Early last month, we announced our agenda for Spark + AI Summit 2018, with over 180 selected talks with 11 tracks and training courses. For this summit, we have added four new tracks to expand its scope to include Deep Learning Frameworks, AI, Productionizing Machine Learning, Hardware in the Cloud, and Python and Advanced Analytics.
These new tracks have fostered some unique AI-related submissions that require massive amounts of constantly updated training data to build state-of-the-art models as well as ways of using Apache Spark at scale.
For want of worthy experience—whether picking a restaurant to dine, electing a book to read, or choosing a talk to attend at a conference—often a nudge guides, a nod affirms, and a review confirms. Rebecca Knight suggests in Harvard Business Review “How to Get Most Out of a Conference” by listening to what experts have to say, being strategic with your time, and choosing the right sessions.
As the program chair of the summit, and to help you choose some sessions, a few talks from each of these new tracks caught my attention: All seem prominent in their promise, possibility, and practicality; all seem to suggest how data and AI engender the best of AI applications because of unified analytics. I wish to share them with you:
AI Use Cases and New Opportunities:
- From Genomics to NLP – One Algorithm to Rule Them All
- Deep Learning and Pest Management in Cranberry Farming – How we are Helping Save Thanksgiving
- The Rise Of Conversational AI
Deep Learning Techniques:
- Productionizing Credit Risk Analytics with LSTM-TensorSpark—A Wells Fargo Case Study
- How Neural Networks See Social Networks
- Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters
Productionizing Machine Learning:
- Near Real-Time Netflix Recommendations Using Apache Spark Streaming
- Deploying MLlib for Scoring in Structured Streaming
- Operationalizing Machine Learning—Managing Provenance from Raw Data to Predictions
Python and Advanced Analytics:
- Pandas UDF: Scalable Analysis with Python and PySpark
- Integrating Existing C++ Libraries into PySpark
- Building a Scalable Record Linkage System with Apache Spark, Python 3, and Machine Learning
Hardware in the Cloud:
- Optimizing Apache Spark Throughput Using Intel Optane and Intel Memory Drive Technology
- Apache Spark Acceleration Using Hardware Resources in the Cloud, Seamlessly
- Accelerated Apache Spark on Azure: Seamless and Scalable Hardware Offloads in the Cloud
Stay tuned for keynote announcements and favorite picks from other tracks by notable technical speakers from the community and big data practioners.
If you have not registered, use code “JulesChoice” for a 15% discount during registration. I hope to see you all there.