Call for presentations
now open

Deadline extended to January 14, 2022



Call for Presentations Data + AI Summit 2022

Data + AI Summit, the world’s largest conference for the open modern data stack community, will bring together tens of thousands of data practitioners in person and online from June 27-30, 2022. All presenters will be expected to speak live from San Francisco, and your talks will be available online and promoted on our channels.

We invite you to share your expertise and stories with fellow data scientists, data engineers, data analysts and data leaders. Your solutions using the modern data stack are defined by open technologies that help deliver advanced data analytics, build data pipelines, and develop AI applications and machine learning models. Your experience solving these problems will be extremely valuable to your peers, whether you’re using technologies like Apache Spark™, Delta Lake and the lakehouse pattern, MLflow, TensorFlow, PyTorch, Scikit-learn, BI and SQL analytics, deep learning or machine learning frameworks.

Our global Summit community of over 50,000 would love to hear from you. So pen down your proposal for a 15-minute lightning talk, 40-minute session or 90-minute technical deep dive on how-to-and-why. We’d love to put your ideas, case studies or production use cases, best practices and technical knowledge in front of the largest gathering of Data + AI professionals. Submit your talk today and share your experience building and working on an open, modern data stack.

Themes and topics

Data scientists, data engineers, analysts, developers, researchers and ML practitioners all attend Summit to learn from the world’s leading experts on topics such as:

Data Lakes, Data Warehouses and Data Lakehouses

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 in those architectural trade-offs and migrations between architectures.

Data Engineering

Share your experience building robust data pipelines with both batch and real-time streaming data architectures. From data ingestion to cleaning to processing for analytics and ML, we know you have a tough job. Share your insight on the architectures, challenges and best practices you’ve learned along the way.

Technologies/topic ideas: Delta Lake, CDC, medallion architecture, DLT, DBT, data munging, ETL/ELT, lakehouses, data lakes, Apache Spark internals, Spark performance optimizations and more

Data Science, Machine Learning and MLOps

Recommendations and decisions in businesses and software are increasingly informed by data science, machine learning and deep learning. If you have real-world experience in these areas, help others learn through the data science analyses, machine learning and deep learning models you’ve built. Share your tips and tricks, triumphs and challenges.

We’re also looking for great sessions on productionizing machine learning projects and pipelines.

Technologies/topic ideas: MLflow, PyTorch, TensorFlow, Keras, XGBoost, Fastai, scikit-learn, Python and R ecosystems, MLlib and other Apache Spark for ML pipelines, trustworthy AI, explainable AI (xAI), model monitoring, model and concept drift and more

Data Analytics, BI and Visualization

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

Technologies/topic ideas: SQL, Redash, Tableau, Power BI, visualization techniques, Spark SQL and DataFrames, data integration

Data Security and Governance

How do we protect data from improper access by external and internal actors, safely share data with others, understand data lineage, and satisfy compliance needs? If you deal with challenges in data security and governance, we’d love to hear how you overcome those challenges through technology and business processes.

Technologies/topic ideas: Encryption, identity federation, data sharing, compliance controls, monitoring and auditing

Data + AI Industry Use Cases

Data analytics, machine learning and AI are having a profound impact on how organizations across industries are solving their toughest data challenges. In this track, we’ll explore how open source technologies, data analytics and AI are being applied to solve business challenges in the hottest industries, including topics like personalized healthcare, cyber threat protection, supply chain forecasting and fraud prevention.

If you have an interesting application of data analytics or AI in your business and want to share your journey of delivering data-driven innovation, then this thematic category is for you.

Recommendation: This talk type works best when presenting with a speaker from the technical side (e.g., data scientist) and someone from the line of business.


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, etc.

Technologies we are looking for:

Data / Data Engineering

  • Databricks
  • Apache Spark
  • Dask
  • PrestoDB
  • Flink
  • Ray
  • Delta Lake
  • LakeFS
  • Iceberg
  • dbt
  • Apache Arrow
  • Kubeflow
  • Airflow
  • Streaming APIs and infrastructure
  • Fivetran

Data Platforms

  • Data lakehouse
  • Data warehouse
  • Data lake

Analytics, Visualization and BI

  • Superset
  • Tableau
  • Microsoft Power BI
  • Google Looker
  • ThoughtSpot

BI/viz tools

  • Superset
  • Tableau
  • Microsoft Power BI
  • Google Looker
  • ThoughtSpot

Cloud / Cloud Data

  • Multicloud
  • AWS
  • Azure
  • Google Cloud Platform

Machine Learning and Deep Learning Frameworks and Integrations

  • scikit-learn
  • XGBoost
  • LightGBM
  • TensorFlow
  • PyTorch
  • Keras

Domain-Specific ML / DL Tools / Solutions

  • NLTK
  • SpaCy
  • BERT

Data Science and Decision-Making Tools / Solutions

  • pandas
  • scikit-learn
  • SciPy
  • PyMC3
  • TensorFlow Probability

Required information

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

  • Proposed title
  • Presentation overview
  • Suggested themes and topics from the thematic categories above
  • Speaker(s): Biography, headshot and mobile number
  • A video or a YouTube link of you speaking. If you don’t have a previous talk, please record yourself explaining your suggested talk
  • Keep the audience in mind: They are professional and already
    pretty smart.
  • Level of difficulty of your talk: Beginner (just getting started), Intermediate (familiar with concepts and implementations) and Advanced (expert)

Tips for submitting a successful proposal

Help us understand why your presentation is the right one for Summit. Please keep in mind that this event is by and for professionals. All presentations and supporting materials must be respectful and inclusive. Here is some advice on how to write a good conference proposal.

  • Be authentic. Your peers need original ideas in real-world scenarios, relevant examples and knowledge transfer.
  • Give your proposal a simple and straightforward title.
  • Include as much detail about the presentation as possible.
  • Keep proposals free of product, marketing or sales pitch.
  • Improve the proposal’s chances of being accepted by writing a jargon-free proposal that contains a clear value for attendees.
  • Keep the audience in mind: They are professional and already pretty smart.
  • Limit the scope: In 40 minutes, you won’t be able to cover “everything about framework X.” Instead, pick a useful aspect, a particular technique or walk through a simple program.
  • Your talk must be technical and show code snippets or some demonstration of working code.
  • Explain why people will want to attend and what they’ll take away from it.
  • Don’t assume that your company’s name buys you credibility. If you’re talking about something important that you have specific knowledge of because of what your company does, spell that out in the description.
  • 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.