Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer with Fred Reiss and Vijay Bommireddipalli
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The document discusses IBM's leadership in AI through its Center for Open Source Data and AI Technologies (CODAIT), which aims to simplify the creation and management of AI solutions for enterprises. It highlights contributions to open-source projects and the development of the Fabric for Deep Learning, showcasing its potential for democratizing deep learning for developers and data scientists. Additionally, it introduces the IBM Code Model Asset Exchange as a resource for accessing vetted, open-source deep learning models to facilitate faster project development.
Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer with Fred Reiss and Vijay Bommireddipalli
- 1. Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer Fred Reiss Vijay Bommireddipalli IBM Center for Open-Source Data & AI Technologies (http://codait.org) 1
- 2. 2 IBM’s history of strong AI leadership 1997: Deep Blue • Deep Blue became the first machine to beat a world chess champion in tournament play 2011: Jeopardy! • Watson beat two top Jeopardy! champions 1968, 2001: A Space Odyssey • IBM was a technical advisor • HAL is “the latest in machine intelligence” 2018: Open Tech, AI & emerging standards • New IBM centers of gravity for AI • OS projects increasing exponentially • Emerging global standards in AI
- 3. 3 Center for Open Source Data and AI Technologies • CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise • Relaunch of the Spark Technology Center (STC) to reflect expanded mission • codait (French) • = coder/coded • https://m.interglot.co m/fr/en/codait • CODAIT • codait.org
- 4. 4 CODAIT by the numb3rs • The team contributes to over 10 open source projects. These projects include - Spark, Tensorflow, Keras, SystemML, Arrow, Bahir, Toree, Livy, Zeppelin, R4ML, Stocator, Jupyter Enterprise Gateway • 17 committers and many contributors in Apache projects- Spark, Arrow, systemML, Bahir, Toree, Livy • Over 980 JIRAs and 50,000 lines of code committed to Apache Spark itself, and Over 65,000 LoC into SystemML – Established IBM as the number 1 contributor to Spark Machine Learning in Spark 2.0 release • Over 25 product lines within IBM leveraging Apache Spark in some form or another. CODAIT engineers have interacted and interlocked with many of them. • Speakers at over 100 conferences, MeetUps, un- conferences etc. • codait (French) • = coder/coded • https://m.interglot.co m/fr/en/codait Spark code contribution growth by week • CODAIT • codait.org
- 5. Improving the Enterprise AI lifecycle in Open Source 5 Center for Open Source Data and AI Technologies • Code - Build and improve practical frameworks to enable more developers to realize immediate value (e.g. FfDL, Tensorflow Jupyter, Spark) • Content – Showcase solutions to complex and real world AI problems • Community – Bring developers and data scientists to engage with IBM (e.g. MAX) • codait (French) • = coder/coded • https://m.interglot.co m/fr/en/codait Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-LearnPandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow CODAIT codait.org
- 6. 6 Fabric for Deep Learning https://github.com/IBM/FfDL https://www.youtube.com/watch?v=nQsY WmkfLP4 • FfDL provides a scalable, resilient, and fault tolerant deep-learning framework • Fabric for Deep Learning or FfDL (pronounced as ‘fiddle’) is an open source project which aims at making Deep Learning easily accessible to the people it matters the most i.e. Data Scientists, and AI developers. • FfDL Provides a consistent way to deploy, train and visualize Deep Learning jobs across multiple frameworks like TensorFlow, Caffe, PyTorch, Keras etc. • FfDL is being developed in close collaboration with IBM Research and IBM Watson. It forms the core of Watson`s Deep Learning service in open source. • FfDL Github Page https://github.com/IBM/FfDL FfDL dwOpen Page https://developer.ibm.com/code/open/proj ects/fabric-for-deep-learning-ffdl/ FfDL Announcement Blog http://developer.ibm.com/code/2018/03/20 /fabric-for-deep-learning FfDL Technical Architecture Blog http://developer.ibm.com/code/2018/03/20 /democratize-ai-with-fabric-for-deep- learning Deep Learning as a Service within Watson Studio https://www.ibm.com/cloud/deep-learning • Research paper: “Scalable Multi- Framework Management of Deep Learning Training Jobs” http://learningsys.org/nips17/assets/paper s/paper_29.pdf • FfDL
- 7. Jupyter Enterprise Gateway March 30 2018 / © 2018 IBM Corporation • Jupyter Enterprise Gateway at IBM Code • https://developer.ibm.com/code/openprojects/jupyter-enterprise-gateway/ • Jupyter Enterprise Gateway source code at GitHub • https://github.com/jupyter-incubator/enterprise_gateway • Jupyter Enterprise Gateway Documentation • http://jupyter-enterprise-gateway.readthedocs.io/en/latest/ 7 • A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across an Apache Spark or Kubernetes cluster for Enterprise/Cloud use cases
- 8. Road Map • Background: Deep Learning Models • The IBM Code Model Asset Exchange • Demo • What’s Next 8
- 9. CODAIT: Enabling End-to-End AI in the Enterprise 9 Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-LearnPandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow
- 10. 10 Making AI as Ubiquitous as the Telephone
- 11. 11 This talk is about enabling domain experts to use deep learning in the enterprise. Q: What is deep learning? A: Machine learning using deep neural networks. Q: What is a deep neural network? A: A neural network with multiple hidden layers.
- 12. 12 Q: What is a neural network?
- 13. What is a neural network? 13 ! = #$%+ &$'+ &$( x1 x2 x3 y a b c Linear regression
- 14. What is a neural network? Multiple linear regressions at the same time 14 x1 x2 x3 y3 y1 y4 y2
- 15. What is a neural network? 15 Dense (3×4) Dense (4×2) Input (3) Output (2) Same network in a more compact notation Multilayer Perceptron Neural Network Second layer of linear regressions
- 16. 16 Dense (3×4) Dense (4×2) Input (3) Output (2) Q: What is a deep neural network? A: A neural network with multiple hidden layers.
- 17. Q: What is a deep neural network? A: A neural network with multiple hidden layers. 17 Dense (3×8) Dense (8×6) Input (3) Output (2)Dense (6×4) Dense (4×2)
- 18. Q: What is deep learning? A: Machine learning using deep neural networks. 18 InceptionV3 Convolutional Neural Net (A “medium-sized” deep learning model) Image Source: https://github.com/tensorflow/models/blob/master/research/inception/g3doc /inception_v3_architecture.png
- 19. Characteristics of Deep Learning (1) • State-of-the-Art prediction quality in many domains – Image classification – Machine translation – Facial recognition – Time series prediction – Many more 19
- 20. Characteristics of Deep Learning (2) • Large, complex models – Model size generally determined by “how big a model can you fit on your device?” 20 Each box ≈ between 32 and 768 linear regression models InceptionV3 Convolutional Neural Net (A “medium-sized” deep learning model)
- 21. Characteristics of Deep Learning (3) Poorly understood today …even by experts – Why do the models converge? – Why do the models converge with low loss? – Why do the models generalize? 21
- 22. Focus of this Talk Incorporating well- understood deep learning models into enterprise applications. 22
- 23. 23 Sounds easy!
- 24. “cat” The Parts of a Deep Learning Model 24 Dense (3×8) Dense (8×6) Input (3) Output (2)Dense (6×4) Dense (4×2) Neural Network Graph Weights (not to scale) Driver Program
- 25. Example: Get an Image Classifier Step 1: Find a suitable neural network graph. – Need to read some papers 25
- 26. Example: Get an Image Classifier Step 2: Find code to generate the neural network graph 26 TensorFlow code to build ResNet50 neural network graph
- 27. Example: Get an Image Classifier Step 3: Find some pre- trained weights for your graph 27 Caffe2 ResNet50 model weights* * Caffe2 only. Find a different binary file if your framework is not Caffe2.
- 28. Example: Get an Image Classifier Step 4: Find example code that performs model inference 28 TensorFlow code for training and batch inference* on ResNet50 * Single-crop inference only. Additional code required to use multiple crops.
- 29. Example: Get an Image Classifier Step 5: Write your own code to perform model inference on one image at a time Step 6: Package your inference code, graph creation code, and pre-trained weights together Step 7: Deploy your package 29
- 30. Model Marketplaces • Collections of well-understood deep learning models • Provide a central place to find known-good implementations of these models 30
- 31. The IBM Code Model Asset eXchange • Free, open-source models. • Wide variety of domains. • Multiple deep learning frameworks. • Vetted and tested code and IP. • Build and deploy a web service in 30 seconds. • Start training on Watson Studio in minutes. 31
- 32. 32 • Demo!
- 33. Model Asset eXchange: Summary • Free, open-source models. • Wide variety of domains. • Multiple deep learning frameworks. • Vetted and tested code and IP. • Build and deploy a web service in 30 seconds. • Start training on Watson Studio in minutes. 33
- 34. Model Asset eXchange: What’s Next • More models • More deployment options • Code Patterns showing how to use the models (including today’s demo!) 34
- 35. 35 Thank you! • http://codait.org • https://developer.ibm.com/code/ exchanges/models/ • github.com/codait • developer.ibm.com/code
- 36. Call for Code inspires developers to solve pressing global problems with sustainable software solutions, delivering on their vast potential to do good. Bringing together NGOs, academic institutions, enterprises, and startup developers to compete build effective disaster mitigation solutions, with a focus on health and well-being. International Federation of Red Cross/Red Crescent, The American Red Cross, and the United Nations Office of Human Rights combine for the Call for Code Award to elevate the profile of developers. Award winners will receive long-term support through open source foundations, financial prizes, the opportunity to present their solution to leading VCs, and will deploy their solution through IBM’s Corporate Service Corps. Developers will jump-start their project with dedicated IBM Code Patterns, combined with optional enterprise technology to build projects over the course of three months. Judged by the world’s most renowned technologists, the grand prize will be presented in October at an Award Event. developer.ibm.com/callforcode
- 37. Date, Time, Location & Duration Session title and Speaker Tue, June 5 | 11 AM 2010-2012, 30 mins Productionizing Spark ML Pipelines with the Portable Format for Analytics Nick Pentreath (IBM) Tue, June 5 | 2 PM 2018, 30 mins Making PySpark Amazing—From Faster UDFs to Dependency Management and Graphing! Holden Karau (Google) Bryan Cutler (IBM) Tue, June 5 | 2 PM Nook by 2001, 30 mins Making Data and AI Accessible for All Armand Ruiz Gabernet (IBM) Tue, June 5 | 2:40 PM 2002-2004, 30 mins Cognitive Database: An Apache Spark-Based AI-Enabled Relational Database System Rajesh Bordawekar (IBM T.J. Watson Research Center) Tue, June 5 | 3:20 PM 3016-3022, 30 mins Dynamic Priorities for Apache Spark Application’s Resource Allocations Michael Feiman (IBM Spectrum Computing) Shinnosuke Okada (IBM Canada Ltd.) Tue, June 5 | 3:20 PM 2001-2005, 30 mins Model Parallelism in Spark ML Cross-Validation Nick Pentreath (IBM) Bryan Cutler (IBM) Tue, June 5 | 3:20 PM 2007, 30 mins Serverless Machine Learning on Modern Hardware Using Apache Spark Patrick Stuedi (IBM) Tue, June 5 | 5:40 PM 2002-2004, 30 mins Create a Loyal Customer Base by Knowing Their Personality Using AI-Based Personality Recommendation Engine; Sourav Mazumder (IBM Analytics) Aradhna Tiwari (University of South Florida) Tue, June 5 | 5:40 PM 2007, 30 mins Transparent GPU Exploitation on Apache Spark Dr. Kazuaki Ishizaki (IBM) Madhusudanan Kandasamy (IBM) Tue, June 5 | 5:40 PM 2009-2011, 30 mins Apache Spark Based Hyper-Parameter Selection and Adaptive Model Tuning for Deep Neural Networks Yonggang Hu (IBM) Chao Xue (IBM) IBM Sessions at Spark+AI Summit 2018 (Tuesday, June 5) 37
- 38. Date, Time, Location & Duration Session title and Speaker Wed, June 6 | 12:50 PM Birds of a Feather: Apache Arrow in Spark and More Bryan Cutler (IBM) Li Jin (Two Sigma Investments, LP) Wed, June 6 | 2 PM 2002-2004, 30 mins Deep Learning for Recommender Systems Nick Pentreath (IBM) ) Wed, June 6 | 3:20 PM 2018, 30 mins Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer Frederick Reiss (IBM) Vijay Bommireddipalli (IBM Center for Open-Source Data & AI Technologies) IBM Sessions at Spark+AI Summit 2018 (Wednesday, June 6) 38 Meet us at IBM booth in the Expo area.
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