Boston | April 30-May 3, 2019

Management, Practice & DataOps

Focusing on the practice, management, workflows and dataops of data science

Understand the Practice of Data Science in the Real World

Sponsored Track By

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.

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

Sessions on Management, Practice & DataOps Track

  • Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability

  • Talk: Adversarial Attacks on Deep Neural Networks

  • Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow

  • Workshop: Machine Learning for Digital Identity

  • Talk: Adding Context and Cognition to Modern NLP Techniques

  • Training: Good, Fast, Cheap: How to do Data Science with Missing Data

  • Workshop: Open Data Hub workshop on OpenShift

  • Talk: Practical AI solutions within healthcare and biotechnology

  • Training:  Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale

  • Workshop: Reproducible Data Science Using Orbyter

  • Talk: Combining millions of products into one marketplace using computer vision and natural language processing

  • See the whole schedule!

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

Some of Our Current Speakers

Haftan Eckholdt, PhD
Haftan Eckholdt, PhD

Chief Data Science & Chief Science Officer at

John Boersma, PhD
John Boersma, PhD

Director of Education at DataRobot

Julien Simon
Julien Simon

Principal Evangelist ML/AI EMEA at Amazon

Peter Wang
Peter Wang

CTO, Co-founder at Anacoda

Ben Vigoda
Ben Vigoda

CEO at Gamalon

Catherine Havasi, PhD
Catherine Havasi, PhD

AI Scientist | CEO & Co-Founder at MIT Media Lab | Luminoso

Christopher P. Bergh
Christopher P. Bergh

CEO, Head Chef at DataKitchen

Robert Christie
Robert Christie

Front End Engineer at S&P Global Market Intelligence

Mat Leonard, PhD
Mat Leonard, PhD

Instructional Designer at Kaggle

Gil Benghiat
Gil Benghiat

Co-founder at DataKitchen

Eric Estabrooks
Eric Estabrooks

Founder and VP of Cloud and Data Services at DataKitchen

Meina Zhou
Meina Zhou

Data Scientist at Capco

James Meickle
James Meickle

Site Reliability Engineer at Quantopian

Aleksandar Lazarevic, PhD
Aleksandar Lazarevic, PhD

VP of Advanced Analytics and Data Engineering at Stanley Black & Decker

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 2019 | April 30-May 3

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