AI & Machine Learning Summit

#AIMachineSummit

The adoption of AI and machine learning (ML) technologies has become mainstream at businesses hungry for greater automation and intelligence with innovative use cases spreading across industries. A strong data management foundation is essential to effectively scaling AI and ML programs to deliver repeatable business value. To equip you with the knowledge to succeed, we are bringing together the leading industry experts for a 2-day immersion into real-world deployments, strategies for overcoming common business and technical barriers and key technologies every organization should know about.

The AI & Machine Learning Summit is designed for chief information officers, chief data officers, data scientists, data engineers, enterprise architects, data analytics directors/managers, application developers and tech-savvy business leaders.

Access to AI & Machine Learning Summit is included when you register for an All Access or Full Two-Day Conference Pass or as a stand alone registration option. View all our registration options here.

 

Tuesday, May 17

Track AI: AI & Machine Learning Summit

Moderator:
Ed Dale, Emerging Technology Associate Director, EY

The adoption of AI and machine learning (ML) technologies has become mainstream at businesses hungry for greater automation and intelligence with innovative use cases spreading across industries. A strong data management foundation is essential to effectively scaling AI and ML programs to deliver repeatable business value. To equip you with the knowledge to succeed, we are bringing together the leading industry experts for a 2-day immersion into real-world deployments, strategies for overcoming common business and technical barriers and key technologies every organization should know about.

Designed for chief information officers, chief data officers, data scientists, data engineers, enterprise architects, data analytics directors/managers, application developers and tech-savvy business leaders.

 

AI101. How to Drive an AI Strategy

10:45 AM2022-05-172022-05-17

Tuesday, May 17: 10:45 a.m. - 11:45 a.m.

Technology without strategy is doomed to fail. Looking at MLOps and Robotic Processes Automation exemplifies the need for clarity and strategic thinking.

Our MLOps Strategy at Galeries Lafayette

Driving the AI strategy in one of the oldest French retailers is a real challenge in and of itself. Using AI to make educated guesses about which customers to target, what products to recommend, and where to display ads is even trickier. Aquarone explains how, as the company’s AI portfolio grew, a standardized canvas to go from a few dozen models to hundreds or even thousands of models became necessary. His team created a set of tools, guidelines, flows, and processes to drive this growth and make sure every prediction is understood, maintained, and used. He details the building blocks of the MLOps Platform and the milestones in transforming the organization to become AI-driven.

Speaker:

, Data Science Products & Innovations Manager, Groupe Galeries Lafayette

Strategies for Unlocking Enterprise Value

For too long, enterprises have lacked the capability to make use of unstructured data. While new technology is the obvious first tool that most organizations look to, savvy automation leaders know that the existing tech stack can be rife with opportunity to yield even more benefit. Wilde identifies ways that enterprises can leverage technology that integrates the existing tech stack to provide a richer, more comprehensive view of enterprise knowledge, while adding structure to their unstructured data. In turn, this strategy unlocks far greater value from existing automation solutions, such as Robotic Process Automation (RPA), and other business-critical technologies, illustrated through use cases in commercial real estate, financial services, insurance, and shared enterprise services.

Speaker:

, CEO, Indico Data

 

AI102. Machine Learning Best Practices Today

12:00 PM2022-05-172022-05-17

Tuesday, May 17: 12:00 p.m. - 12:45 p.m.

Machine learning has evolved considerably over the past few years, and best practices have changed in tandem with that evolution.

How Do I Know My Machine Learning Data Model Is Good?
A data model is the key output part of a machine learning (ML) process. It helps the prediction of new outcomes as new values are inserted into it. But how can we make certain that we have the right data model? Each ML technique is associated with metrics that evaluates the ML data model performance. This presentation uses Oracle Analytics Cloud (OAC) to answer the data model question and demonstrates the most commonly used ML techniques and algorithms, along with their corresponding data model evaluation metric.
Speaker:

, Director Analytics, Datavail

Finally, Realize Business ROI From ML: Unleash Your Event-Based Data

On average, it takes 7 to 18 months, to go from idea to ML model in production. But things are changing. Data platforms are maturing and success is getting within reach of many organizations. Come hear about some top-level trends in machine learning.

Speaker:

, VP of Product, Kaskada

 

AI103. Enhancing AI With Graphs

02:00 PM2022-05-172022-05-17

Tuesday, May 17: 2:00 p.m. - 2:45 p.m.

Graph technology has become a main driver of AI and machine learning advances within a wide variety of industries.

Driving Business Outcomes With Graph Database & AI

The pandemic accelerated the pace of digital transformation across all industries. Organizations are looking for ways to accelerate their analytics, AI, and machine learning projects to increase revenue, manage risks, and improve customer experience. Join us to learn about the three core capabilities necessary to drive the business outcomes: connecting internal and external datasets and pipelines with a distributed graph database, analyzing connected data to discover insights with advanced analytics, and learning from the connected data with in-database machine learning.

Speaker:

, VP, Product & Innovation, TigerGraph

 

AI104. ML Methodologies in the Real World

03:15 PM2022-05-172022-05-17

Tuesday, May 17: 3:15 p.m. - 4:00 p.m.

Theories about machine learning have their place, but applying methodologies to real-world issues give practitioners a leg up.

Application of Advanced Analytics in Anti-Money Laundering

Money laundering impacts society in a number of ways. Banks must adhere to regulatory guidelines to counter them. To combat money laundering, banks use data analytics to gain a complete understanding of transactions data accuracy, completeness, and timeliness from various sources within the bank. Maheshwari explains how extraction, transformation, and loading of data from various systems are critical. Machine learning methodologies are also critical to determine how these transactions (which are millions of dollars on a given day) can be analyzed for any money-laundering activity.

Speaker:

, VP, New York Community Bank

 

AI105. Data Privacy in the Digital Era

04:15 PM2022-05-172022-05-17

Tuesday, May 17: 4:15 p.m. - 5:00 p.m.

Addressing Data Privacy in AI & Machine Learning Projects

In this session, Hodeghatta addresses the challenges of protecting data while providing data for AI and machine learning projects and why data privacy is a concern and can be a hindrance for these types of projects.

Speaker:

, Professor, College of Professional Studies, Northeastern University

Trends in Trusted Data
The explosive growth of data and the value it creates calls on data professionals to level up their programs to build, demonstrate, and maintain trust. The days of fine print, pre-ticked boxes, and data hoarding are gone and strong collaboration from data, privacy, marketing and ethics teams is necessary to design trustworthy data-driven practices. Learn how to build trust in data practices.
Speaker:

, Lead Solutions Engineer, OneTrust

 

Reception in the Data Solutions Showcase

05:00 PM2022-05-172022-05-17

Tuesday, May 17: 5:00 p.m. - 6:00 p.m.

 

Wednesday, May 18

Track AI: AI & Machine Learning Summit

Moderator:
Heather Hedden, Data & Knowledge Engineer, Semantic Web Company, USA,

The adoption of AI and machine learning (ML) technologies has become mainstream at businesses hungry for greater automation and intelligence with innovative use cases spreading across industries. A strong data management foundation is essential to effectively scaling AI and ML programs to deliver repeatable business value. To equip you with the knowledge to succeed, we are bringing together the leading industry experts for a 2-day immersion into real-world deployments, strategies for overcoming common business and technical barriers and key technologies every organization should know about.

Designed for chief information officers, chief data officers, data scientists, data engineers, enterprise architects, data analytics directors/managers, application developers and tech-savvy business leaders.

 

AI201. Unlocking Your Data With AI & ML

10:45 AM2022-05-182022-05-18

Wednesday, May 18: 10:45 a.m. - 11:30 a.m.

Data within the enterprise is useless if it can’t be found and used. AI and ML provide some pathways to data discovery that give companies competitive advantage.

Accelerating Your DevOps Journey With ML

Machine learning is both a buzzword and the Holy Grail, depending on how you use it. Enterprise cloud companies use machine learning to accelerate or supercharge their data journey—it helps them work at a faster pace, with more efficiency and greater accuracy. Once AI is fully in use, teams need to be able to answer this question: How do we know if this is working correctly? In order to do this, teams must extend their existing observability approaches to cover their AI and ML capabilities. Today, there’s a big gap there, and that creates risk as organizations can’t manage those assets properly. This session takes a deep dive into how companies utilize machine learning to take every competitive advantage to advance their ability to use all the data at their disposal.

Speaker:

, Senior Director, Product Management, Sumo Logic

 

AI202. Building a Streaming ML Platform

11:45 AM2022-05-182022-05-18

Wednesday, May 18: 11:45 a.m. - 12:30 p.m.

A single view of the customer, powered by AI and ML, can help identify fraud, personalize recommendations, and contribute to outstanding customer care encounters.

Customer 360 in a Box

With the advent of omnichannel, leveraging customer data has become paramount. In a behemoth such as Walmart, each customer’s identity exists as a silo rather than a single customer from the company perspective. Handling customer data for privacy, regulations, and deprecation becomes challenging with every new ID introduced in the system. A streaming ML platform that seamlessly combines data belonging to the same customer on a single box and runs ML models, which use this data as features, leads to a single view of a customer. Brar discusses the platform (built on Kafka ecosystem) and its important aspects.

Speaker:

, Staff Engineer, Walmart Global Tech

 

AI203. Rethinking Architectures for AI

02:00 PM2022-05-182022-05-18

Wednesday, May 18: 2:00 p.m. - 2:45 p.m.

As the world becomes increasingly data-driven, AI/ML algorithms are being incorporated in most business applications.

The Era of Distributed AI Architectures

Historically, data in AI architectures was moved to a central location to perform both model training and inference. This centralized approach is becoming untenable due to cost, performance, and privacy reasons. In this talk, Kaladhar shares his thoughts on next-generation distributed AI architectures and presents the concepts of “AI Marketplaces” and “Federated AI” to demonstrate how these concepts are an integral part of distributed AI architectures.

Speaker:

, Senior Fellow, Technology & Architecture, Office of the CTO, Equinix

 

AI204. Operationalizing AI: Challenges & Opportunities

03:00 PM2022-05-182022-05-18

Wednesday, May 18: 3:00 p.m. - 3:45 p.m.

Putting AI to work to improve healthcare requires having technology and infrastructure coupled with the right people.

Insights for Unlocking the Potential of AI in Healthcare

Healthcare innovators are innovators are prioritizing AI and operationalizing it for better performance, better outcomes, and better patient experience. But getting into predictive and prescriptive analytics takes many of them out of their comfort zones. Yet with every opportunity for improvement, there is a risk that organizations won’t have what it takes to successfully develop, implement, or operationalize AI. Having the technology and infrastructure for AI is not enough. Having the right people—with the technical capability and healthcare expertise—is enormously important. In this talk, Mehrotra and Fernando provide insight into strategies and plans to attract and retain the talent needed to unlock the potential of AI.

Speakers:

, SVP, Analytics, Optum Advisory Services

, SVP, Optum

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