San Francisco | Oct 28th - Nov 1st, 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

As data science extends its reach across the enterprise the need for better management, workflow, production and deployment practices has increased.  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 on how to effectively incorporate data science practice into the wider business process.  This focus area will look beyond data sourcing and modeling and towards the many challenges teams need to overcome to effectively apply data science in their organization.

What You’ll Learn

Data science is a practical field with many unique challenges, from managing data science projects to deploying to production environments. This  track will cover topics that range from team and project management to the new area of DataOps.

  • Experimentation to Production

  • Data science DevOps

  • Agile Data Science

  • Data Science Architecture

  • Runtime Pipelines

  • Model Monitoring & Auditing

  • Model deprecation in Production

  • Mange Data Science in Your Organization

  • Collaborative Practices and Tools

  • Team management

  • Data Science Workflows

  • Data Provenance & Governance

  • Best Practices & Uses Cases

  • Cross industry and cross enterprise challenges

Here are Samples of What You’ll Learn

  • Talk: Monitoring AI applications with AI

  • Talk: How to Go From Data Science to Data Operations

  • Talk: AI applications, best practices, and lessons learned in the automotive domain

  • Talk: Building an Application-Specific Intelligent Assistant

  • Talk: Conversational Interfaces in Customer Service

  • Workshop: AI in personal finance: More than just chatbots

  • 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 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 of 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 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 and collaborate 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


Bill Shander
Bill Shander

Founder at Beehive Media

Julien Simon
Julien Simon

Principal Evangelist ML/AI EMEA at Amazon

Mehrnoosh Sameki, PhD
Mehrnoosh Sameki, PhD

Technical Program Manager at Microsoft

Simon Goring, PhD
Simon Goring, PhD

Assistant Research Scientist at University of Wisconsin - Madison

Liang Wu, PhD
Liang Wu, PhD

Machine Learning Data Scientist at Airbnb

Stephanie Kirmer
Stephanie Kirmer

Senior Data Scientist at Uptake

Lars Hulstaert
Lars Hulstaert

Data Scientist at Microsoft

Alex Ratner
Alex Ratner

PhD Candidate at Stanford University

Reza Shiftehfar, PhD
Reza Shiftehfar, PhD

Engineering Manager at Uber Technologies Inc.

Sourav Dey, PhD
Sourav Dey, PhD

CTO at Manifold

Alex NG
Alex NG

Senior Data Engineer at Manifold

Steven Pousty, PhD
Steven Pousty, PhD

Director of Developer Relations at Crunchy Data

Benn Stancil
Benn Stancil

Chief Analyst at Mode Analytics

Brian Lucena, PhD
Brian Lucena, PhD

Consulting Data Scientist at Agentero

Sessions on Management, Practice & DataOps Track

  • Talk: Scientific Annotation: Using Graphs to Facilitate Interdisciplinary Science

  • Talk: Deploying AI for Near Real-Time Engineering Decisions

  • Workshop: Integrating Elasticsearch with Analytics Workflows

  • Talk: Guiding AI To Generate The Labels We Do Not Have With Active Learning

  • Training: Scalable Machine Learning with Kubernetes and Kubeflow

  • Training: From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact

  • Talk: Making the Best Possible Decisions with Evolutionary AI

  • See the whole schedule!

Who Should Attend

Data Science is cross industry and cross enterprise, impacting many different departments across job roles and functions.  This track is useful not only for data scientists of all levels, but anyone interested in the practice and management of data science; including:

  • Data scientists looking to move beyond model experimentional 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 the field of DevOps, databases, project management and other 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 WEST | Oct 28th - Nov 1st 2019

Register Now