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JUNE 26-29, 2023
SAN FRANCISCO + VIRTUAL
Attend Live

Agenda
Call for Presentations
Sponsors
FAQ
2022 On demand

June 26–29, 2023


DATA + AI SUMMIT 2023


CALL FOR PRESENTATIONS

Apply to speak


Do you have an innovative story or a lakehouse case study to share? Have you
built new features in popular open source technologies? How about tips and
tricks, how-tos and best practices? If so, we want you on the Data + AI Summit
stage. Submit today to share your expertise with the data community!

Please submit before January 13, for a chance to be featured at the global
hybrid event.




EVENT DETAILS AND INTRODUCTION

Data teams overcome challenges by building data pipelines, using advanced
analytics and developing machine learning models. These challenges often span
across disciplines to incorporate multiple data types, technologies, and tools —
this is the driver of data lakehouse adoption.   

 

Are you a practitioner solving data, analytics and AI challenges using Apache
Spark™, Delta Lake, MLflow, TensorFlow, PyTorch, Scikit-learn, BI and SQL
analytics, real time streaming, deep learning and machine learning frameworks?
If so, we invite you to share your experience with our global Summit community.

 

Draft your proposal for a 15-minute lightning talk, 40-minute session or
90-minute technical deep dive about how you are simplifying data, analytics and
AI challenges. Share your expertise with the largest gathering of data and AI
professionals. 


Submit today!

 

JOIN OUR LINEUP OF PAST SPEAKERS

Natalia Baryshnikova

Head of Product, Confluence Experience

Atlassian

Reynold Xin

Chief Architect

Databricks

Elpida Ormanidou

VP of Analytics and Insights

PetSmart

Tristan Handy

CEO and Founder

dbt Labs

Zhamak Dehghani

Director of Emerging Technologies, Thoughtworks

Thoughtworks

Ganesh Jayaram

Chief Information Officer

John Deere

Celine Xu

lead data scientist

H&M group

Jude Ken-Kwofie

Principal Software Engineer

HSBC

Duan Peng

Senior Vice President, Global Data & AI

Warner Bros. Discovery

Sol Rashidi

Chief Analytics Officer

Estee Lauder

Ali Ghodsi

Co-founder and CEO, Databricks; Original Creator of Apache Spark™

Databricks

Jacqueline Bilston

Software Developer

Yelp

Andrew Ng

Founder and CEO of Landing AI and DeepLearning.AI

DeepLearning.AI, Landing AI

Daphne Koller

CEO and Founder

insitro

Christopher Manning

Professor of Computer Science and Linguistics

Stanford University

Hilary Mason

Co-founder and CEO

Hidden Door

Matei Zaharia

Co-founder and Chief Technologist, Databricks; Original Creator of Apache Spark™
and MLflow

Databricks

Chenya Zhang

Senior Software Engineer

Apple

Tarika Barrett

CEO

Girls Who Code

Peter Norvig

Pioneer in AI and author of best-selling textbook, Artificial Intelligence: A
Modern Approach

Stanford's Human-Centered AI Institute and Google Inc

Natalia Baryshnikova

Head of Product, Confluence Experience

Atlassian

Reynold Xin

Chief Architect

Databricks

Elpida Ormanidou

VP of Analytics and Insights

PetSmart

Tristan Handy

CEO and Founder

dbt Labs

Zhamak Dehghani

Director of Emerging Technologies, Thoughtworks

Thoughtworks

Ganesh Jayaram

Chief Information Officer

John Deere

Celine Xu

lead data scientist

H&M group

Jude Ken-Kwofie

Principal Software Engineer

HSBC

Duan Peng

Senior Vice President, Global Data & AI

Warner Bros. Discovery

Sol Rashidi

Chief Analytics Officer

Estee Lauder

Ali Ghodsi

Co-founder and CEO, Databricks; Original Creator of Apache Spark™

Databricks

Jacqueline Bilston

Software Developer

Yelp

Andrew Ng

Founder and CEO of Landing AI and DeepLearning.AI

DeepLearning.AI, Landing AI

Daphne Koller

CEO and Founder

insitro

Christopher Manning

Professor of Computer Science and Linguistics

Stanford University

Hilary Mason

Co-founder and CEO

Hidden Door

Matei Zaharia

Co-founder and Chief Technologist, Databricks; Original Creator of Apache Spark™
and MLflow

Databricks

Chenya Zhang

Senior Software Engineer

Apple

Tarika Barrett

CEO

Girls Who Code

Peter Norvig

Pioneer in AI and author of best-selling textbook, Artificial Intelligence: A
Modern Approach

Stanford's Human-Centered AI Institute and Google Inc




THEMES AND TOPICS

We have expanded this year’s session tracks and are excited to offer the
opportunity for the global data, analytics and AI community to share and learn
more from one another than ever before. 2023 topics include:

DATA LAKEHOUSE ARCHITECTURE

The architectural decisions you make for your core data platform affect the
reliability, performance and utility of your data analysis, data science and
machine learning. This track is for you to share your experiences adopting data
lakehouses and migrations from data lakes, data warehouses, and data lakehouses;
as well as integrating lakehouses with other data platforms. 

Technologies/Topic ideas: Lakehouse Architecture, Delta Lake, Photon, Platform
Security & Privacy, Severless, Administration, Data Warehouse, Data Lake, Apache
Iceberg, Data Mesh

DATA GOVERNANCE

Data governance, security, and compliance are critical because they help
guarantee that all data assets are maintained and managed securely across the
enterprise and that the company is in compliance with regulatory frameworks.
This track is for you to share best practices, frameworks, processes, roles,
policies, and standards for data governance of structured and unstructured data
across clouds.


Technologies/Topic ideas: Data Governance, Multi-Cloud, Unity Catalog, Security,
Compliance, Privacy

DATA SHARING

Data sharing is accelerating innovation in the digital economy as enterprises
wish to easily and securely exchange data with their customers, partners,
suppliers and internal line of business to better collaborate and unlock value
from that data. Share best practices for making data available across data
platforms and clouds, methods to avoid replication and lock-in, and the
distribution of data products through marketplaces.


Technologies/Topic ideas: Sharing & Collaboration, Delta Sharing, Data
Cleanliness, Data Cleanrooms, Data Marketplace

DATA ENGINEERING

Modern data engineering is critical for enterprises seeking to optimize data
processing and reduce costs. Show how you use a combination of systems and
processes that ingest, orchestrate, and transform raw data into high-quality
information to support use cases such as analytics and machine learning. Dive
into best practices for data architectures, software engineering, ETL, data
management, data quality, DataOps and orchestration.


Technologies/Topic ideas: Data pipelines, orchestration, CDC, medallion
architecture, Delta Live Tables, dbt, Databricks Workflows, data munging,
ETL/ELT, lakehouses, data lakes, DataOps, Parquet, Data Mesh, Apache Spark
internals.

DATA STREAMING

The world operates in real-time and today’s organizations need to react
instantly as events unfold. Data streaming unlocks real-time ingestion,
analytics, machine learning and applications. How have you enabled faster
decision making, more accurate predictions, and improved customer experiences
with data streaming? Share best practices for implementing real-time data
pipelines with your favorite tools and languages, your experiences reducing
complexity for real-time data workflows and eliminating silos to support all
your real-time use cases.


Technologies/Topic ideas: Apache Spark Structured Streaming, real-time
ingestion, real-time ETL, real-time ML, real-time analytics, and real-time
applications, Delta Live Tables.

DSML: PRODUCTION ML/MLOPS

Operationalizing and productionalizing machine learning projects at scale to
affect business impact has unique challenges. Tell us your organization’s
approach to scaling ML in production  - how you are applying MLOps practices
across the end-to-end machine learning lifecycle from feature engineering and
experimentation to model deployment and monitoring in production applications? 

 

Technologies/Topic areas: MLOps, Feature Stores, Organizational ML, MLflow, MLR,
Serving and more

 

DSML: ML USE CASES/TECHNOLOGIES

Machine learning continues to disrupt industries and accelerate business
outcomes - across use cases and industries. Share with us how your company is
applying ML to solve business challenges, what specific technologies you are
using, what lessons you have learned, and how you’re integrating data science
with the rest of your organization. 

 

Technologies/Topic areas: PyTorch, TensorFlow, Keras, XGBoost, Fastai,
scikit-learn, Python and R ecosystems, Deep Learning, Notebooks, and more.

 

DATA WAREHOUSING, ANALYTICS AND BI

Data without analysis is wasted. Often, that analysis comes in the form of
reports and visualization that allow companies to make higher-quality decisions.
If you have experience building analysis pipelines, integrations, tooling or
infrastructure for data warehousing, SQL analytics, BI, and visualization, the
Summit audience would love to learn from you.

Sample Technologies/Topic ideas: ANSI SQL, Redash, Databricks SQL, Tableau,
Power BI, visualization techniques, Spark SQL and DataFrames, Data
warehouse-based analytics

RESEARCH

Although the fields of Data and AI have advanced a lot in the last 10 years,
there are plenty of exciting problems to be solved and systems to optimize.
Dedicated to academic and advanced industrial research, we want talks on
large-scale data analytics and machine learning systems, the hardware that
powers them (GPUs, I/O storage devices, etc.) as well as applications of such
systems for use cases like genomics, astronomy, image scanning, disease
detection, vaccine research, etc.

DATA STRATEGY

Implementing a successful data strategy is more complex than ever. Choosing a
data lakehouse platform is just the first step. True adoption requires a
thoughtful approach to people and processes. Share your experience and insights
on aligning goals, identifying the right use cases, organizing and enabling
teams, mitigating risk, and operating at scale to find more success with data,
analytics, and AI.




DETAILS & REQUIREMENTS

To help with your submission, our team has outlined guidelines and best
practices to reference when writing your proposal.

REQUIREMENTS

A maximum of 2 speakers will be accepted per presentation. You’ll need to
include the following information for each proposal:

 * Proposed title and presentation overview
 * Level of difficulty of your talk: Beginner (just getting started),
   Intermediate (familiar with concepts and implementations), and Advanced
   (expert)
 * Speaker(s): Biography, Headshot
 * Speaking sample (video or YouTube link). If you don’t have a speaking sample,
   please record yourself explaining your suggested topic.

TIPS FOR A SUCCESSFUL PROPOSAL

Help us understand why your presentation is the right one for Summit. Please
keep in mind that this event is for global data, analytics and AI professionals.
All presentations and supporting materials must be insightful and inclusive.
Here are some best practices to reference when writing your proposal: 

 * Give your proposal a simple and straightforward title.
 * Clearly outline the value and benefit your proposal will provide to other
   data practitioners. 
 * Make sure to provide original ideas with world scenarios and use cases. Get
   technical — show code snippets or some demonstration of working code.
 * Limit the scope of your proposal to one of the allotted timeframes (15
   minutes, 40 minutes or 90-minute deep dive).  
 * Keep your proposal free of product, marketing or sales pitch content —
   jargon-free will increase your chance of acceptance. 
 * Does your presentation have the participation of a woman, person of color, or
   member of another group often underrepresented at a tech conference?
   Diversity is one of the factors we seriously consider when reviewing
   proposals as we seek to broaden our speaker roster.

SUBMITTING YOUR PHOTO

When applying to speak at Data + AI Summit, we ask that you submit a photo to be
used in promotional materials, such as on the event website.

 

To help make sure we’re able to present you in the best possible light, your
submitted photo must follow these requirements:

 * It must be a recognizable photograph of you that includes your full
   head/face. It should depict you from the chest up with your head toward the
   center of the frame. Imagine it like a LinkedIn profile image.
 * It must be a minimum of 500x500px in size and in square aspect ratio.
 * Your photo must be in full color.

If your image does not meet these requirements, you may be asked to provide a
replacement.

Share your knowledge

Apply to speak
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Apache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache
Software Foundation. The Apache Software Foundation has no affiliation with and
does not endorse the materials provided at this event.






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