Awesome sessions to inspire and accelerate your data science career
This year, Rev focuses on how enterprises can unleash breakthrough innovations through easier access to infrastructure, better collaboration across teams, and faster model learning and iteration.
Our agenda is split into three tracks:
- Data Science Management: Best practices to manage and scale data science teams, processes, and culture.
- The Future of Model-Driven Businesses: Discussing the impact of being model driven in your organization.
- MLOps Applied: Tips, tricks, helpful hints, and best practices to accelerate your day to day data science work.
Wednesday, May 4th
Domino customers are invited for in-depth training sessions on Wednesday, May 4 before the full conference kicks off the next day. All guests are invited to the welcome reception after trainings.
11:00am - 2:00pm: Introduction to Domino for Practitioners
Learn about the end-to-end data science lifecycle within Domino. See how to connect to data sources and integrate with Git repositories, work with large data, and develop your model in an interactive environment with easy access to the tools you prefer. Then, deploy the model using a model API, set up automated monitoring, create scheduled reports, and host an interactive dashboard. Lastly, learn how to work with Domino programmatically.
2:30pm - 4:45pm: Domino Administration Certification
Learn how to effectively manage both the Kubernetes cluster that runs Domino and the Domino platform itself to enable your team to do their best work. This course covers optimizing Domino to balance cost savings while ensuring optimal performance for your data scientists. You'll also pick up tips on troubleshooting, configuration, and managing resources.
2:30pm - 4:45pm: Advanced Data Science in Domino
The Advanced Data Science course covers advanced techniques using the Domino platform. We begin with how to add distributed computing environments to your workflow and how to execute deep learning on the Domino platform. In addition, we cover optimizing GPU usage, Best Practices for computing environments (Docker Best Practices), and advanced Git integration.
4:00pm – 7:00pm: Registration
4:45pm – 6:00pm: Welcome Reception
Thursday, May 5
After kicking off with inspiring keynote speakers, customize your Rev experience with three session tracks: MLOps Applied, Data Science Management, and The Future of Model-Driven Businesses.
8:00am - 4:45pm: Registration
8:00am - 9:00am: Breakfast
9:00am - 11:00am: General Session
- Mike Hayes, Chief Digital Transformation Officer, VMWare and former Commanding Officer for SEAL Team TWO
- Nick Elprin, CEO & Co-founder, Domino Data Lab
- Linda Avery, Chief Data & Analytics Officer, Verizon
11:00am - 11:15am: Transitional Break
11:15am - 12:00pm: Breakouts
|
Breakout 1 Skills for the Future: Creating an Analytics-Driven Workforce |
Breakout 2 Ways to Win the Data Science Talent Race |
Breakout 3 Bayer's Data Science Journey: Driving Transformation By Evolving People, Processes, and Technology |
Breakout 4 Cross-Functional Collaboration: Orchestrating IT, Data Science & Business Partner Engagement |
Breakout 5 How to Use New Jupyter Tools in Production Environments |
12:00pm - 1:30pm: Lunch
12:30pm - 1:15pm: Breakout
|
Breakout 4 Lessons Learned for AI Data Management at Scale (Presented by NetApp) |
1:30pm - 2:15pm: Breakouts
|
Breakout 1 Data Scientists and C-Suiters: Is Your AI Breaking the Law? |
Breakout 2 Scaling to Multi-Node AI: The Future of Distributed Computing in Data Science? |
Breakout 3 The Data Collaboration Stack: From DataOps to MLOps |
Breakout 4 Healthcare Delivery in a Model-Driven World |
Breakout 5 Solving Enterprise Data Challenges with Apache Arrow: How to Mitigate Risks and Boost Time-to-Value |
2:15pm - 2:30pm: Transitional Break
2:30pm - 3:15pm: Breakouts
|
Breakout 1 Financial Time-Series Modeling: A Data-Driven Machine-Learning Technique |
Breakout 2 Transforming Drug Discovery with MLOps |
Breakout 3 How 'Explainable' Is Your AI? A Real-World Evaluation |
Breakout 4 Navigating the Ethics Minefield: Building Legal, Ethical AIs |
Breakout 5 Buy AI Services or Roll Your Own? Decision Criteria, Hybrid Strategies, and Execution Tips |
3:15pm - 3:30pm: Transitional Break
3:30pm - 4:45pm: General Session
- Dr. Jennifer Doudna, Nobel Prize Winner and Professor, University of California at Berkeley
4:45pm - 7:00pm: Rev 3 Reconnect
Friday, May 6
Start the morning with more inspiring keynotes, continue the learning on your chosen track sessions, and wrap up the Rev experience with a must-see presentation from Atomic Habits author James Clear.
8:00am - 3:30pm: Registration
8:00am - 9:00am: Breakfast
9:00am - 10:45am: General Session
- Cassie Kozyrkov, Chief Decision Scientist, Google
- Cass Sunstein, NYT Best-selling Author and Professor, Harvard Law School
- Jim Swanson, EVP & Enterprise Chief Information Officer, Johnson & Johnson
10:45am - 11:15am: Transitional Break
11:15am - 12:00pm: Breakouts
|
Breakout 1 It's in the Data! How Improving ML Datasets Is The Best Way To Improve Model Performance |
Breakout 2 Where is the Next Frontier in Model-Driven Businesses? |
Breakout 3 How New Types of Data are Unlocking Business Opportunities |
Breakout 4 How Data Science is Changing the Defense Industry |
Breakout 5 "Scaling AI Enabled Digital Transformation in Healthcare & Life Sciences Industry " |
12:00pm - 1:30pm: Lunch
12:30pm - 1:15pm: Breakout
|
Breakout 2 Cure Quest: The Impact of Data Science Models on Next-gen Life Science Product Development |
Breakout 3 Domino Data Lab Live Demo |
Breakout 4 The 7 Things You Need to Get Right When Operationalizing AI (Presented by NVIDIA) |
1:30pm - 2:15pm: Breakouts
|
Breakout 1 Is Synthetic Data the Key to Better Enterprise ML & Software Testing? |
Breakout 2 How Data Science is Reshaping the Insurance Industry |
Breakout 3 When Good Models Go Bad: How to Monitor Effectively for Better Real-World Performance |
Breakout 4 Why Data Science Isn't Rocket Science--It's Harder! Learnings from Lockheed Martin |
Breakout 5 The Untapped Potential of Vector Embeddings to Power Great ML Products |
2:15pm - 2:30pm: Transitional Break
2:30pm - 3:30pm: General Session
- James Clear, NYT Best-selling Author of "Atomic Habits"
Filter Sessions by Track
Wednesday @ 11:00 AM
Introduction to Domino for Practitioners
Introduction to Domino for Practitioners
Domino Training
Wednesday, May 4, 11:00 AM – 12:15 PM (75 min.)
Learn about the end-to-end data science lifecycle within Domino. See how to connect to data sources and integrate with Git repositories, work with large data, and develop your model in an interactive environment with easy access to the tools you prefer. Then, deploy the model using a model API, set up automated monitoring, create scheduled reports, and host an interactive dashboard. Lastly, learn how to work with Domino programmatically.
Wednesday @ 02:30 PM
Domino Administration Certification
Domino Administration Certification
Domino Training
Wednesday, May 4, 2:30 PM – 4:45 PM (135 min.)
Learn how to effectively manage both the Kubernetes cluster that runs Domino and the Domino platform itself to enable your team to do their best work. This course covers optimizing Domino to balance cost savings while ensuring optimal performance for your data scientists. You'll also pick up tips on troubleshooting, configuration, and managing resources.
Wednesday @ 02:30 PM
Advanced Data Science in Domino
Advanced Data Science in Domino
Domino Training
Wednesday, May 4, 2:30 PM – 4:45 PM (135 min.)
This course will cover a variety of advanced topics in Domino, including how to add distributed computing into your workflow through on-demand Spark, Ray, and Dask clusters, optimize GPU usage, get the most out of compute environments by learning Docker best practices, enable better collaboration through organizations and advanced Git integration, and establish publishing and deployment pipelines.
Thursday @ 11:15 AM
Cross-functional Collaboration to Make Data Science Sing: Connecting the Dots with your IT, Data Science & Business Partners
Cross-functional Collaboration to Make Data Science Sing: Connecting the Dots with your IT, Data Science & Business Partners
Data Science Management
Thursday, May 5, 11:15 AM – 12:00 PM (45 min.)
Data Science is not a spectator sport. For a project to be successful, multiple teams and stakeholders must remain fully engaged throughout its life, finding ways to collaborate and trust one another. Otherwise, a project could be severely delayed or might even fail entirely—a possibility that grips many burgeoning data science teams. So how do you nurture these key relationships? How can you avoid the pitfalls of such a complex operation and create a model development life cycle that functions smoothly and effectively? In this talk, we share the successful strategies we use at New York Life to facilitate this process. We explain how to help Business Partners shift their mindset from spectator to participant, coming together to create business value. We also introduce the concept of the life cycle funnel—a way to think about how many ideas flow through each development stage until a single, final model is deployed. Finally, we end with the most important lesson of all: how the real life of a model begins not with its inception, but with its first release, and how the cycle starts all over again.
About Deb Grace
Deb Grace is the VP of Operations for New York Life’s Center for Data Science & Artificial Intelligence (CDSAI). Deb joined CDSAI in January of 2017 to manage Operations for the newly established Data Science capability. The team has grown from 5 to 55 people over the past five years and offers Data Science services to many areas of the company, including Underwriting, Actuarial, Finance, Marketing, Agency Distribution, Service, Product and Compliance. Deb began her career with New York Life in 2007 as a Marketing Operations Manager for New York Life-Tampa Direct. In 2012 she moved to New York Life’s Home Office to manage Operations for the Corporate Marketing team. Prior to joining New York Life, Deb spent 10 years as the owner of a lead generation direct marketing firm that served large insurance companies. She received her MBA from Temple University in Philadelphia and her bachelor’s degree in Business Administration from the University of Miami, Florida.
Thursday @ 11:15 AM
Skills for the future: Creating an Analytics-driven Workforce
Skills for the future: Creating an Analytics-driven Workforce
Data Science Management
Thursday, May 5, 11:15 AM – 12:00 PM (45 min.)
Creating an analytics driven workforce is a must for a model driven business. Learn what approaches Allstate has taken to upskill employees throughout the organization, including business leaders, to create an analytic driven workforce.
About Meg Walters
Meg Walters leads Allstate's central data science unit's Analytics Center of Excellence which is positioned at the center of Allstate’s analytics ecosystem and builds, deploys, and integrates advanced analytics across Allstate. Meg has a PhD in Mathematics from the University of Rochester.
Thursday @ 11:15 AM
Bayer's Data Science Journey - Driving Transformation by Evolving People, Processes, and Technology
Bayer's Data Science Journey - Driving Transformation by Evolving People, Processes, and Technology
The Future of Model-Driven Business
Thursday, May 5, 11:15 AM – 12:00 PM (45 min.)
Learn how Bayer’s 15-year data science journey has transformed the business. Through talent development, the implementation of tools to increase model velocity, and deep integration into the business, Bayer was successfully positioned to navigate the changes in the post-Covid era.
About Patricio Salvatore La Rosa
Patricio Salvatore La Rosa is the Head of Decision Sciences for Bayer Crop Science. His emphasis is in Statistical Signal Processing and Statistical & Machine Learning and their applications to analysis, modeling, decision automation, and decision orchestration in biological, biomedical, and agronomical systems. His areas of recent contributions include the design of scalable machine learning workflows to perform global environmental modeling, Crop Performance Interaction Analyses between genetic, environmental factors, and management practices, Prescriptive Analytics to support Precision Agriculture decisions, Experimental design to guide genome and microbiome research, Predictive Bio-signal analysis and bio-physical modeling of whole organ responses to support an early medical diagnosis.
Thursday @ 11:15 AM
Winning the Data Science Talent Race
Winning the Data Science Talent Race
Data Science Management
Thursday, May 5, 11:15 AM – 12:00 PM (45 min.)
The market for data science professionals continues to be red hot. The switch to remote work has both opened up the candidate pool and made the market more competitive as large organizations are reaching into new markets for candidates. It also means your existing team is getting multiple offiers. Learn what approaches are working to attract and retain talent and trends to be aware of.
About Meghan Anzelc
As Head of Data and Analytics, a global and firm-wide role, Dr. Meghan Anzelc is responsible for building and implementing a strategy and roadmap to advance the data and analytics capabilities at Spencer Stuart. She works with colleagues across the firm to understand their challenges and the potential opportunities for data and analytics to have a positive impact on the organization and on Spencer Stuart’s products and services for the firm’s clients. Spencer Stuart is one of the world’s leading executive search and leadership advisory firms. With 70 offices in 31 countries, the firm is considered to be the advisor of choice among top companies seeking guidance and counsel on senior leadership needs, and has unrivaled access to leading executives around the world. Prior to joining Spencer Stuart, Dr. Anzelc held a number of leadership roles in data and analytics in the insurance industry, most recently serving as Chief Analytics Officer for AXIS Capital, a global provider of specialty lines insurance and reinsurance. She also held analytics leadership roles at Zurich North America, a leading provider of commercial property-casualty insurance solutions and risk management products and services for businesses and individuals; at CNA, one of the largest U.S. commercial property and casualty insurance companies; and in Travelers’ personal insurance division. Throughout her career, Dr. Anzelc has been active in numerous initiatives focused on providing relevant guidance and mentoring on career planning and professional development with a focus on underrepresented groups in STEM. While in graduate school, Dr. Anzelc served in multiple roles with the APS Forum on Graduate Student Affairs and as the student representative on the APS Committee on the Status of Women in Physics. Since receiving her PhD, she has been a panelist and speaker at numerous APS meetings and events, the Conference for Undergraduate Women in Physics, and various events for STEM students at universities including Northwestern University and Loyola University Chicago. She has also been involved in a range of diversity, equity and inclusion initiatives. Among these, Dr. Anzelc was a member of the Women in Insurance Networking Group and was the Co-founder and Co-Chair of the Women in Actuarial & Analytics group at Travelers, the organization’s first Diversity Business Network, a group dedicated to the recruitment, retention, and advancement of women in analytical roles and winner of the 2011 NALC Above-and-Beyond Award. Dr. Anzelc holds a Master’s and PhD in Physics from Northwestern University and a Bachelor’s in Physics from Loyola University Chicago.
About Michael Butts
Michael is an executive leader with 18 years of experience providing talent solution to the Data Analytics, Technology, and Healthcare industries. He currently is the CEO of Burtchworks, a recruiting firm specializing in data science talent. Michael has worked closely with future 500 organizations, small to midsized business, and startups to developing and deploying dynamic contingent labor models that have allowed them to thrive. He is passionate about solving complex problems and tackling challenging projects that improve business outcomes.
About John Kahan
John Kahan is a proven leader with nearly four decades of data science, worldwide business, and technology transformation experience across Microsoft and IBM. In 2021, John was named by DatatechVibe as one of the top 15 data professionals globally, transforming and redefining business and the industry. Through his work with Microsoft, Kahan has leveraged large datasets to solve problems and help transform the company into a customer-driven paradigm. His direct work with the senior leadership team resulted in better product development, engineering, marketing, sales, and financial strategies. This includes building the early data infrastructure that underpins today's Microsoft's Azure Cloud Services and Bing search engine. Kahan holds several board and advisory roles. He currently holds roles on the board of predictive analytics platform company Equinauts, and as a strategic advisor, to its CEO; and on the board Novartis Foundation, where he advises on the use of AI to help drive improvements in cardiovascular disease in low-income areas. John is also the Chairman of the Board of the Aaron Matthew SIDS Research Foundation of Seattle Children's — named after his son who died of SIDS in 2003.
About Deepali Vyas
Deepali Vyas serves as Global Head of FinTech, Payments and Crypto Practice at Korn Ferry based in the firm’s New York office. She also serves as the Global Leader of the Applied Intelligence (AI, Data Science) Practice. Deepali brings more than 20 years of c-suite executive search and board experience. Her search practice spans financial services, technology, industrial, healthcare and consumer retail industries. She has advised clients on organizational design, strategy and assessments across the data ecosystem, including: artificial intelligence, data science, and machine learning. She is also a well published thought leader on topics such as digital assets, blockchain, cryptocurrencies, innovation, digital transformation, and talent in data and analytics. Deepali frequently contributes to media outlets including Bloomberg, FundFire, CNBC, Business Insider and Forbes. Deepali has been featured as Business Insiders’ “Top 10 Global Headhunters in FinTech.” Deepali is deeply passionate about diversity and inclusion, partnering with startups like Correlation One on diversity initiatives that offer data science training to the Black and Latinx communities. She has been an advocate for women in STEM careers, sponsoring annual the Women’s Summit in Data Science (DS4A). Prior to joining Korn Ferry, Deepali was sector leader for Global Markets, Quant & Analytics at a large executive search firm. Her collection of search work has been on behalf of Fortune 500, venture-backed, and private-equity-owned companies. Functional search experience includes chief data officer, chief digital officer, chief analytics officer, data science, artificial intelligence, CEO, CTO, Chief Product Officers, machine learning and general management roles. Prior to that role, Deepali ran a boutique executive search firm that she founded, where she worked primarily in investment management and quantitative strategy recruiting and developed expertise in the electronic and algorithmic trading arenas.
Thursday @ 11:15 AM
How to Use New Jupyter Tools in Production Environments
How to Use New Jupyter Tools in Production Environments
MLOps Applied
Thursday, May 5, 11:15 AM – 12:00 PM (45 min.)
Over the last ten years, Jupyter Notebooks have firmly established themselves as an indispensable environment for data analysis and machine learning development. However, Jupyter Notebooks can be difficult to integrate, deploy, and operationalize within conventional software engineering workflows, so much so that some software engineers and DevOps specialists discourage the use of Jupyter at all. Recently, many dedicated developers have been creating a new ecosystem of tools that address the challenges of using Jupyter in production software environments. In this talk, we'll explore some of these tools and show how they can be used to retain the flexibility, interactivity, and rich expressiveness of Jupyter Notebooks in data analysis and machine learning workflows in production.
About Patrick Harrison
Patrick Harrison serves as an AI consultant, researcher, and educator. Before starting Data Theoretic, Patrick spent eight years working in machine learning at a major financial intelligence company. As the first data scientist in the organization, he partnered with senior leadership to identify high-impact opportunities to deploy AI within the business. After several pilot projects delivered measurable business value, Patrick had the opportunity to start and lead the company's AI Engineering team. He led a recruiting effort that resulted in hiring sixteen high-powered data scientists and machine learning engineers. He managed the design and implementation of the organization's infrastructure, tools, and workflows to support machine learning research, development, and production operations. Along the way, he spent time working at many stages of the engineering career ladder: from a solo data scientist, to a tech lead with a small team, to an engineering manager with a larger team, to a department head responsible for multiple AI development teams. He has practical experience working on business applications using many areas of machine learning and natural language processing, including text classification, information extraction, document recognition, search, recommendations, probabilistic data matching, customer segmentation, time-series forecasting, churn prediction, and more. Patrick's group became an incubator for technology talent: many of the team members he recruited, hired, and led have since gone on to hold senior technical positions at companies like Meta (Facebook), Microsoft, Capital One, hedge funds, and a variety of smaller, tech-forward organizations.
Thursday @ 12:30 PM
Lessons Learned for AI Data Management at Scale (Presented by NetApp)
Lessons Learned for AI Data Management at Scale (Presented by NetApp)
Data Science Management
Thursday, May 5, 12:30 PM – 1:15 PM (45 min.)
Data scientists demand a best-in-class workbench experience, with efficient data pipelines and scalable compute performance. Converged infrastructure paired with MLOps can address the most common AI workflow pain points, streamlining the flow of data reliably and accelerating analytics, training, and influence. Learn how you can leverage NetApp, NVIDIA, and Domino Data Lab to accelerate a variety of ML/DL workloads, such as NLP and computer vision across financial services, life sciences, and other industries. In addition, you will hear about specific customer examples of how these technologies work together.
About Hoseb Dermanilian
Hoseb joined NetApp in 2014. In his current role, he is responsible of leading NetApp’s AI and Digital Transformation business globally. Hoseb heads a global team focused on helping customers build the right platform for their data driven business strategies. As part of his role, Hoseb is also focused on developing NetApp’s AI channel business by recruiting and enabling the right AI ecosystem partners and enabling Go-To-Market strategies with those partners. Hoseb is coming from a technical background. In his previous role, He was the Consulting System Engineer for NetApp’s video surveillance and big data analytics solutions. Hoseb holds a Master's degree with distinction in Electrical and Computer Engineering from the American University of Beirut.
About Tony Paikeday
Tony Paikeday is senior director of AI systems at NVIDIA, responsible for the go-to-market for NVIDIA’s DGX portfolio of AI supercomputers. He helps enterprise organizations infuse their business with the power of AI with infrastructure solutions that enable insights from data. Tony was previously with VMware, responsible for bringing desktop and application virtualization solutions to market, as well as key enabling technologies including GPU virtualization and software-defined data center. Prior to joining VMware, Tony was at Cisco, building its data center solutions.
About Thomas Robinson
Thomas Robinson is VP of strategic partnerships and corporate development at Domino, where he's responsible for building Domino's partner ecosystem, developing offerings providing differentiated value to partners. He previously acted as Domino's chief people officer, responsible for building an organization to unleash data science to address the world's most important challenges. Prior to Domino, Thomas worked at Bridgewater Associates, driving strategic transformation efforts, first as a director in Bridgewater's Core Technology Department to define the next generation of enterprise architecture, and then as a general manager focused on recruiting and retaining technical talent.
Thursday @ 01:30 PM
Scaling to Multi-Node AI: The Future of Distributed Computing in Data Science?
Scaling to Multi-Node AI: The Future of Distributed Computing in Data Science?
MLOps Applied
Thursday, May 5, 1:30 PM – 2:15 PM (45 min.)
Could some of your workloads be accelerated for better performance? In this panel, three of the top innovators and thought leaders in distributed computing will discuss how multi-node AI and associated software frameworks are already speeding up everything from processing large data pipelines to training complex deep networks. They’ll also explain how to use distributed compute clusters with modern analytical workloads . Learn what is possible today—and how to tell which of your workloads are good candidates for a new, multi-node AI enhancement. You’ll also gain unique insights about where we are headed to better position your teams to take advantage of emerging trends and technology.
About Robert Nishihara
Robert is currently working on Ray, a high-performance distributed execution framework for AI applications. He studied mathematics at Harvard. He’s broadly interested in applied math, machine learning, and optimization, and was a member of the Statistical AI Lab, the AMPLab/RISELab, and the Berkeley AI Research Lab at UC Berkeley.
About Michael Balint
Michael is the Principal Product Architect for NVIDIA, focused on cluster management of AI compute resources including the deployment of Kubernetes-based platforms and HPC tools. Michael was a White House Presidential Innovation Fellow, where he brought his technical expertise to projects like Vice President Biden’s Cancer Moonshot program and Code.gov. Michael has had the good fortune of applying software engineering and data science to many interesting problems throughout his career, including tailoring genetic algorithms to optimize air traffic, harnessing NLP to summarize product reviews, and automating the detection of melanoma via machine learning. He is a graduate of Cornell and Johns Hopkins University.
About Kjell Carlsson
Kjell advises enterprises on how to drive business outcomes with artificial intelligence (AI) and data science. He does consulting, workshops, keynote speeches and research on topics ranging from augmented intelligence, automated machine learning, computer vision, advanced analytics and machine learning platforms, MLOps, AI APIs, conversation intelligence, AI-enabled business intelligence, AI in healthcare, best practice for scaling data science and the future of AI. He has a unique background spanning strategy and AI including experience leading a product organization for an NLU platform, covering AI as an industry analyst, leading a data science team building AI applications, management consulting experience in the tech and financial sectors, and a Business Economics PhD focused on strategy and quantitative analysis.
About Neil Conway
Neil Conway is co-founder and CTO of Determined AI, a startup that builds software to dramatically accelerate deep learning model development. Neil was previously a technical lead at Mesosphere and a major developer of both Apache Mesos and PostgreSQL. Neil holds a PhD in Computer Science from UC Berkeley, where he did research on large-scale data management, distributed systems, and programming languages.
Thursday @ 01:30 PM
Solving Enterprise Data Challenges with Apache Arrow
Solving Enterprise Data Challenges with Apache Arrow
MLOps Applied
Thursday, May 5, 1:30 PM – 2:15 PM (45 min.)
Since its launch in 2016, the Apache Arrow project has become the de facto standard for efficient in-memory analytics and fast data transport. In this talk, Wes McKinney will discuss recent developments in Arrow and in complementary projects, including Substrait and Ibis. These developments are making Arrow relevant in more technology categories and driving its increased use in business-critical applications. Wes will describe several ways that enterprises are depending on Arrow to solve crucial data challenges. Highlighting real-world use cases, Wes will show how Voltron Data is helping to safeguard and accelerate enterprise applications of Arrow.
About Wes McKinney
Wes McKinney is an open source software developer focusing on analytical computing. He created the Python pandas project and is a co-creator of Apache Arrow, his current focus. He authored two editions of the reference book, Python for Data Analysis. Wes is a member of The Apache Software Foundation and also a PMC member for Apache Parquet. He is now the CEO of Ursa Computing, a new startup working on accelerated computing technologies powered by Apache Arrow for data science languages like Python and R.
Thursday @ 01:30 PM
The Data Collaboration Stack: From DataOps to MLOps
The Data Collaboration Stack: From DataOps to MLOps
MLOps Applied
Thursday, May 5, 1:30 PM – 2:15 PM (45 min.)
How does the Modern Data Stack enable collaboration for data teams? Collaboration works like a flywheel that harnesses the collective energy of a data team and directs it towards new opportunities and innovation. Outstanding achievements emerge when teams collaborate to integrate and leverage their strengths towards a common goal. We’ll walk through some of the approaches that successful teams employ at Amazon, AWS, and Netflix to succeed on these fronts. We’ll also walk through what we called the Data Collaboration Stack, from DataOps to MLOps.
About Pierre Brunelle
Pierre is a co-founder, Co-CEO, and CPO of Noteable. Pierre Brunelle led Amazon’s notebook initiatives both for internal use and SageMaker. He also worked on many open source initiatives, including a standard for Data Quality work and an open-source collaboration between Amazon and UC Berkeley to advance AI and machine learning. Pierre helped launch the first Amazon online car leasing store in Europe. At Amazon, Pierre also launched a Price Elasticity Service and pushed investments in Probabilistic Programming Frameworks. And Pierre represented Amazon on many occasions to teach Machine Learning or at conferences such as NeurIPS. Pierre also writes about Time in Organization Studies. Pierre holds an MS in Building Engineering from ESTP Paris and an MRes in Decision Sciences and Risk Management from Arts et Métiers ParisTech.
Thursday @ 01:30 PM
Data Scientists and C-Suiters: Is Your AI Breaking the Law?
Data Scientists and C-Suiters: Is Your AI Breaking the Law?
Data Science Management
Thursday, May 5, 1:30 PM – 2:15 PM (45 min.)
AI is an important technology with disruptive potential for industry, government, and the public. While many are familiar with the perceived benefits and hype around the tech, AI systems can be abusive black-boxes, they can be hacked, they can violate data privacy and nondiscrimination laws, and they can physically harm people. With more regulation on the horizon, ignorance of AI risks is not bliss. With governments, regulators, and the public beginning to view AI with more skepticism, now is the time to set your organization apart from the competition with solid AI risk management practices. This presentation will draw from established practices in model risk management, computer security, data privacy, and nondiscrimination law to put forward a holistic vision for AI risk management. It will also provide a high-level overview of pending regulations. To move forward, organizations need to prepare for a higher-scrutiny landscape. There's no time like the present to get started.
About Patrick Hall
Patrick Hall is principal scientist at BNH.AI, where he advises Fortune 500 clients on matters of AI risk and conducts research on AI risk management in support of NIST’s efforts on trustworthy AI and technical AI standards. He also serves as visiting faculty in the Department of Decision Sciences at The George Washington School of Business, teaching classes on data ethics, machine learning, and the responsible use thereof. Prior to co-founding BNH, Patrick led H2O.ai’s efforts in responsible AI, resulting in one of the world’s first commercial solutions for explainable and fair machine learning. He also held global customer-facing roles and R&D research roles at SAS Institute. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. Patrick’s technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch and others. An ardent writer himself, Patrick has contributed pieces to outlets like McKinsey.com, O’Reilly Ideas, Thompson-Reuters Regulatory Intelligence, and he is the lead author for the forthcoming book, Machine Learning for High Risk Applications.
Thursday @ 01:30 PM
Healthcare Delivery in a Model-Driven World
Matt McGinnis
Managing Director, Head of Data Science CoE and Pharmacy and Specialty Solutions Analytics, Cigna/Evernorth
Healthcare Delivery in a Model-Driven World
The Future of Model-Driven Business
Thursday, May 5, 1:30 PM – 2:15 PM (45 min.)
Digital healthcare data and machine learning at scale has brought unprecedented change in the healthcare and life sciences sector, with data and predictive models driving a new focus on innovation across the value chain. This panel includes leaders from healthcare companies leading the charge using predictive models to drive better health outcomes and business value. How can a data science center of excellence scale ML across the enterprise? How can both data scientists and healthcare/business domain experts better collaborate to put more models into production? What infrastructure is required to deliver on the promise of ML?
Thursday @ 02:30 PM
Buy AI Services or Build Your Own? Decision Criteria, Hybrid Strategies, and Execution Tips
Buy AI Services or Build Your Own? Decision Criteria, Hybrid Strategies, and Execution Tips
Data Science Management
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
Many companies are investing in building data science teams whose mission is to deliver AI-powered benefits for the company and its customers. But as companies realize the value of AI throughout their organizations, data science teams are often stretched too thin to develop every AI project internally. How and when and should you buy AI-based services instead of developint them in-house? What are right decision criteria? This talk will address these questions and more, including what makes for a sound hybrid strategy, the pitfalls of outsourcing and how to avoid them, and how to handle the challenges of outsourcing AI in the context of upcoming AI regulations.
Thursday @ 02:30 PM
Cure Quest: The Impact of Data Science Models on the Next Generation of Life Science Product Development
Cure Quest: The Impact of Data Science Models on the Next Generation of Life Science Product Development
The Future of Model-Driven Business
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
How will data science influence the next generation of cures and treatments? Can data science increase the likelihood and velocity of scientific discoveries making it into clinical practice? Where will the biggest data science payoffs be, and how can current impacts be extended? Hear from an expert panel of data science leaders on current efforts to extend the power of data in every phase of the new product development lifecycle.
About Andrea de Souza
A former neuroscience researcher, Andrea’s portfolio career has included leadership assignments at the intersection of science, technology and business development. She has built and led informatics and scientific teams across the entire pharmaceutical value chain. Most recently, Andrea focused on building the Pharma Artificial Intelligence market at NVIDIA. Through this experience she has travelled the world advising biopharmaceutical, academics, research institutes, and startups in the potential of machine learning and artificial intelligence across every discipline in our industry. Prior to her role at NVIDIA, Andrea held leadership positions at the Broad Institute of Harvard and MIT, Amgen, and Roche. In the last three years, Andrea’s work at Eli Lilly & Company has focused around empowering the LRL Research organization with greater computational, analytics-intense experimentation to raise the innovation of our scientists.
Thursday @ 02:30 PM
Navigating the Ethics Minefield: Building Legal, Ethical AIs
Navigating the Ethics Minefield: Building Legal, Ethical AIs
Data Science Management
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
No one wants to end up on the front page of the paper due to an biased AI model or one that doesn't behave in a way that society finds acceptable. Staying within the ethical bounds of your industry requires constant attention. It's critical to put in place the practices that help your organization identify issues, mitigate them and oversee model behavior. In this session you will hear how leading organizations are tackling this challenge and steps you can take to successfully navigate the ethics minefield.
About Anju Gupta
Anju is responsible for enabling data science and analytics for Northwestern Mutual. Prior to Northwestern Mutual, Anju was responsible for overseeing Enterprise Holdings’ data asset strategy and develop insights that helped positively impact the future of mobility – from enhancing day-to-day operations and customer experience to leading strategic decision-making and technology innovation efforts.
About Bill Franks
Bill Franks is the Director of the Center for Statistics and Analytical Research within the School of Data Science and Analytics at Kennesaw State University. In this role, he helps companies and governmental agencies pair with faculty and student resources to further research in the area of analytics and data science. He is also Chief Analytics Officer for The International Institute For Analytics (IIA) and serves on the advisory board of ActiveGraf, Aspirent, DataPrime, DataSeers, and Kavi Global. Bill continues to provide perspective on trends in the analytics, data science, AI, and big data space to help organizations understand how to further their analytics and data science efforts. His work, including several years as Chief Analytics Officer for Teradata (NYSE: TDC), has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. He provides advisory services through his company Analytics Advisory Partners (Click here for more info). Franks is also the author of the books Winning The Room, 97 Things About Ethics Everyone In Data Science Should Know, Taming The Big Data Tidal Wave, and The Analytics Revolution.
Thursday @ 02:30 PM
How 'Explainable' Is Your AI-driven Image Analysis? A Real-world Evaluation
How 'Explainable' Is Your AI-driven Image Analysis? A Real-world Evaluation
MLOps Applied
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
The term ""explainable AI"" implies that AI models will be understandable for everyone, not only Data Scientists. We set out to test current explainable AI tools on one of the image models currently used in TopDanmark. This work is a joint collaboration with scientists from a research institute. The results of this study illustrate the extent of explainability with these tools. When we talk about explainable AI in relation to neural nets used for Computer Vision what can we learn? We set out to test the XAI tools respectively GradCam, Guided GradCam and Occlusion on one of our Computer Vision models. We conducted the test in collaboration with Data Scientists from The Alexandra Institute’s department of Artificial Intelligence. The results from this study will illustrate what our Data Scientists can use the XAI tools for.
About Stig Pedersen
Stig Pedersen is Topdanmark's head of the Machine Learning Centre of Excellence, responsible for machine learning and data science at the leading Danish insurance firm. Having first worked as part of business units commercializing other technology by working to deploy robotic process automation models, Stig saw the value of machine learning and data science. He embarked on a mission to build a group of data scientists to construct algorithms and models to help build better efficiency and ultimately create value inside Topdanmark.
Thursday @ 02:30 PM
Financial Time-series Modeling: A Data-driven Machine-Learning Technique
Financial Time-series Modeling: A Data-driven Machine-Learning Technique
MLOps Applied
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
In this talk, we discuss how data-driven machine learning approach can be leveraged for financial time series generation. In finance industry, being able to generate realistic financial time series, yet have the ability to flexibly incorporate the view on the market are crucial to applications such as backtesting, stress testing in portfolio construction or risk management. Traditional econometric models impose assumptions on the underlying dynamics, yet struggle to fully capture the stylized facts of the financial time series. We provide an overview of various machine learning techniques that can be used to tackle the problem to preserve patterns from the time series and discuss how they can be extended to incorporate views.
Thursday @ 02:30 PM
Transforming drug discovery with MLOps
Transforming drug discovery with MLOps
Data Science Management
Thursday, May 5, 2:30 PM – 3:15 PM (45 min.)
Novo Nordisk is transforming drug development and optimization by embedding ML in their lab automation. In this talk, you will hear how the team at Novo Nordisk is accelerating model velocity with the Domino Enterprise MLOps Platform to continually improve the quality of models used in the lab. They have streamlined model development through robust experiment management and use model APIs to dramatically cut the time to deployment.
About Carsten Stahlhut
Carsten Stahlhut is the Principal Data Scientist within the research environment at Novo Nordisk. Carsten was previously Head of Quants for Alipes ApS. With 15+ years of experience in the field, Carsten is passionate about analyzing challenging data embodied in noise to discover new meaningful patterns using tools ranging from simple linear models to more advanced machine learning techniques. Carsten holds a Ph.D. in Applied Mathematics, Machine Learning, and Neuroimaging from the Technical University of Denmark.
Friday @ 11:15 AM
Scaling AI enabled Digital Transformation in Healthcare & Life Sciences Industry
Siddhartha Bhattacharya
Director, Healthcare Analytics & AI, PwC
Scaling AI enabled Digital Transformation in Healthcare & Life Sciences Industry
The Future of Model-Driven Business
Friday, May 6, 11:15 AM – 12:00 PM (45 min.)
How do you scale up AI-enabled digital transformations? Learn the importance of defining and aligning on AI-enabled digital transformation strategies, where AI is making the biggest impact in life sciences, the challenges organizations face scaling up AI and best practices for overcoming them.
About Siddhartha Bhattacharya
Sidd is a technology and management consultant in PwC’s Pharma & Life Sciences practice with over 10+ years of experience across pharmaceutical, biotechnology, and medical devices.
Friday @ 11:15 AM
How to Unlock Exciting Business Opportunities Using New Data Sources
How to Unlock Exciting Business Opportunities Using New Data Sources
The Future of Model-Driven Business
Friday, May 6, 11:15 AM – 12:00 PM (45 min.)
The main ingredient in any great model -- whether for making a prediction, automating a business process, or anything else -- is high quality data. Moreover, the breadth and depth of data available today is greater than ever. Alex Izydorczyk pioneered many of the approaches used in the finance industry for turning unique data sources into investment insights. He's now building on what he learned from almost 7 years in finance to find new business opportunities being created by the explosion in data. In this talk, he'll share some of the key lessons he learned in finance and the kind of new opportunities he sees being made available by new data in the world.
About Alexander Izydorczyk
Alex Izydorczyk previously led the data science team at Coatue, overseeing the engineering and statistical teams’ process of integrating “alternative data” into the investment process. The team uses cutting edge methods from machine learning and statistics to digest and analyze a broad universe of data points to identify market and investing trends. Alex is also involved on the private investment side, particularly on topics of cryptocurrency and data science infrastructure. He graduated from the Wharton School at the University of Pennsylvania in 2015 with a degree in statistics.
Friday @ 11:15 AM
Where is the Next Frontier in Model-driven Businesses?
Where is the Next Frontier in Model-driven Businesses?
The Future of Model-Driven Business
Friday, May 6, 11:15 AM – 12:00 PM (45 min.)
This panel considers what comes next: Which businesses and industries will models disrupt in the next five years. The panelists are a mix of investors and strategists who spend their days envisioning the future and will share with us how they see the model-driven future playing out. We'll talk about the where, the why, and the how of these opportunities.
About Ash Fontana
Ash currently acts as a Special Advisor to Zetta Venture Partners, where he was a Managing Director from 2015 to 2021. Before Zetta, Ash launched AngelList’s fundraising platform that now manages over $7B. Prior to AngelList, he co-founded Topguest (acquired), a loyalty software platform that counted United Airlines, Hilton Worldwide, and Virgin America as clients. He started his career in growth investing at Macquarie Capital in New York after studying law at the University of Sydney. Penguin Random House has published his book, “The AI-First Company: How to Compete and Win with Artificial Intelligence”.
About Alexander Andonyadis
Alexander is part of Capgemini's Data Science & Artificial Intelligence practice. He uses his extensive academic and professional experience in advanced analytics and business strategy to deliver models, insights, and actionable recommendations to optimize and modernize strategy & operations.
About Sri Chandrasekar
Sri joined Point72 from In-Q-Tel, the strategic investment arm of the CIA and the U.S. Intelligence Community. At In-Q-Tel, Sri led Lab41, an AI-focused lab that built and delivered solutions to some of the intelligence community’s toughest problems. Before Lab41, Sri led the Analytics practice at In-Q-Tel, a role in which he was responsible for setting the investment priorities for a team of technical investment professionals. Prior to In-Q-Tel, Sri spent nearly a decade designing and building communication systems for the military at BAE Systems.
Friday @ 11:15 AM
It's in the Data! How Improving ML Datasets Is The Best Way To Improve Model Performance
It's in the Data! How Improving ML Datasets Is The Best Way To Improve Model Performance
MLOps Applied
Friday, May 6, 11:15 AM – 12:00 PM (45 min.)
When working to improve an ML model, many teams will immediately turn to fancy models or hyperparameter tuning to eke out small performance gains. However, the majority of model improvement can come from holding the model code fixed and properly curating the data it's trained on! In this talk, Peter discusses why data curation is a key part of model iteration, some common data and model problems, then discusses how to build workflows + team structures to efficiently identify and fix these problems in order to improve your model performance.
About Peter Gao
Peter was an early employee (#18) at Cruise, where he built a large part of a self driving car from scratch and led the computer vision team and was the tech lead for the overall perception team. Before that, Peter did research on deep learning for object detection at UC Berkeley. Before that, he interned at Pinterest and Khan Academy, doing a mix of ML and web work. Now he's founding a company building deep learning pipelines that can improve themselves!
Friday @ 11:15 AM
How Is Machine Learning Transforming Defense?
Gokul Subramanian
Anduril Industries, Vice President of Engineering
How Is Machine Learning Transforming Defense?
The Future of Model-Driven Business
Friday, May 6, 11:15 AM – 12:00 PM (45 min.)
Data, machine learning, and models are reshaping one of the most critical industries in the world: the defense industry. This panel -- which includes some of the leading builders of these new systems -- will consider the critical questions arising from these efforts to integrate cutting-edge technologies into today's military challenges. Can these tools be used to create more effective weapons? What defensive capabilities do they offer? How quickly are countries integrating these new capabilities into their military strategies? What obstacles stand in the way? And across all of these questions, what are the ethical considerations?
About Daniel Gwak
Before joining Point72, Dan was a Partner on the Investment Team at In-Q-Tel, the strategic investment firm of the CIA and U.S. Intelligence Community. At In-Q-Tel, Dan focused on enterprise analytics and infrastructure companies whose technologies aided the mission of the U.S. intelligence community. Previously, Dan was an investment banking analyst in the M&A group at Credit Suisse, a private equity associate at The Carlyle Group, and a fireteam leader in the United States Marine Corps, where he was awarded the Combat Action Ribbon and Purple Heart for actions in support of Operation Enduring Freedom in Helmand Province, Afghanistan.
About Brandon Tseng
Brandon Tseng is Shield AI’s Co-Founder and Chief Operating Officer. Prior to founding Shield AI, Brandon proudly served in the US Navy for seven years. As a Navy SEAL, he served two deployments to Afghanistan and one to the Pacific Theater. Previously, he was the Assistant Chief Engineer aboard the USS Pearl Harbor (LSD-52) and deployed to the Persian Gulf. Brandon earned his Bachelor of Science in Mechanical Engineering from the United States Naval Academy and his MBA from Harvard Business School.
About Gokul Subramanian
Gokul Subramanian is a Vice President of Engineering at Anduril Industries. In his current role, Gokul leads the software organization and oversees new product development across the business. At Anduril, Gokul was the founding member of Anduril’s Perception team as well as Chief Engineer for Anduril’s Counter Unmanned Aerial Systems (CUAS) program. Prior to Anduril, Gokul worked as Forward Deployed Engineer at Palantir Technologies and as a researcher at Systems and Technology Research (STR). Gokul holds undergraduate and graduate degrees in Electrical Engineering.
Friday @ 12:30 PM
The 7 Things You Need to Get Right When Operationalizing AI (Presented by NVIDIA)
The 7 Things You Need to Get Right When Operationalizing AI (Presented by NVIDIA)
Data Science Management
Friday, May 6, 12:30 PM – 1:15 PM (45 min.)
Many teams with ambitious AI goals focus intently on delivering viable AI models to production and scaling their work to support various initiatives. Unfortunately, too often the right strategy across personnel, processes, and platform is missing, thereby inhibiting AI success at scale. Without a shared platform and definition of what success looks like, data scientists take matters into their own hands, resulting in fragmented practices, innovation sprawl, and inefficient use of resources. From the experience of enterprise AI implementations around the globe across many industries, we’ve compiled the top seven things every AI project team needs to weigh carefully to speed the ROI of their AI investment.
About Michael Balint
Michael is the Principal Product Architect for NVIDIA, focused on cluster management of AI compute resources including the deployment of Kubernetes-based platforms and HPC tools. Michael was a White House Presidential Innovation Fellow, where he brought his technical expertise to projects like Vice President Biden’s Cancer Moonshot program and Code.gov. Michael has had the good fortune of applying software engineering and data science to many interesting problems throughout his career, including tailoring genetic algorithms to optimize air traffic, harnessing NLP to summarize product reviews, and automating the detection of melanoma via machine learning. He is a graduate of Cornell and Johns Hopkins University.
About William Vick
Will is a Technical Strategist at NVIDIA, focused on helping customers across the globe with the development, definition, & creation of their AI Center Of Excellence
Friday @ 01:30 PM
How Data Science is Reshaping the Insurance Industry
How Data Science is Reshaping the Insurance Industry
The Future of Model-Driven Business
Friday, May 6, 1:30 PM – 2:15 PM (45 min.)
What's ahead for one of Ithe oldest industries in the world? SCOR's Antoine Ly will explain how data sciencemodels, and machine learning are improving the ability to understand and price risks. Insurance companies are also using these capabilities to offer new services to customers (like instant quotes in the case of auto accidents). They are even creating new insurance products like cyber and climate insurance. Our panelists are at the frontline of these changes -- building these products and services themselves -- and will paint a vivid picture of the challenges and opportunities that lie ahead.
About Antoine Ly
Antoine puts knowledge sharing and learning at the center of the team & company development. He contributes to the company growth by acting and sharing those key values that benefit to innovation, self-development and value-added deliveries. With his mathematical background, he is sensitive to the application of research and technologies to answer business and real life issues by following software engineering best practices.
About Glenn Hofmann
Glenn Hofmann is currently the Chief Analytics Officer at New York Life Insurance Company (NYL), a Fortune 100 firm, where he combines extensive knowledge in applied statistics, analytics and data infrastructure with deep expertise in building and leading large teams of data- and analytics-focused professionals from the ground up to influence strategy as well as transactional decisions through data science-driven innovation. Glenn has previously held leadership positions at Allstate, TransUnion, and Verisk Analytics.
About Nat Manning
Nathaniel previously led Ushahidi, the world’s largest open source data platform for crisis response. Here he helped scale the Ushahidi platform to over 200 countries gathering over 10M first hand reports. Previous to that he was a Presidential Innovation Fellow for Open Data, and then the first Chief Data Officer of the US Agency for International Development, where he helped open up and analyze large data sets for humanitarian response. He has been part of the founding teams of technology startups like BRCK and FellowAI.
About Nathan Greenhut
Nathan Greenhut is a Cognizant Client Solutions Executive. He has over 16 years of experience in cognitive systems, artificial intelligence, decision support systems, large-scale system modernization and transformation, and continuous improvement programs. His area of expertise is in banking and financial services. Nate recently led the delivery of a data warehouse and big data lake for a client to establish and augment the technological infrastructure, bringing siloed data together. Additionally, Nate developed and drove a conversational AI implementation on a development platform that included Amazon Alexa, Google Assistant and IPSoft Amelia. Throughout his career, Nate has led large, multi-disciplined groups, including data science, artificial intelligence, machine learning and engineering teams, to build integrated data analytics platforms for performance improvement, customer, sales analytics, fraud, cyber and anti-money laundering analytics. Nate holds bachelor of science, master of science and master of business administration degrees from Rutgers University and is a Certified Fraud Examiner. Currently, Nate is enrolled in Harvard’s Business Analytics Program.
Friday @ 01:30 PM
The Untapped Potential of Vector Embeddings
The Untapped Potential of Vector Embeddings
MLOps Applied
Friday, May 6, 1:30 PM – 2:15 PM (45 min.)
Just an array, or the secret ingredient inside the world’s best products? Vector embeddings are used by the most sophisticated ML teams at places like Google, Facebook, Microsoft, and Amazon to make ML products that seem to know you better than your friends do. In this session you will: Discover how those companies use vector embeddings to power semantic search, image/audio search, recommendations, feed ranking, abuse/fraud detection, deduplication, and other applications. Learn where and how you can use vector embeddings in your own applications, from the lab to large-scale production applications. Edo Liberty is the Founder and CEO of Pinecone — the vector database — and the former Director of Research and Head of AI Labs at AWS.
About Edo Liberty
Edo Liberty is the Founder and CEO of Pinecone, the vector database for machine learning. Until April 2019, he was a Director of Research at AWS and Head of Amazon AI Labs. The Lab built cutting-edge machine learning algorithms, systems, and services for AWS customers. Before AWS, He also has served as a Senior Research Director at Yahoo and Head of Yahoo’s Research Lab in New York, where he worked on building horizontal machine learning platforms and improving applications such as online advertising, search, security, media recommendation, and email abuse prevention. Edo received his B.Sc in Physics and Computer Science from Tel Aviv University and his Ph.D. in Computer Science from Yale University. After that, he was a Postdoctoral fellow at Yale in the Program in Applied Mathematics.
Friday @ 01:30 PM
Why Data Science Isn't Rocket Science--It's Harder! Learnings from Lockheed Martin
Why Data Science Isn't Rocket Science--It's Harder! Learnings from Lockheed Martin
Data Science Management
Friday, May 6, 1:30 PM – 2:15 PM (45 min.)
What are the challenges in scaling AI/ML skills, capabilities and projects across a large enterprise? Hear from Lockheed Martin's Bruno Janota, Artificial Intelligence Research Manager and Mike Koelemay, Senior Manager of AI/ML Labs and AI Consulting, who will share their experiences and tips for better processes, management, collaboration, and technology.
About Bruno Janota
Bruno Janota works as a Lead Artificial Intelligence Research Engineer within the Chief Technology Office AI Consulting team at Lockheed Martin. He and his team develop end-to-end advanced analytics models that deliver actionable insights and recommendations using machine learning, data mining, and visualization techniques.
Friday @ 01:30 PM
Is Synthetic Data the Key to Better Enterprise ML & Software Testing?
Is Synthetic Data the Key to Better Enterprise ML & Software Testing?
MLOps Applied
Friday, May 6, 1:30 PM – 2:15 PM (45 min.)
Most large enterprises benefit from economies of scale. However, with size come more operational bottlenecks in the handling of data–for reasons relating to security, regulatory compliance and corporate structure. Many enterprises sacrifice data agility for lower risk, which hinders innovation and strategic goal setting. In this talk, you’ll learn how to use synthetic data to both mitigate these risks and simultaneously reduce both time-to-data and time-to-value.
About Nicolai Baldin
Nicolai is co-founder and CEO of Synthesized, which has built a DataOps platform creating optimized and compliant data products to fuel excellence in testing, analytics and machine learning. He has led the growth of the business from a simple idea to a service used by tech companies in the UK, Europe and the US. Nicolai is responsible for the direction and product strategy of Synthesized. For over eight years, Nicolai has designed and delivered complex ML solutions used by top financial and healthcare institutions. He holds a PhD in Machine Learning and Statistics from the University of Cambridge.
Friday @ 01:30 PM
When Good Models Go Bad: How to Monitor Effectively for Better Real-world Performance
When Good Models Go Bad: How to Monitor Effectively for Better Real-world Performance
MLOps Applied
Friday, May 6, 1:30 PM – 2:15 PM (45 min.)
Machine Learning models often exhibit poor performance when applied to real-world problems. Surprisingly, this effect is observed even when rigorous validation procedures have been followed, and the model generalization measured under laboratory conditions seems acceptable. New research reveals that underspecified pipelines could be the main culprit for models that score well on hold-out sets but perform poorly once operationalized, recent research says. In this talk, we cover the theoretical foundations of the underspecification effect, discuss several case studies using ensemble and deep learning models, and provide suggestions for training models with credible inductive biases. The talk features a Python-based demo using some simple examples and discusses a monitoring framework that alleviates the underspecification effects.
About Nikolay Manchev
Nikolay Manchev is the Principle Data Scientist for EMEA at Domino Data Lab. In this role, Nikolay helps clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.