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.

Technologies/Topic ideas: Data strategy, governance, lakehouse implementations, cost optimizations, organizing and enabling teams, data and AI roadmap, operating at scale

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 SQL analytics, BI, and visualization, the Summit audience would love to learn from you. 

Technologies/Topic ideas: ANSI SQL, Redash, Databricks SQL, Tableau, Power BI, visualization techniques, Spark SQL and DataFrames, data integration, data warehouses and analytics

Modern data engineering is critical for efficient data processing and cost reduction in every business. It involves ingesting and transforming various types of raw data into quality data, supporting analytics and machine learning. Share best practices in building pipelines, including real-time data streaming, which is crucial for immediate decision-making, accurate forecasting or enhancing customer experiences. This track is for you to share your experience in implementing real-time pipelines and how simplifying orchestration can drive the efficacy of real-time solutions.

Technologies/Topic ideas: Data pipelines, orchestration, CDC, medallion architecture, Delta Live Tables, dbt, Databricks Workflows, ETL/ELT, DataOps, Parquet, Apache Spark™ internals, Apache Spark Structured Streaming, real-time ingestion, real-time ETL, real-time ML, real-time analytics and real-time applications

The architectural decisions you make for your core data platform affect the reliability, performance and scalability of your data engineering, analytics and AI. The lakehouse platform combines the best elements of data lakes and data warehouses to help you reduce costs and deliver on your data and AI initiatives faster. Come share your experiences in adopting the lakehouse. This track is for you to talk about adopting data lakehouses, extending the lakehouse by building on and contributing to the open source technologies underpinning the lakehouse, migrations from data lakes and data warehouses, as well as integrating lakehouses with other data platforms.

Technologies/Topic ideas: Lakehouse architecture, Delta Lake, Photon, platform security and privacy, serverless, cost management, data warehouse, data lake, Apache Iceberg, Data Mesh, Unity Catalog

Operationalizing and productionalizing AI/ML projects at scale to affect business impact has unique challenges, such as managing real-time infrastructure that scales with demand, ensuring reliability and monitoring performance. Share your organization’s approach to scaling AI/ML projects in production and about how you are applying MLOps/LLMOps practices across the end-to-end machine learning lifecycle. 

Technologies/Topic areas: MLOps, feature stores, organizational ML, MLflow, serving and more

In the age of AI, governance, security and compliance for data and AI 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. For this track, we invite you to share best practices, frameworks, processes, roles, policies, standards for data and AI governance for both structured and unstructured data, and AI assets across cloud platforms. 

Technologies/Topic ideas: Data governance, multicloud, Unity Catalog, security, compliance, privacy, AI governance

Innovations in generative AI have unlocked new possibilities for organizations, and the ideal platform enables them to use their enterprise data to deliver unique generative AI-powered experiences for their users. The ideal platform allows customers to simply, securely and cost-effectively build and control their generative AI solutions, including the models and data. Share your experience building innovative and high-impact generative AI use cases, large-scale AI model training, deployment, real-time monitoring, and flexibility to use future generative AI innovations with the lakehouse.

Technologies/Topic ideas: LLMs, vector search, feature serving, Delta Lake, MosaicML, AutoML, Lakehouse Monitoring, Unity Catalog, Model Serving, MLflow AI Gateway, curated models, LLMOps, gen AI regulation, privacy and security

Data sharing is accelerating innovation as enterprises seek to easily and securely exchange data with their customers, partners, suppliers and internal lines 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 and collaboration, Delta Sharing, data cleanliness, data clean rooms, data marketplace

AI/machine learning continues to disrupt industries and accelerate business outcomes — across use cases and industries. Share with us how your company is applying AI/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

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

• Talk details including:
  • Proposed title
  • Presentation overview
  • Level of difficulty of your talk: Beginner (just getting started), Intermediate (familiar with concepts and implementations) and Advanced (expert)
• Speaker details including:
  • Speaker(s): biography, headshot
  • A video or a YouTube link of you speaking. If you don’t have a previous talk, please record yourself explaining your suggested talk.

• 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 pitches
• Improve the proposal’s chances of being accepted by using jargon-free language 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 or 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 your presentation 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.

When applying to speak at Data + AI Summit, you must submit a photo of yourself that can 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.