Oct 31st – Nov 3rd, 2023
MLOps & Data Engineering
Focusing on the practice, management, workflows and dataops of data science
Track Sponsored by: 
Understand the MLOps & Practice of Data Engineering in the Real World
As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices increases. The challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI are a few of the issues organizations wrestle with.
Learn best practices for effective data science management
Sessions in this broad focus area will look at uses cases, best practices, and stories from the field to show how to effectively incorporate data science practice into the wider business process. This focus area will look beyond data sourcing and modeling towards the many challenges teams need to overcome to effectively apply data science in their organization.
Some of Our Previous MLOps & Data Engineering Speakers
Yaron Haviv
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies 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 platform with over 4,000 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also 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. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.
From AutoML to AutoMLOps(Track Keynote)
Diego Klabjan, PhD
Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics, and the Deep Learning Lab. His expertise is focused on data science and deep learning with a concentration in finance, insurance, and healthcare. Professor Klabjan has led projects with large companies such as The Chicago Mercantile Exchange Group, Intel, General Motors and many others, and he is also assisting numerous start-ups with their analytics needs. He is also a founder of Opex Analytics.
MLOps for Deep Learning(Talk)
Roger Dev
Roger is a Senior Architect leading the Machine Learning and Analytics Library team at LexisNexis Risk Solutions. Roger has been involved in the implementation and utilization of machine learning and AI techniques for many years, and he has more than 20 patents in diverse areas of software technology.
Open-source Data Curation and Governance for Large and Growing Data Lakes(Talk)
David Alsabery
David has over 20 years of experience in the fields of data, AI and enterprise cloud. He has led teams for EMC Dell, Hitachi and Cisco, working with some of the most innovative companies in the world in both classified and commercial environments. Today, David acts as the Western Regional Director at Iguazio, working with Enterprise customers to help them bring their data science initiatives to life. David is passionate about applying MLOps principles to real-world AI projects, on-premise, in multi-cloud environments, on a SCIF or all of the above. When he’s not working with customers on AI projects, he volunteers at the Salvation Army and Rotary International. He and his wife have twins – a boy and a girl, as well as a 94lb/43kg Labrador that eats everything.
Nick Schenone
Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.
Building an AI App in Under 20 Minutes Using OS MLOps tool MLRun(Demo Talk)
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)
Frank Zickert, PhD
Frank Zickert is Quantum machine learning engineer and the author of Hands-On Quantum Machine Learning With Python. He teaches quantum machine learning in an accessible way to help those without a degree in math or physics to get started in the field.
In his research, Frank strives to use quantum machine learning to advance the field of knowledge graph-based natural language processing. He is also the Chief Technology Officer of Ihr MPE B+C where he supports medical physicists to provide radiation protection services for clinical customers. Previously he worked at Aperto-An IBM Company and Deutsche Bank.
Frank earned his Ph.D. in Information Systems Development from Goethe University Frankfurt am Main, Germany.
Getting Started With Quantum Bayesian Networks in Python and Qiskit(Tutorial)
Martin Shell
Martin has over 30 years of experience in Data Science, AI, Decision Optimization. He worked as Consulting Project Manager, Technical Sales, Data Scientist with organizations including ILOG, IBM, Manhattan Associates, Emptoris. He has strong modeling skills in constraint programming, mathematical programming, machine learning. He is skilled in C++, Java, Python. Martin’s main objective is to help organizations identify and deploy analytics that maximize ROI. He was selected as INFORMS Franz Edelman Award finalist. He has studied M.S. in Operations Research from Massachusetts Institute of Technology.
Turning your Data/AI algorithms into full web apps in no time with Taipy (Demo Talk)
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
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.
Swasti Kakker
Swasti Kakker is a senior software development engineer on the data analytics and infrastructure team at LinkedIn, where she worked on the design and implementation of Darwin – a hosted Jupyter notebook solution. She has worked on features like scheduling notebooks based on a cron expression, creating publishable reports from executions of a notebook, introducing Language servers in notebooks and integrating notebooks with various apps at LinkedIn. She works closely with stakeholders to understand the expectations and requirements of the platform that would improve developer productivity. Her passion lies in increasing and improving developer productivity by designing and implementing scalable platforms. She has also spoken previously at international conferences like Grace Hopper, Orlando and O’reilly Strata, New York in 2019.
Unified Data Science Platform for Accelerating Data Insights(Talk)
Danny Chiao
Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a Bachelor’s degree in Computer Science from MIT.
Building Production-Ready Recommender Systems with Feast(Talk)
Adam Breindel
Adam Breindel consults and teaches widely on Apache Spark and other technologies. Adam’s experience includes work with banks on neural-net fraud detection, streaming analytics, cluster management code, and web apps, as well as development at a variety of startup and established companies in the travel, productivity, and entertainment industries. He is excited by the way that Spark and other modern big-data tech remove so many old obstacles to system design and make it possible to explore new categories of interesting, fun, hard problems.
Yegna Jambunath
Yegna Jambunath is a Researcher at Centre for Deep Learning, Northwestern University. Yegna has six years of total work experience with four years of industry focused research experience in ML and Data Science. His areas of interest are MLOps, ML in Healthcare and RL.
MLOps for Deep Learning(Talk)
Manu Ram Pandit
Manu Ram Pandit is a Staff software engineer on the data analytics and infrastructure team at LinkedIn, where he’s influenced the design and implementation of hosted notebooks, providing a seamless experience to end users. Manu has worked on setting up multiple features in the platform like sharing/choosing custom docker environments & recently is involved with visualization efforts to effectively view big data visualizations.He works closely with customers, engineers, and product to understand and define the requirements and design of the system. He has extensive experience in building complex and scalable applications. Previously, he was with Paytm, Amadeus, and Samsung, where he built scalable applications for various domains.
Unified Data Science Platform for Accelerating Data Insights(Talk)
Chase Christensen
Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the “non-negotiables” are enforced to provide the best return on their production models.
How Far Left Can You Shift? The Tension Between Data Science and ML Engineering(Talk)
Personal to Product to Platform: Reporting Your Results with Kubeflow(Demo Talk)
Souheil Inati, PhD
Souheil is the Head of Field Data Science at Arrikto where he helps build machine learning solutions for clients. Previously, Souheil worked at Freddie Mac and Capital One where he built models and machine learning platforms. Prior to becoming a data scientist, he spent 15 years in academia working on MRI and Brain Imaging. Souheil holds a BS and PhD in Physics from Yale and MIT respectively.
How Far Left Can You Shift? The Tension Between Data Science and ML Engineering(Talk)
Personal to Product to Platform: Reporting Your Results with Kubeflow(Demo Talk)
Sandeep Agrawal, PhD
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
Bob Foreman
Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.
Relational Dataset Analytics for Clear Customer Insights(Workshop)
What You’ll Learn
Data science has many focus areas. The goal of this track is to accelerate your knowledge of data science through a series of introductory level training sessions, talks, tutorials and workshops on the most important data science tools and topics.
Experimentation to Production
Agile Data Science
Data Science Architecture
Runtime Pipelines
Model Monitoring & Auditing
Automated Machine Learning
Debugging Machine Mearning
Kubeflow and Kubernetes
Distributed Computing
Data Science Workflows
Data Provenance & Governance
and many more…
Why Attend?
Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics
With numerous introductory level workshops, you get hands-on experience to quickly build your skills
Post-conference, get access to recorded talks online and learn from over 100+ high quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science practice and management
Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today
Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels
Network at our numerous lunches and events to meet with data scientists, enthusiasts, and business professionals
Get access to other focus area content, including machine learning & deep learning, data visualization, and much more
Who Should Attend
Data Science is cross industry and cross enterprise, impacting many different departments across job roles and functions. This track is not only for data scientists of all levels but for anyone interested in the practice and management of data science, including:
Data scientists moving beyond model experimentation looking to understand production workflow
Data scientists seeking to improve the overall practice of management and development
Anyone interested in understanding better collaborative and agile management techniques as applied to data science
Business professionals and industry experts looking to understand data science in practice
Software engineers and technologists who need to work with data science workflows and understand the unique requirements of these systems
CTO, CDS, and other managerial roles that require a bigger picture view of data science
Technologists in the field of MLOps, databases, project management and others looking to break into data science
Students and academics looking for more practical applied training in data science tools and techniques
ODSC WEST 2023 - Oct 31st – Nov 3rd
REGISTER YOUR INTERESTODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!