March 30th – April 1st, 2021

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 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 Current Data Engineering & MLOps Speakers

Open Source Relations ManagerVertica

Paige Roberts

In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.

Director of Data Science and EngineeringSalesforce

Sarah Aerni, PhD

Sarah Aerni is a Senior Manager of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.

 

Software EngineerSaturn Cloud

James Lamb

James Lamb is a software engineer at Saturn Cloud, where he works on a managed data science platform built on Dask and Kubernetes. Before Saturn Cloud, James worked on industrial internet of things (IIoT) problems as a data scientist at AWS and Chicago-based Uptake. He is a core maintainer on LightGBM, and has contributed on other open source data science and data engineering projects such as XGBoost and prefect. James holds Masters degrees in Applied Economics (Marquette University) and Data Science (University of California, Berkele

Scaling Machine Learning with Dask(Tutorial)

Technical Lead, PartnershipsSuperconductive

Sam Bail, PhD

Sam Bail is a data professional with a passion for turning high quality data into valuable insights. Sam holds a PhD in Computer Science and has worked for several data-focused startups. In her current role as Engineering Director at Superconductive, she works on “Great Expectations”, an open source Python library for data validation and documentation.

Building a Robust Data Pipeline with the “dag Stack”: dbt, Airflow, and Great Expectations(Talk)

Some Previous 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.

It’s a Breeze to Contribute to Apache Airflow(Training)

Introduction to Apache Airflow(Talk)

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)

Senior Cloud Advocate LeadMicrosoft

Francesca Lazzeri, PhD

Francesca Lazzeri, PhD is a machine learning scientist, author, and speaker. She leads an international team of data scientists, developers and cloud advocates at Microsoft. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit.

She is also a board member of Microsoft “Women@NERD” association, data science mentor at the Massachusetts Institute of Technology and Columbia University, and active member of the AI community.

Training and Operationalizing Interpretable Machine Learning Models(Talk)


Click Here For Full Lineup
2021 Speakers
See all our talks and hands-on workshop and training sessions
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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 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 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 2021 | March 30th – April 1st

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