IN-PERSON & VIRTUAL
Oct 31st – Nov 3rd, 2023
Hyatt Regency San Francisco Airport
Speakers
Hours of Content
Companies
Hybrid Attendees
EXPLORE PAST TRACKS
The Conference was amazing! Thank you to the staff and volunteers of the Open Data Science Conference for putting an amazing conference together! I can’t wait to attend next year!
Data Analyst, USA
I had the amazing opportunity to attend #ODSCWest this past week in San Francisco with my team. I was able to learn more about cutting-edge AI and ML techniques and ways that we can utilize these at our company!
Data Scientist, USA
#ODSCWest Awesome insightful talks and workshops! Buzzwords: MLOps, FeatureStore, ML MetadataStore, Automated retraining, and many more…
Data Science Engineer, USA
Amazing to see so many professionals sharing their knowledge. Exciting concepts which will gain further momentrum no matter which industry you are working in. Check it out!
Process & Quality Manager, Canada
Previous ODSC West Keynotes
SOME OF OUR FEATURED SPEAKERS
David Patterson, PhD
David Patterson received BA, MS, and PhD degrees from UCLA. He is a UC Berkeley Pardee professor emeritus, a Google distinguished engineer since 2016, the RIOS Laboratory Director, and the RISC-V International Vice-Chair.
His most influential Berkeley projects likely were RISC and RAID. He received service awards for his roles as ACM President, Berkeley CS Division Chair, and CRA Chair and awards for his teaching. The most prominent of his seven co-authored books is Computer Architecture: A Quantitative Approach.
He and his co-author John Hennessy shared the 2017 ACM A.M Turing Award, the 2021 BBVA Foundation Frontiers of Knowledge Award, and the 2022 NAE Charles Stark Draper Prize for Engineering. The Turing Award is often referred to as the “Nobel Prize of Computing” and the Draper Prize is considered a “Nobel Prize of Engineering.”
Outside of work he plays soccer, lifts weights, cycles, and bodysurfs. He has been married to his high-school sweetheart since 1967, and they have raised two sons, who in turn are raising three grandchildren.
A Decade of Machine Learning Accelerators: Lessons Learned and Carbon Footprint(Talk)
Dawn Song, PhD
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007. She is also a serial entrepreneur.
(Talk)
Dr. Jon Krohn
Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
Deep Learning with PyTorch and TensorFlow(Training)
NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning(Training)
Andreas Mueller, PhD
Andreas Mueller is a Principal Research SDE at Microsoft (previously Columbia, NYU, Amazon), and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. Andreas is also a Software Carpentry instructor.
Automatic DataFrame Profiling and Visualization for Machine Learning(Talk)
Kira Radinsky, PhD
Dr. Kira Radinsky is the CEO and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. In the past, she co-founded SalesPredict, acquired by eBay in 2016, and served as eBay director of data science and IL chief scientist. One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as “woman of the year” by Globes. She is a frequent presenter at global tech events, including TEDx, Wired, Strata Data Science, Techcrunch and academic conferences, and she publishes in the Harvard Business Review. Radinsky serves as a board member in: Israel Securities Authority, Maccabi Research Institute, and technology board of HSBC bank. Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.
Ben Taylor, PhD
Ben Taylor has over 17 years of machine-learning experience. After studying chemical engineering, Taylor joined Intel and Micron and worked in their photolithography, process control, and yield prediction groups. Pursuing his love for high-performance computing (HPC) and predictive modeling, Taylor joined an artificial intelligence hedge fund (AIQ) as their AI expert. Taylor then joined a young HR startup called HireVue and built out their data science group and helped o launch HireVue’s AI insights product using video/audio from candidate interviews. In 2017 Taylor co-founded Zeff.ai to pursue deep learning for image, audio, video, and text for the enterprise.
Building & Selling AI Startups(Business Talk)
Yaron Haviv
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in AI, cloud, data and networking to leading startups and enterprises since the late 1990s. As the Co-Founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless framework with over 4,000 Github stars and MLRun, a cutting-edge open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA – NASDAQ: NVDA), where he led technology innovation, software development and solution integrations. He also served as the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007 and was later acquired by Mellanox (NASDAQ:MLNX). Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He sits on the Data Science Committee of the AI Infrastructure Alliance (AIIA), of which Iguazio is a founding member. He is co-authoring a book on Implementing MLOps in the Enterprise for O’Reilly. Yaron presents at major industry events worldwide and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.
MLOps in the Era of Generative AI(Track Keynote)
James Demmel, PhD
James Demmel is the Dr. Richard Carl Dehmel Distinguished Professor of Computer Science and Mathematics at the University of California at Berkeley, and former Chair of the EECS Dept. He also serves as Chief Strategy Officer for the start-up HPC-AI Tech, whose goal is to make large-scale machine learning much more efficient, with little programming effort required by users. Demmel’s research is in high performance computing, numerical linear algebra, and communication avoiding algorithms. He is known for his work on the widely used LAPACK and ScaLAPACK linear algebra libraries. He is a member of the National Academy of Sciences, National Academy of Engineering, and American Academy of Arts and Sciences; a Fellow of the AAAS, ACM, AMS, IEEE and SIAM; and winner of the IPDPS Charles Babbage Award, IEEE Computer Society Sidney Fernbach Award, the ACM Paris Kanellakis Award, the J. H. Wilkinson Prize in Numerical Analysis and Scientific Computing, and numerous best paper prizes.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training(Tutorial)
Mike Stonebraker, PhD
Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system.
Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.
Data Mastering at Scale(Track Keynote)
Hannaneh Hajishirzi, PhD
Hanna Hajishirzi is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Senior Research Manager at the Allen Institute for AI. Her research spans different areas in NLP and AI, focusing on developing general-purpose machine learning algorithms that can solve diverse NLP tasks. Applications for these algorithms include question answering, representation learning, green AI, knowledge extraction, and conversational dialogue. Honors include the NSF CAREER Award, Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, best paper and honorable mention awards, and several industry research faculty awards. Hanna received her PhD from University of Illinois and spent a year as a postdoc at Disney Research and CMU.
Toward Robust, Knowledge-Rich Natural Language Processing(Talk)
Greg Michaelson, PhD
Greg Michaelson is Cofounder and Chief Product Officer at Zerve, a young, stealthy startup that’s rethinking the data science development experience. Previously, Greg was an early joiner at DataRobot where he played many roles, including Chief Customer Officer. Prior to that, he worked as a data scientist in the financial sector after earning a PhD in Applied Statistics from the University of Alabama. In his spare time, Greg manufactures a line of flavored breakfast cereal toppings called Cerup. He lives in Spring Creek, Nevada with his wife, four children, and two Clumber Spaniels.
Four Reasons the Data Science Development Experience Sucks(Talk)
Raluca Ada Popa, PhD
Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.
Confidential Data Computing and Collaboration for Data Scientists(Keynote)
Dr. Blaine Nelson
Dr. Blaine Nelson earned his B.S. (University of South Carolina), M.S. and Ph.D (UC Berkeley) degrees in Computer Science. He was a Humboldt Postdoctoral Research Fellow at the University of Tübingen (2011-13) and a Postdoctoral Researcher at the University of Potsdam (2013-14) in Germany. As a graduate student and post-doc, Dr. Nelson co-established the foundations of adversarial machine learning. He has twice co-chaired the ACM CCS workshop on Artificial Intelligence & Security, and co-coordinated the Dagstuhl Perspectives Workshop on Machine Learning Methods for Computer Security (2012).
Following his post-doctoral work, Dr. Nelson worked as a software engineer in Google’s fraud detection group (2014-2016) where he built models and designed infrastructure for large scale machine learning. He then became a senior software engineer at Google’s counter-abuse technology team (2016-2021) where he designed and built a large scale machine learning workflow system. Currently, Dr. Nelson is a principal machine learning engineer at Robust Intelligence where he works in a multi-faceted role to build infrastructure for testing the reliability and security of machine learned models by finding potential flaws or vulnerabilities in their behavior.
Practical Adversarial Learning: How to Evaluate, Test, and Build Better Models(Training)
Celia Cintas, PhD
Celia Cintas is a Research Scientist at IBM Research Africa – Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research explores subset scanning for anomalous pattern detection under generative models and the improvement of ML techniques to address challenges in Global Health. Previously, a grantee from the National Scientific and Technical Research Council at LCI-UNS and IPCSH-CONICET. She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). More info https://celiacintas.github.io/about/
A Tale of Adversarial Attacks & Out-of-Distribution Detection Stories in the Activation Space(Talk)
Scott Zoldi, PhD
Scott Zoldi is chief analytics officer at FICO responsible for advancing the company's leadership in artificial intelligence (AI) and analytics in its product and technology solutions. At FICO Scott has authored more than 120 analytic patents, with 71 granted and 49 pending. Scott is actively involved in the development of analytics applications, Responsible AI technologies and AI governance frameworks, the latter including FICO's blockchain-based [SZ1] model development governance methodology. Scott is a member of the Board of Advisors of FinRegLab, a Cybersecurity Advisory Board Member of the California Technology Council, and a Board Member of Tech San Diego and the San Diego Cyber Center of Excellence. He is also a member of the CNBC Technology Executive Council. Scott received his Ph.D. in theoretical and computational physics from Duke University.
Sadie St Lawrence
Sadie St Lawrence is the Founder and CEO of Women in Data, a community of 30,000+ data leaders, practitioners, and citizens whose mission is to increase diversity in data careers. Women in Data has been named a Top 50 Leading Company of The Year, and has been rated as the #1 community for Women in AI and Tech. Sadie has trained over 400,000 people in data science and has developed multiple programs in machine learning and career development. Sadie has been awarded, Top 30 Most Inspiring Women in AI, Top 10 Most Admired Businesswomen to Watch in 2021, Top 21 Influencer in Data, and is the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on boards, and is the host of the Data Bytes podcast.
Creating An Ethical AI Environment (Business Talk)
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Steven Bird, PhD
Steven Bird has spent much of his career pursuing scalable computational methods for capturing, enriching, and analysing data from endangered languages, drawing on fieldwork in West Africa, South America, and Melanesia. Over the past 5 years he has shifted to working from the ground up with remote Aboriginal communities in Australia, supporting language learning and development in an Aboriginal ranger program, school, and arts centre. He is a co-developer of the Natural Language Toolkit (NLTK), co-founder of the Open Language Archives Community (OLAC), founder of the ACL Anthology, and director of the Aikuma Project. He has held academic appointments at the universities of Edinburgh, Pennsylvania, UC Berkeley, and Melbourne, and is now professor at Charles Darwin University, in Darwin, Australia.
Guy Van den Broeck, PhD
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
Artificial Intelligence Can Learn from Data. But Can It Learn to Reason?(Talk)
Bing Liu, PhD
Bing Liu is a distinguished professor at the University of Illinois at Chicago. He received his Ph.D. in AI from the University of Edinburgh. His current research interests include continual/lifelong learning, lifelong learning dialogue systems, open-world learning, natural language processing, and machine learning. His previous research interests include sentiment analysis, fake review detection, and Web data mining. He has published extensively in prestigious conferences and journals and authored four books: one about lifelong/continual learning, two about sentiment analysis, and one about Web mining. Three of his papers have received Test-of-Time awards and another one received Test-of-Time honorable mention. Some of his works have also been widely reported in the popular and technology press internationally. He served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading conferences. He is the winner of 2018 ACM SIGKDD Innovation Award, and is a Fellow of AAAI, ACM, and IEEE.
Continual Learning of Natural Language Processing Tasks(Talk)
Meg Kurdziolek, PhD
Meg is currently the Lead UXR for Intrinsic.ai, where she focuses her work on making it easier for engineers to adopt and automate with industrial robotics. She is a “Xoogler”, and prior to Intrinsic worked on the Explainable AI services on Google Cloud. Meg has had a varied career working for start-ups and large corporations alike, and she has published on topics such as user research, information visualization, educational-technology design, voice user interface (VUI) design, explainable AI (XAI), and human-robot interaction (HRI). Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction.
Joe Hellerstein, PhD
Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”.
Hellerstein is a co-founder of Aqueduct, which is bringing new open source technology for Prediction Infrastructure to market. Previously he co-founded Trifacta, the pioneering company in Data Preparation, where he served as founding CEO and Chief Strategy Officer. Hellerstein has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Datometry and Acryl Data.
Jennifer Dawn Davis, PhD
Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.
Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)
Aaron Roth, PhD
Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science, in the Computer and Information Sciences department at the University of Pennsylvania, with a secondary appointment in the Wharton statistics department. He is affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. He is also an Amazon Scholar at Amazon AWS. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and research awards from Yahoo, Amazon, and Google. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.” Together with Michael Kearns, he is the author of “The Ethical Algorithm”.
Sophia Liu, PhD
Sophia Liu is a Senior Data scientist at Netflix. She leads the data science initiatives for Netflix games offerings. She specializes in online controlled experimentation (A/B tests), causal inferences and analytics. Before Netflix, she was a senior data scientist in Analysis and Experimentation (A&E) team at Microsoft. Dr. Liu received her M.S. and PhD degrees in Electrical Engineering from Columbia University and Northwestern University in 2012 and 2016, respectively. During her graduate study, she has won two best paper awards out of 14 international publications and conducted internships in Bell Labs, Cisco and Alliance Data Systems.
5 Things We Have Learned From Continuous Explore Exploit Applications at Netflix(Talk)
Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)
Nadia Fawaz, PhD
Nadia Fawaz is a Senior Staff Applied Research Scientist and the Technical Lead for Inclusive AI at Pinterest. Her research and engineering interests include machine learning for personalization, AI fairness and data privacy, and her work aims at bridging theory and practice. She was named one of the 100 Brilliant Women in AI Ethics 2021, her work on Hair Pattern Search was recognized in the AI and Data category on Fast Company’s World Changing Ideas 2022 list with an honorable mention, and her work on inclusive AI was featured in many news outlets, including The Wall Street Journal, Fast Company, Vogue Business and CBS. She was a winner of the ACM RecSyS challenge on Context-Aware Movie Recommendations CAMRa2011 and her 2012 UAI paper was featured in an MIT TechReview article as “The Ultimate Challenge For Recommendation Engines”. Earlier, she was a Staff Software Engineer in Machine Learning and the Tech Lead for the job recommendation AI team at LinkedIn, a Principal Research Scientist at Technicolor Research lab, and a postdoctoral researcher at the Massachusetts Institute of Technology, Research Laboratory of Electronics. She received her Ph.D. in 2008 and her Diplome d’ingenieur (M.Sc.) in 2005 both in EECS from Telecom ParisTech and EURECOM, France. She is a member of the IEEE and of the ACM.
Malte Pietsch
Malte Pietsch is CTO & Co-Founder at deepset. His current focus is on building deepset Cloud – a SaaS platform for developers to build, deploy and operate modern NLP pipelines. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset he worked as a data scientist for multiple startups. He is an active open-source contributor and author of the NLP framework Haystack.
Building Modern Search Pipelines with Haystack, Large Language Models and Hybrid Retrieval(Talk)
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
Why You Should Attend the Leading Data Science Conference
HANDS-ON TRAINING
Build job-ready skills and stay up-to-date with the latest advances in machine learning, NLP, data analytics, responsible AI, and more with ODSC West’s expert-led, immersive, training sessions.
With 300 hours of content, the conference features a wide range of sessions for data scientists at every level, from beginner to expert.
NETWORKING
Connect with and learn from thousands of your peers and data science experts during ODSC West’s numerous in-person and virtual events. Meet with our expert speakers to ask questions and continue the discussion during Meet the Speaker and Book Signing events. Or, set a goal to meet as many of your peers as possible at the ODSC Networking Reception.
AI EXPO AND DEMO HALL
Meet representatives from some of the leading AI startups and companies at the AI Expo and Demo Hall. Visit their booths, or see their products demoed live to learn about the latest advancements in AI in enterprise and discover how to build AI better in your organization.
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Co-located with ODSC West, the Ai X Summit brings together the leading practitioners, innovation experts, and business professionals who are driving the advancement of AI across a range of industries.
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