Spark + AI Summit Europe will be from October 15-17, 2019. The call for presentation is now closed.
Today data and AI work together. The best AI applications and machine learning models require a massive amount of processing power and data to build sophisticated models. We are looking for deep, technical content in these areas.
Based on past attendance, we expect an audience of data scientists and engineers, developers, researchers, and machine learning experts. We seek speakers at this awesome summit and venue in Amsterdam to share developer-focused and technical talks on a broad set of themes and topics for you to choose from:
Do you have big ideas, compelling stories or cases studies to share on these themes and topics, tips and tricks, how-to-and-whys, and best practices with community members embarking on the Spark + AI journey?
Have you built complex data pipelines for ETL using Apache Spark along with popular streaming engines? Or worked on complex data pipeline for productizing machine learning models at scale? Have you built deep learning models with popular frameworks that have made a difference?
If so, pen down your proposal for a 40-minute talk, 80-minute technical deep dive, or 90-minute tutorial on how-to-and-why. We’d love to put your ideas, case studies, best practices, and technical knowledge in front of the largest gathering of Spark, AI, and big data professionals.
These are just guidelines and suggestions—we are open to your creativity.
In this developer-focused theme, presenters cover technical content across a wide range of topics ranging from Spark engine internals, Spark performance and optimizations, extending or using Spark APIs, Spark SQL, machine learning to streaming.
You will be able to categorize your talk into different sections including:
Please make sure to categorize your talk if you would like to include in a specific subtopic or category.
If you have an AI use case, case study or solved a specific problem in automating a process, device or an automaton; recognizing and analysing speech, video, image or text; improving conversational interfaces like chatbots and intelligent personal assistants or playing intelligent games—whether you used neural networks, natural language processing, rule-based engine—this thematic category is for your use case.
Share your journey of automation with the community and tell us what’s possible in this pervasive field helping innovate modern businesses.
You will be able to categorize your talk into different use case scenarios including:
Please make sure to categorize your talk if you would like to include in a specific subtopic or category.
As a class of machine learning algorithms, deep learning has fueled the development of AI and predictive analytic applications that learn from data or transferred knowledge.
If you have implemented a real-world application—in speech recognition, image and video processing, natural language processing, recommendation engines, ad tech or mobile advertising—using any of these frameworks outlined below and the techniques they offer, this category is for you.
Share your technical details and implementation with the community and tell us your gains, pains points, and merits of your solutions.
You will be able to categorize your talk into a different use of DL frameworks
How do you build and deploy machine learning models to a production environment? How do you manage an entire machine learning life cycle? How do update your model with new features? And what are the best practices and agile data architectures that data scientists and data engineers employ to productionize machine learning models?
Whether your model is a deep learning model or a Spark MLlib machine learning model, how do you experiment, track, and score your trained model with real-time data?
If you have answers to these questions, if you have addressed them in your design, implementation, and deployment schemes in production then we want to hear from you.
Share your technical details and model implementation and deployment with the community, and tell us your gains, pains points, and merits of your solutions.
As the name suggests, this topic will be an 80-min slot that allows a presenter to go deeper into the topic than the normal regular 40-min sessions allow. The session should be highly technical with some demonstration and code examples. For example Scalable Monitoring of GPU Usage with TensorFlow Models Using Prometheus, A Deep Dive into Query Execution Engine of Spark SQL, Easy, Scalable, Fault-Tolerant Stream Processing with Structured Streaming in Apache Spark or Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apache Spark are examples from previous summits.
This thematic category is not only restricted to Spark, though. It can cover deep learning practices and techniques too.
Dedicated to academic and advanced industrial research, we want talks spanning systems research involving and extending Spark + AI in use cases (e.g. genomics, GPUs, I/O storage devices, MPP, self-operating automatons, image scanning and disease detection in cancer etc.).
While Data Science is a broad theme and overlaps with deep learning, machine learning and AI, this thematic category spotlights the practice of data science using Spark, including SparkR. Sessions can cover innovative techniques, algorithms, and systems that refine raw data into actionable insight using visualization, statistics, feature engineering, and machine learning algorithms, from supervised and unsupervised learning.
This theme features use cases on how businesses deploy Apache Spark and the lessons learned. Talks offer an exploration into business use cases across industries, ROI, best practices, relevant business metrics, compliance requirements for specific industries, and customer testimonials.
For this thematic category, we seek speakers’ experiences in building complex data infrastructure for doing advanced data analytics using Apache Spark. In particular, we want to hear how you grappled with data quality issues and complexities in building end-to-end data pipeline: from ingestion to ETL to cleaning data for consumption downstream for machine learning models or other applications.
If you have answers to how you architected, implemented, monitored, and deployed these data pipelines or how you combined streaming data with historical data from myriad sources, then we want your stories.
This is a new track we have added dedicated to the broad and commanding discipline of data engineering for advanced analytics when grappling with massive amounts of data.
Some examples of tracks in this new theme: Scaling Apache Spark at Facebook, Migrating to Apache Spark at Netflix, or Scaling Apache Spark on Kubernetes at Lyft,
Spark users can easily install PySpark through PyPi and use it for writing scalable advanced analytics applications. This theme is dedicated to talks regarding the specific use of Python with scalable data not only in writing data science and machine learning applications but also in writing Spark applications. If you have a use case implemented in PySpark and you wish to share it with the Python user community, this thematic category is for you.
These 90-minute talks are designed to introduce concepts followed by hands-on exercises, in the instructor’s choice of the execution environment in notebooks, allowing attendees to have a hands-on experience to learn from exercises. They are not consigned only to Spark but can also cover machine learning techniques or building deep learning models using a particular framework, or showing how to use Structured Spark’s APIs for a use case.
Some examples of tutorials are Deep Learning and Modern NLP, Building Robust Production Data Pipelines with Databricks Delta, or Writing Continuous Applications with Structured Streaming PySpark API
You’ll need to include the following information for your proposal:
Help us understand why your presentation is the right one for Spark + AI Summit. Please keep in mind that this event is by and for professionals. All presentations and supporting materials must be respectful and inclusive.