Do you have some exciting generative AI, data science or machine learning insights? Would you like to share your tips, tricks and best practices? Maybe you’ve even crafted some epic features in open source tech? Well, our Data + AI Summit community, an incredible 70,000+ strong, can’t wait to learn from you.
The deadline for submissions is January 5, 2024.
Event Details and Introduction
The adoption of data lakehouses is driven by the need for modern, flexible and cost-effective data management and analytics solutions in today’s data-intense business environment.
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 Data + AI Summit community.
Draft your proposal for a 20-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.
Join our lineup. Past speakers include:
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 Strategy and Lakehouse Implementation
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 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 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
Data Engineering and Streaming
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
Data Lakehouse Architecture
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
DSML: Production ML/MLOps
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
DSML: AI/ML Use Case
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
Details and Requirements
To set up your submission for success, take a look through the requirements before applying. While we will review every application, complete and thorough submissions will be given priority over those that do not include the required information.
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.
Tips for a successful 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 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.
Submitting your photo
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.