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Greg Werner, CEO & Founder, at MLconf ATL 2017

Productive Machine Learning and Deep Learning Projects
Machine Learning (ML) and Deep Learning (DL), known holistically as Artificial Intelligence, are no longer luxuries but necessities if companies want to remain relevant n today’s market. Data driven organizations that encourage the development of ML and DL projects allow companies to create and deploy models to create predictions in real time. Even more exciting, these real time predictions allow organizations to trigger actions based on these predictions, which ultimately improves the bottom line. However, organizations struggle to incorporate ML and DL projects to create models that improve performance. This talk focuses on how companies can enable data science platforms so that data engineers, data scientists and business analysts can quickly explore data, create and test ML and DL models, and deploy to staging and production environments regardless of the language or framework used by the team and organization.

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Greg Werner, CEO & Founder, at MLconf ATL 2017

  1. 1. Data Science with Teams Improve the efficiency of your data science teams with platforms that enhance collaboration and flexibility
  2. 2. Agenda ● Some Background ● Goals ● Data Science Project Teams ● Challenges ● Some Solutions ● Conclusions
  3. 3. Background Integration experience with Oil & Gas, Financial, Insurance and Retail industries in multiple geographies What did these customers have in common? All had data science teams that worked in Silos Difficulties when taking a data science course Source: Wikipedia
  4. 4. Background (cont) What’s going on here? We started to do some digging! Source:
  5. 5. Data Teams - The Old Way Department Teams Data Scientist Data Analyst IT Manager
  6. 6. The Analytics Deliverable A dashboard! An interactive dashboard is even cooler.
  7. 7. Conway’s Data Scientist Venn Diagram
  8. 8. Data Science Teams - The New Way Data ScientistFinance Manager Accountant Tax and Compliance Treasury Data Gurus: - Analytics - Data Engineers - Business Intelligence - Compliance IT Manager
  9. 9. The Data Science Deliverable A machine or deep learning model!
  10. 10. I Want GPUs - And I Just Want Them to Work Work around for NVidia Docker Wrapper: - nvidia-docker -d -p 8888:8888 tensorflow/tensorflow:latest-gpu OR - docker run -ti --rm `curl -s http://localhost:3476/docker/cli` tensorflow/tensorflow:latest-gpu OR - docker run -ti --rm --volume-driver=nvidia-docker -- volume=nvidia_driver_375.82:/usr/local/nvidia:ro --device=/dev/nvidiactl -- device=/dev/nvidia-uvm --device=/dev/nvidia0 nvidia/cuda nvidia-smi
  11. 11. The Need for DevOps Chops Registrator Docker Container EC2 Instance Reverse Proxy with consul-template The old way... The new way... Docker Container EC2 Instance Reverse Proxy with static upstream location name $$$ $
  12. 12. Infrastructure Management Uh-oh, someone has to manage this stuff!
  13. 13. Our Architecture - API First and Microservices
  14. 14. 3Blades Hub for Data Scientists
  15. 15. Solutions Provide flexibility with the tools that data scientists use for exploratory data analysis and visualizations One central source for project files with support for version control Share visualizations from EDA Train and save Machine Learning and Deep Learning models with multiple frameworks, from within the same project Streamline deployment pipelines
  16. 16. Thank You!! Email: Web: Twitter: @3bladesio GitHub: Email: Twitter: @gwerner LinkedIn: GitHub: