Boston | April 13th – April 17th, 2020

Data Engineering & MLOps

Focusing on the practice, engineering, workflows and DataOps of data science
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Understand the 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 increase. 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 Current Data Engineering & MLOps Speakers

Head of Machine LearningDatatonic

Oliver Gindele, PhD

Oliver Gindele is head of machine learning at Datatonic. Oliver is passionate about using computer models to solve real-world problems. Working with clients in retail, finance, and telecommunications, he applies deep learning techniques to tackle some of the most challenging use cases in these industries. He studied materials science at ETH Zurich and holds a PhD in computational physics from UCL.

ML in Production – Serverless and Painless(Talk)

Software Engineer & Apache Airflow CommitterPolidea

Tomasz Urbaszek

Tomek is a Software engineer at Polidea, Apache Airflow committer and book lover. He fancies functional programming because he is graduated, mathematician. Every day he tries to make the world a better place.

Head of Developer Relations, AuthorAnyscale

Dean Wampler, PhD

Dean Wampler (@deanwampler) is an expert in streaming data systems, focusing on applications of ML/AI. He is Head of Evangelism at Anyscale.io, which is focused on distributed Python for ML/AI. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for building and running streaming data applications with Akka Streams, Apache Spark, Apache Flink, and Apache Kafka. Dean has written books for O’Reilly and contributed to several open-source projects. He is a frequent conference speaker and a co-organizer of several conferences and user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington.

Ray: A System for High-performance, Distributed Python Applications(Training)

Junior Principal Data EngineerQuantumBlack

Tom Goldenberg

Tom is a Junior Principal Data Engineer at QuantumBlack, a McKinsey Company. Prior to consulting, Tom was CTO and co-founder of Commandiv, a wealth management startup.

Kedro + MLflow – Reproducible and Versioned Data Pipelines at Scale (Workshop)

Director of AI ConsultingDataiku

Conor Jensen

Conor Jensen is an experienced Data Science executive with over 15 years working in the analytics space. He is the founder of Renegade Science, a Data Science strategy and coaching consultancy and works as a Customer Success Team Lead at Dataiku, helping customers make the most of their Data Science platform and guiding them through building teams and processes to be successful. He took his lumps building out data science teams and platforms at multiple large enterprises and does his best to help other companies not repeat his mistakes.

Apache Spark Community & Developer AdvocateDatabricks

Jules Damji

Jules S. Damji is an Apache Spark community and developer advocate at Databricks. He’s a hands-on developer with over 20 years of experience. Previously, he worked at leading companies such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, Verisign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a BSc and MSc in computer science and MA in political advocacy and communication from Oregon State University, California State University, and Johns Hopkins University, respectively.

Managing the Complete Machine Learning Lifecycle with MLflow(Training)

Senior Engineering ManagerFacebook

Mohamed Fawzy

Senior Software Engineering Manager – Facebook AI Infrastructure Mohamed Fawzy leads the Distributed AI Group at Facebook. The Distributed AI group is focused on scaling and product-ionizing machine learning training at Facebook scale by building platform and infrastructure to support large scale distributed training on heterogeneous hardware.

Distributed Training Platform at Facebook (Talk)

Head Of Engineering and Machine LearningArangoDB

Jörg Schad, PhD

Jörg Schad is Head of Development and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He is a frequent speaker at meetups, international conferences, and lecture halls.

Graph Powered Machine Learning(Training)

Principal Software EngineerTwilio

Scott Haines

Scott Haines is a full-stack engineer with a current focus on real-time, highly available, trustworthy analytics systems. He is currently working at Twilio as a Principal Software Engineer on the Voice Insights team, where he helped drive spark adoption and streaming pipeline architectures, and build out a massive stream-processing platform.

Prior to Twilio, he worked on writing the backend Java APIs for Yahoo! Games, as well as the real-time game ranking/rating engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo! working for Flurry Analytics where he wrote the alerts/notifications system for mobile.

Streaming Decision Intelligence and Predictive Analytics with Spark 3(Workshop)

Principal Software Engineer & Apache Airflow PMC MemberPolidea | Apache Software

Jarek Potiuk

After working in all the engineering/team/project management functions up to a CTO. As CTO he built software house 10-fold: from 6 to 60 people. After a few years of being the CTO, he decided to go back to a full-time engineering role and he works as a Principal Software Engineer in his company (and is super happy about it). Jarek has been working as an engineer in many industries – Telecoms, Mobile app development, Google, Robotics and Artificial Intelligence, Cloud and Open Source data processing. Jarek is currently PMC in the Apache Airflow project.

Co-Founder & Chief TechnologistDatabricks

Matei Zaharia, PhD

Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. He started the Apache Spark project during his PhD at UC Berkeley in 2009 and has worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow development effort at Databricks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award and several best paper awards.

Director Of EngineeringMobileIron

Yong Tang, PhD

Yong Tang is the Director of Engineering at MobileIron. His most recent focus is on data processing in machine learning. He is a maintainer and the SIG I/O lead of the TensorFlow project. He received the Open Source Peer Bonus Award from Google for his contributions to TensorFlow and is the author of the Kafka Dataset module in TensorFlow. In addition to TensorFlow, Yong Tang also contributes to many other projects for the open-source community. He is a maintainer of Docker, CoreDNS, and SwarmKit. Yong Tang received his PhD in Computer Science & Engineering at the University of Florida.

Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline(Workshop)


Click Here For Full Lineup
2020 Speakers

Focus Area: MLOps & Data Engineering, Machine Learning      Session: Talk 

ML in Production – Serverless and Painless

Oliver Gindele, PhD, Head of Machine Learning, Datatonic

Focus Area: MLOps & Data Engineering, Machine Learning      Session: Training

Managing the Complete Machine Learning Lifecycle With MLflow

Jules Damji, Apache Spark Community & Developer Advocate, DataBricks

Focus Area: MLOps & Data Engineering, Machine Learning      Session: Workshop

Streaming Decision Intelligence and Predictive Analytics With Spark 3

Scott Haines, Principal Software Engineer, Twilio

Focus Area: MLOps & Data Engineering      Session: Training

Graph Powered Machine Learning

Jörg Schad, PhD, Head Of Engineering and Machine Learning, ArangoDB

Focus Area: MLOps & Data Engineering      Session: Workshop

Kedro + MLflow – Reproducible and Versioned Data Pipelines at Scale

Tom Goldenberg, Junior Principal Data Engineer, QuantumBlack

Focus Area: MLOps & Data Engineering, Deep Learning      Session: Workshop

Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline

Yong Tang, PhD, Director Of Engineering, MobileIron

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

  • Data Science DevOps

  • Agile Data Science

  • Data Science Architecture

  • Runtime Pipelines

  • Model Monitoring & Auditing

  • Model Depreciation in Production

  • Manage Data Science in Your Organization

  • Collaborative Practices and Tools

  • Team Management

  • Data Science Workflows

  • Data Provenance & Governance

  • Best Practices & Uses Cases

  • Cross Industry & Cross Enterprise Challenges

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 up 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 post conference

Take time out of your busy schedule to accelerate your knowledge on the latest advances in data science practice and managment

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 DevOps, 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

Sign Up for ODSC EAST 2020 | April 13th – April 17th

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