases, and performs a live demo with custom recipes to solve a specific machine learning problem. Read more.