The Need for Speed: How the Automotive Industry Accelerates Machine Learning

While GPU-accelerated analytics have already radically accelerated the speed of training machine learning models, data scientists and analysts still grapple with deriving insights from these complex models to better inform decision-making. The key: Visualizing and interrogating black box models with a GPU-enabled architecture. Volkswagen and MapD will discuss how interactive, visual analytics are helping the automotive brand interactively explore the output of their ML models to interrogate them in real time, for greater accuracy and reduced biases. They'll also examine how applying the GPU Data Frame to their efforts has enabled them to accelerate data science by minimizing data transfers and made it possible for their complex, multi-platform machine learning workflows to run entirely on GPUs.

Transcript

1. © MapD 2018 © MapD 2018 The Need for Speed: How the Auto Industry Accelerates Machine Learning with Visual Analytics Big Data Day LA 2018 August 11, 2018
2. © MapD 2018 Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd
3. © MapD 2018 © MapD 2018 3 “Every business will become a software business, build applications, use advanced analytics and provide SaaS services.” - Smart CEO Guy has
4. © MapD 2018 The Evolution of Competitive Data 4 Collect It Make It Actionable Make it Predictive
5. © MapD 2018 MapD is the analytics platform created for GPUs
6. © MapD 2018 © MapD 2018 6 DEMO TIME
7. © MapD 2018 Advanced memory management Three-tier caching to GPU RAM for speed and to SSDs for persistent storage 7 SSD or NVRAM STORAGE (L3) 250GB to 20TB 1-2 GB/sec CPU RAM (L2) 32GB to 3TB 70-120 GB/sec GPU RAM (L1) 24GB to 256GB 1000-6000 GB/sec Hot Data Speedup = 1500x to 5000x Over Cold Data Warm Data Speedup = 35x to 120x Over Cold Data Cold Data COMPUTE LAYER STORAGE LAYER Data Lake/Data Warehouse/System Of Record
8. © MapD 2018 The GPU Open Analytics Initiative (GOAI) Creating common data frameworks to accelerate data science on GPUs 8 /mapd/pymapd /gpuopenanalytics/pygdf
9. © MapD 2018 Machine Learning Pipeline 9 Personas in Analytics Lifecycle (Illustrative) Business Analyst Data Scientist Data Engineer IT Systems Admin Data Scientist / Business Analyst Data Preparation Data Discovery & Feature Engineering Model & Validate Predict Operationalize Monitoring & Refinement Evaluate & Decide GPUs
10. © MapD 2018 © MapD 2018 10 MORE DEMO TIME
11. © MapD 2018 • We’ve published a few notebooks showing how to connect to a MapD database and use an ML algorithm to make predictions • And we’ve created a churn example with VW 11 ML Examples /gpuopenanalytics/demo-docker /mapd/mapd-ml-demo
12. © MapD 2018 © MapD 2018 • mapd.com/demos Play with our demos • mapd.cloud Get a MapD instance in less than 60 seconds • mapd.com/platform/download-community/ Download the Community Edition • community.mapd.com Ask questions and share your experiences 12 Next Steps
13. © MapD 2018 Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron slides: https://speakerdeck.com/mapd Thank you! Questions?