Open Source Deep Learning for Developers at Red Hat Summit

Open Source Deep Learning for Developers at Red Hat Summit

While we are a long way out from machines that can perform artificial general intelligence (AGI) tasks, deep learning can be used for many narrow artificial intelligence (AI) tasks including image or audio classification, facial recognition, object recognition, image caption generation, and natural language processing. We will explore how developers can easily integrate open source deep learning models into their applications.

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Bradley Holt

May 08, 2019
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  1. 1.

    Open Source Deep Learning for Developers — Bradley Holt Program

    Manager, Developer Advocacy Center for Open-Source Data & AI Technologies ↳ (CODAIT) @BradleyHolt
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    It’s a hybrid world Enterprises want the ability to span

    traditional IT, private and public clouds. Containers are strategic Enterprises need agility to meet the demands of their clients and their markets. Innovation is the path forward Mission critical projects need industry leading middleware and data. IBM and Red Hat Share Three Common Beliefs 2 © 2019 IBM Corporation IBM and Red Hat – Partners for 20 years For over 20 years IBM and Red Hat have collaborated with the open source community to drive innovation and power businesses around the world.
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    Artificial Intelligence Center for Open-Source Data & AI Technologies (CODAIT)

    / May 8, 2019 / © 2019 IBM Corporation @BradleyHolt
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    ↳ General AI Metal Skull With Terminator Eye by L.C.

    Nøttaasen, on Flickr <https://flic.kr/p/6xh2Dr> (CC BY-SA 2.0).
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    ↳ Broad AI -[ electrIc b88Gal88 ]- by JD Hancock,

    on Flickr <https://flic.kr/p/6x9t1H> (CC BY 2.0).
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    ↳ Narrow AI Danbo on the Lookout by IQRemix, on

    Flickr <https://flic.kr/p/x5oWjP> (CC BY-SA 2.0).
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    Approaches to Artificial Intelligence Center for Open-Source Data & AI

    Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation Machine Learning (ML) • Predictive analytics • Data mining • Anomaly detection • Email filtering Deep Learning • Image or audio classification • Facial recognition • Object recognition • Image caption generation • Natural language processing @BradleyHolt
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    Scan Directory with magicat $ magicat . --contains sheep Scanning

    directory '~/tfjs-demos/magicat' for sheep... Sheep found in: 37038669284_899d7784a9_k.jpg 37489697170_31d05aa027_k.jpg @BradleyHolt
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    Save Image Segment with magicat $ magicat 37699976806_5ce694be36_k.jpg --save horse

    The image '37699976806_5ce694be36_k.jpg' contains the following segments: background, horse. saved 37699976806_5ce694be36_k-horse.png Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
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    Software Programming Center for Open-Source Data & AI Technologies (CODAIT)

    / May 8, 2019 / © 2019 IBM Corporation Untitled by Marcin Wichary, on Flickr <https://flic.kr/p/68Lhc9> (CC BY 2.0).
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    Center for Open-Source Data & AI Technologies (CODAIT) / May

    8, 2019 / © 2019 IBM Corporation 01011001001011000001 10101101010100100110 00010110101010011100 10101010110101010100 10001001011101000101 Software Program Business Logic @BradleyHolt
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    Center for Open-Source Data & AI Technologies (CODAIT) / May

    8, 2019 / © 2019 IBM Corporation 01011001001011000001 10101101010100100110 00010110101010011100 10101010110101010100 10001001011101000101 Software Program Input Program Execution Output @BradleyHolt
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    Deep Learning & ML vs. Software Programming Center for Open-Source

    Data & AI Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation Deep Learning & ML • Training • High-quality examples (i.e., training data) • Approximation of a correct function Software Programming • Programming • Set of direct instructions • Precisely-defined function @BradleyHolt
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    Center for Open-Source Data & AI Technologies (CODAIT) / May

    8, 2019 / © 2019 IBM Corporation Input Layer Hidden Layers Output Layer @BradleyHolt
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    Fashion-MNIST dataset by Zalando Research, on GitHub <https://github.com/zalandoresearch/fashion-mnist> (MIT License).

    Backpropagation Labeled Training Data Coat Sneaker T-shirt Sneaker Pullover Output Errors Pullover Coat Coat Sneaker T-shirt ❌ ❌ ❌
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    Center for Open-Source Data & AI Technologies (CODAIT) / May

    8, 2019 / © 2019 IBM Corporation @BradleyHolt
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    Applying Deep Learning Center for Open-Source Data & AI Technologies

    (CODAIT) / May 8, 2019 / © 2019 IBM Corporation Data Science Expertise Computing Resources High-Quality Training Data Model Deployment Time Model Integration Inferencing Code And more… @BradleyHolt
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    Model Asset Exchange Classify Generate Recognize Center for Open-Source Data

    & AI Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation @BradleyHolt
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    Microservice Center for Open-Source Data & AI Technologies (CODAIT) /

    May 8, 2019 / © 2019 IBM Corporation Choose deployable model Deep Learning asset from the Model Asset Exchange (MAX) Deploy Swagger specification Inference endpoint Metadata endpoint Input preprocessing, model execution, and output post-processing Deploy model Use model @BradleyHolt
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    AI Lifecycle Center for Open-Source Data & AI Technologies (CODAIT)

    / May 8, 2019 / © 2019 IBM Corporation Deploy & Run Operate & Manage Prepare, Build & Train @BradleyHolt
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    Preparing, Building, and Training AI Models Center for Open-Source Data

    & AI Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation Model Asset Exchange (MAX) @BradleyHolt
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    Deploying and Running AI Models Center for Open-Source Data &

    AI Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation ONNX.js Model Asset Exchange (MAX) @BradleyHolt
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    Operating and Managing AI Systems Fabric for Deep Learning (FfDL)

    Train and deploy deep learning models on Kubernetes using TensorFlow, Caffe2, PyTorch, and other frameworks AI Fairness 360 (AIF360) Comprehensive set of fairness metrics for machine learning models, explanations for these metrics, and algorithms to mitigate bias in models Adversarial Robustness Toolbox (ART) Python library for adversarial attacks and defenses for neural networks with multiple framework support @BradleyHolt
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    IBM Data & AI Portfolio Center for Open-Source Data &

    AI Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation The Ladder to AI Pre-built Use Cases Watson Applications Multicloud Data & AI Platform IBM Cloud Platform for Data Hybrid Data Management Db2 Family Data Governance & Integration InfoSphere Family Open Source meets a multicloud, working as ONE Watson Machine Learning Watson Knowledge Catalog Watson Studio Watson OpenScale Prepare Build Manage Run @BradleyHolt
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    Watson Data Science Platform Center for Open-Source Data & AI

    Technologies (CODAIT) / May 8, 2019 / © 2019 IBM Corporation Build and Deploy Upon Open Source Frameworks Watson Studio A universal runtime to embed, share and optimize AI models Design, build and train AI models, with visual modeling & generation Watson Machine Learning Watson OpenScale Operate and scaling AI value & usage with trust & transparency Unify on a Multicloud Data & AI Platform Watson Knowledge Catalog Data Discovery Data Preparation Policy-based Catalogs Visual Design Develop & Train Automation (AutoAI) Deploy & Manage Model-ops Retraining Optimize Outcomes Explainability & Lineage Automated Evolution Unified catalog to data users find, curate, categorize and share data Prep Build Run Manage @BradleyHolt
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    What will you build with deep learning? Dynamic Earth -

    Continental Shelf by NASA Goddard Space Flight Center, on Flickr <https://flic.kr/p/ch8t25> (CC BY 2.0).
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    Resources: Machine Learning Libraries • TensorFlow https://www.tensorflow.org/ • Train your

    first neural network: basic classification | TensorFlow https://www.tensorflow.org/tutorials/keras/basic_classification • Keras https://keras.io/ • PyTorch https://pytorch.org/ • Caffe2 https://caffe2.ai/ @BradleyHolt
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    Resources: IBM Developer • IBM Developer https://developer.ibm.com/ • IBM Cloud

    https://ibm.biz/Bd2NAr • Center for Open-Source Data & AI Technologies (CODAIT) http://codait.org/ • IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ • magicat https://github.com/CODAIT/magicat @BradleyHolt
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    Resources: Preparing, Building, and Training AI Models • Project Jupyter

    https://jupyter.org/ • IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ • IBM Watson Knowledge Catalog https://www.ibm.com/cloud/watson-knowledge-catalog • IBM Watson Studio https://www.ibm.com/cloud/watson-studio @BradleyHolt
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    Resources: Deploying and Running AI Models • Data Mining Group

    (PMML & PFA) http://dmg.org/ • ONNX https://onnx.ai/ • ONNX.js https://github.com/Microsoft/onnxjs • TensorFlow.js https://js.tensorflow.org/ • TensorFlow Lite https://www.tensorflow.org/lite • Core ML https://developer.apple.com/machine-learning/ • IBM Watson Machine Learning https://www.ibm.com/cloud/machine-learning @BradleyHolt
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    Resources: Operating and Managing AI Systems • Fabric for Deep

    Learning (FfDL) https://github.com/IBM/FfDL • AI Fairness 360 (AIF360) https://github.com/IBM/AIF360 • Adversarial Robustness Toolbox (ART) https://github.com/IBM/adversarial-robustness-toolbox • IBM Watson OpenScale https://www.ibm.com/cloud/watson-openscale @BradleyHolt
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    Thank you. Center for Open-Source Data & AI Technologies (CODAIT)

    / May 8, 2019 / © 2019 IBM Corporation Bradley Holt Program Manager, Developer Advocacy Center for Open-Source Data & AI Technologies (CODAIT) — ibm.com @BradleyHolt
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