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Open, Secure & Transparent AI Pipelines

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Nick Pentreath

AI algorithms offer great promise in criminal justice, credit scoring, hiring and other domains. However, algorithmic fairness is a legitimate concern. Possible bias and adversarial contamination can come from training data, inappropriate data handling/model selection or incorrect algorithm design. This talk discusses how to build an open, transparent, secure and fair pipeline that fully integrates into the AI lifecycle — leveraging open-source projects such as AI Fairness 360 (AIF360), Adversarial Robustness Toolbox (ART), the Fabric for Deep Learning (FfDL) and the Model Asset eXchange (MAX).

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Open, Secure & Transparent AI Pipelines Nick Pentreath, Principal Engineer IBM
About @MLnick on Twitter & Github Principal Engineer, IBM CODAIT - Center for Open-Source Data & AI Technologies Machine Learning & AI Apache Spark committer & PMC Author of Machine Learning with Spark Various conferences & meetups
Center for Open Source Data and AI Technologies Watson West Building 505 Howard St. San Francisco, California CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise. Relaunch of the IBM Spark Technology Center (STC) to reflect expanded mission. We contribute to foundational open source software across the enterprise AI lifecycle. 36 open-source developers! Improving Enterprise AI Lifecycle in Open Source CODAIT codait.org
What is AI?
What about Machine Learning / Deep Learning? Learn from data to make predictions
What about Machine Learning / Deep Learning? Learn from historical data to make predictions about the future
Applied Machine Learning Learn from historical data to make predictions about the future, in order to make decisions
Intelligent Systems Source: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ Automated decision-making Continual learning (new data & feedback) Adapting to environment Memory & generalization
Trust in AI
The Machine Learning Workflow
Perception
In reality the workflow spans teams …
… and tools …
… and is a small (but critical!) piece of the puzzle *Source: Hidden Technical Debt in Machine Learning Systems
Training
* Logos trademarks of their respective projects
17 Fabric for Deep Learning https://developer.ibm.com/ope n/projects/fabric-for-deep- learning-ffdl/ https://github.com/IBM/FfDL• Fabric for Deep Learning or FfDL (pronounced ‘fiddle’) aims at making Deep Learning easily accessible to Data Scientists and AI developers. • FfDL provides a consistent way to train and visualize Deep Learning jobs across multiple frameworks like TensorFlow, Caffe, PyTorch, Keras etc. FfDL Community Partners FfDL is one of InfoWorld’s 2018 Best of Open Source Software Award winners for machine learning and deep learning!
AI Deployment
What, Where, How? • What are you deploying? • What is a “model”? • Where are you deploying? • Target environment • Batch, streaming, real-time? • How are you deploying? • “devops” deployment mechanism • Serving framework We will talk mostly about the what
What is a “model”?
Pipelines, not Models • Deploying just the model part of the workflow is not enough • Entire pipeline must be deployed • Data transform • Feature extraction & pre-processing • ML model itself • Prediction transformation • Technically even ETL is part of the pipeline! • Pipelines in open-source frameworks • scikit-learn • Spark ML pipelines • TensorFlow Transform • pipeliner (R)
Challenges • Proliferation of formats • Open source, open standard: PMML, PFA, ONNX • Open-source, non-standard: MLeap, Spark, TensorFlow, Keras, PyTorch, Caffe, … • Proprietary formats: lock-in, not portable • Lack of standardization leads to custom solutions • Where standards exist, limitations lead to custom extensions, eliminating the benefits • Need to manage and bridge many different: • Languages - Python, R, Notebooks, Scala / Java / C • Frameworks – too many to count! • Dependencies • Versions • Performance characteristics can be highly variable across these dimensions • Friction between teams • Data scientists & researchers – latest & greatest • Production – stability, control, minimize changes, performance • Business – metrics, business impact, product must always work!
Containers for ML Pipelines 23
Containers for ML Deployment • But … • What goes in the container is most important • Performance can be highly variable across language, framework, version • Requires devops knowledge, CI / deployment pipelines, good practices • Does not solve the issue of standardization • Formats • APIs exposed • A serving framework is still required on top • Container-based deployment has significant benefits • Repeatability • Ease of configuration • Separation of concerns – focus on what, not how • Allow data scientists & researchers to use their language / framework of choice • Container frameworks take care of (certain) monitoring, fault tolerance, HA, etc.
IBM Developer Model Asset eXchange http://ibm.biz/model-exchange Free, open-source deep learning 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 Fabric for Deep Learning (FfDL) or Watson Machine Learning in minutes.
MAX Model Metadata
Open Standards for Model Deployment
Predictive Model Markup Language (PMML) • Shortcomings • Cannot represent arbitrary programs / analytic applications • Flexibility comes from custom plugins => lose benefits of standardization • Data Mining Group (DMG) • Model interchange format in XML with operators • Widely used and supported; open standard • Spark support lacking natively but 3rd party projects available: jpmml-sparkml • Comprehensive support for Spark ML components (perhaps surprisingly!) • Watch SPARK-11237 • Other exporters include scikit-learn, R, XGBoost and LightGBM
Portable Format for Analytics • Shortcomings • PFA is still young and needs to gain adoption • Production robustness and performance at scale? • Limitations of PFA – especially for deep learning applications • A standard can move slowly in terms of new features, fixes and enhancements • PFA is being championed by the DMG • PFA consists of: • JSON serialization format • AVRO schemas for data types • Encodes functions (actions) that are applied to inputs to create outputs with a set of built-in functions and language constructs (e.g. control-flow, conditionals) • Essentially a mini functional math language + schema specification • Portability across languages, frameworks, run times and versions https://github.com/CODAIT/aardpfark
Open Neural Network Exchange (ONNX) • Shortcomings • Relatively poor support for “traditional” ML or general language constructs (currently) • String / categorical processing • Datetime, collection operators • Intermediate variables • Evolving standard – coverage of operators (e.g. TensorFlow) • Championed by Facebook & Microsoft • Protobuf serialization format • Describes computation graph (including operators) • In this way the serialized graph is “self-describing” similarly to PFA • More focused on Deep Learning / tensor operations • Baked into PyTorch 1.0.0 / Caffe2 as the serialization & interchange format • ONNX-ML covers “traditional” ML operators https://github.com/onnx
Monitoring & Feedback DBG / May 10, 2018 / © 2018 IBM Corporation
Monitoring Performance Business Monitoring Software Traditional software monitoring Latency, throughput, resource usage, etc Model metrics Traditional ML evaluation measures + bias, robustness, explainability Business metrics Impact of predictions on business outcomes • Additional revenue - e.g. uplift from recommender • Cost savings – e.g. value of fraud prevented • Metrics implicitly influencing these – e.g. user engagement
Feedback Adapt An intelligent system must automatically learn from & adapt to the world around it Continual learning Retraining, online learning, reinforcement learning Feedback loops Explicit: models create or directly influence their own training data Implicit: predictions influence behavior in longer-term or indirect ways Humans in the loop Data Transform TrainDeploy Feedback
AI Fairness & Transparency 34
35 AI Fairness 360 Toolbox: Fairness metrics (30+) Fairness metric explanations Bias mitigation algorithms (10) AIF360AIF360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models. https://github.com/IBM/AIF360 https://developer.ibm.com/patterns/ensuring- fairness-when-processing-loan-applications/
36 AIF360 Demo: http://aif360.mybluemix.net
Defending machine learning systems
Adversarial Attacks 38Sources: Explaining and Harnessing Adversarial Examples Robust Physical-World Attacks on Deep Learning Visual Classification
IBM Adversarial Robustness Toolbox ART ART is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attack and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. 39 https://github.com/IBM/adversarial-robustness-toolbox https://developer.ibm.com/patterns/integrate-adversarial- attacks-model-training-pipeline/ Toolbox Evasion attacks (11) Defenses (9) Detection methods for adversarial samples & poisoning attacks Robustness metrics
Getting CLEVER 40 Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER): • Published by IBM Research • Attack-agnostic measure • Efficient computation https://medium.com/@MITIBMLab/getting-clever-er-expanding- the-scope-of-a-robustness-metric-for-neural-networks-81c6c6ecb https://arxiv.org/abs/1801.10578 https://github.com/IBM/CLEVER-Robustness-Score
41 ART Demo: https://art-demo.mybluemix.net/
Wrapping Up
CODAIT: End-to-end Enterprise AI in Open Source
44 codait.org twitter.com/MLnick github.com/MLnick developer.ibm.com FfDL Sign up for IBM Cloud and try Watson Studio! https://ibm.biz/Bd2Gb7 MAX
Thank you.

More Related Content

Open, Secure & Transparent AI Pipelines

  • 1. Open, Secure & Transparent AI Pipelines Nick Pentreath, Principal Engineer IBM
  • 2. About @MLnick on Twitter & Github Principal Engineer, IBM CODAIT - Center for Open-Source Data & AI Technologies Machine Learning & AI Apache Spark committer & PMC Author of Machine Learning with Spark Various conferences & meetups
  • 3. Center for Open Source Data and AI Technologies Watson West Building 505 Howard St. San Francisco, California CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise. Relaunch of the IBM Spark Technology Center (STC) to reflect expanded mission. We contribute to foundational open source software across the enterprise AI lifecycle. 36 open-source developers! Improving Enterprise AI Lifecycle in Open Source CODAIT codait.org
  • 5. What about Machine Learning / Deep Learning? Learn from data to make predictions
  • 6. What about Machine Learning / Deep Learning? Learn from historical data to make predictions about the future
  • 7. Applied Machine Learning Learn from historical data to make predictions about the future, in order to make decisions
  • 8. Intelligent Systems Source: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ Automated decision-making Continual learning (new data & feedback) Adapting to environment Memory & generalization
  • 12. In reality the workflow spans teams …
  • 14. … and is a small (but critical!) piece of the puzzle *Source: Hidden Technical Debt in Machine Learning Systems
  • 16. * Logos trademarks of their respective projects
  • 17. 17 Fabric for Deep Learning https://developer.ibm.com/ope n/projects/fabric-for-deep- learning-ffdl/ https://github.com/IBM/FfDL• Fabric for Deep Learning or FfDL (pronounced ‘fiddle’) aims at making Deep Learning easily accessible to Data Scientists and AI developers. • FfDL provides a consistent way to train and visualize Deep Learning jobs across multiple frameworks like TensorFlow, Caffe, PyTorch, Keras etc. FfDL Community Partners FfDL is one of InfoWorld’s 2018 Best of Open Source Software Award winners for machine learning and deep learning!
  • 19. What, Where, How? • What are you deploying? • What is a “model”? • Where are you deploying? • Target environment • Batch, streaming, real-time? • How are you deploying? • “devops” deployment mechanism • Serving framework We will talk mostly about the what
  • 20. What is a “model”?
  • 21. Pipelines, not Models • Deploying just the model part of the workflow is not enough • Entire pipeline must be deployed • Data transform • Feature extraction & pre-processing • ML model itself • Prediction transformation • Technically even ETL is part of the pipeline! • Pipelines in open-source frameworks • scikit-learn • Spark ML pipelines • TensorFlow Transform • pipeliner (R)
  • 22. Challenges • Proliferation of formats • Open source, open standard: PMML, PFA, ONNX • Open-source, non-standard: MLeap, Spark, TensorFlow, Keras, PyTorch, Caffe, … • Proprietary formats: lock-in, not portable • Lack of standardization leads to custom solutions • Where standards exist, limitations lead to custom extensions, eliminating the benefits • Need to manage and bridge many different: • Languages - Python, R, Notebooks, Scala / Java / C • Frameworks – too many to count! • Dependencies • Versions • Performance characteristics can be highly variable across these dimensions • Friction between teams • Data scientists & researchers – latest & greatest • Production – stability, control, minimize changes, performance • Business – metrics, business impact, product must always work!
  • 24. Containers for ML Deployment • But … • What goes in the container is most important • Performance can be highly variable across language, framework, version • Requires devops knowledge, CI / deployment pipelines, good practices • Does not solve the issue of standardization • Formats • APIs exposed • A serving framework is still required on top • Container-based deployment has significant benefits • Repeatability • Ease of configuration • Separation of concerns – focus on what, not how • Allow data scientists & researchers to use their language / framework of choice • Container frameworks take care of (certain) monitoring, fault tolerance, HA, etc.
  • 25. IBM Developer Model Asset eXchange http://ibm.biz/model-exchange Free, open-source deep learning 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 Fabric for Deep Learning (FfDL) or Watson Machine Learning in minutes.
  • 27. Open Standards for Model Deployment
  • 28. Predictive Model Markup Language (PMML) • Shortcomings • Cannot represent arbitrary programs / analytic applications • Flexibility comes from custom plugins => lose benefits of standardization • Data Mining Group (DMG) • Model interchange format in XML with operators • Widely used and supported; open standard • Spark support lacking natively but 3rd party projects available: jpmml-sparkml • Comprehensive support for Spark ML components (perhaps surprisingly!) • Watch SPARK-11237 • Other exporters include scikit-learn, R, XGBoost and LightGBM
  • 29. Portable Format for Analytics • Shortcomings • PFA is still young and needs to gain adoption • Production robustness and performance at scale? • Limitations of PFA – especially for deep learning applications • A standard can move slowly in terms of new features, fixes and enhancements • PFA is being championed by the DMG • PFA consists of: • JSON serialization format • AVRO schemas for data types • Encodes functions (actions) that are applied to inputs to create outputs with a set of built-in functions and language constructs (e.g. control-flow, conditionals) • Essentially a mini functional math language + schema specification • Portability across languages, frameworks, run times and versions https://github.com/CODAIT/aardpfark
  • 30. Open Neural Network Exchange (ONNX) • Shortcomings • Relatively poor support for “traditional” ML or general language constructs (currently) • String / categorical processing • Datetime, collection operators • Intermediate variables • Evolving standard – coverage of operators (e.g. TensorFlow) • Championed by Facebook & Microsoft • Protobuf serialization format • Describes computation graph (including operators) • In this way the serialized graph is “self-describing” similarly to PFA • More focused on Deep Learning / tensor operations • Baked into PyTorch 1.0.0 / Caffe2 as the serialization & interchange format • ONNX-ML covers “traditional” ML operators https://github.com/onnx
  • 31. Monitoring & Feedback DBG / May 10, 2018 / © 2018 IBM Corporation
  • 32. Monitoring Performance Business Monitoring Software Traditional software monitoring Latency, throughput, resource usage, etc Model metrics Traditional ML evaluation measures + bias, robustness, explainability Business metrics Impact of predictions on business outcomes • Additional revenue - e.g. uplift from recommender • Cost savings – e.g. value of fraud prevented • Metrics implicitly influencing these – e.g. user engagement
  • 33. Feedback Adapt An intelligent system must automatically learn from & adapt to the world around it Continual learning Retraining, online learning, reinforcement learning Feedback loops Explicit: models create or directly influence their own training data Implicit: predictions influence behavior in longer-term or indirect ways Humans in the loop Data Transform TrainDeploy Feedback
  • 34. AI Fairness & Transparency 34
  • 35. 35 AI Fairness 360 Toolbox: Fairness metrics (30+) Fairness metric explanations Bias mitigation algorithms (10) AIF360AIF360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models. https://github.com/IBM/AIF360 https://developer.ibm.com/patterns/ensuring- fairness-when-processing-loan-applications/
  • 38. Adversarial Attacks 38Sources: Explaining and Harnessing Adversarial Examples Robust Physical-World Attacks on Deep Learning Visual Classification
  • 39. IBM Adversarial Robustness Toolbox ART ART is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attack and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. 39 https://github.com/IBM/adversarial-robustness-toolbox https://developer.ibm.com/patterns/integrate-adversarial- attacks-model-training-pipeline/ Toolbox Evasion attacks (11) Defenses (9) Detection methods for adversarial samples & poisoning attacks Robustness metrics
  • 40. Getting CLEVER 40 Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER): • Published by IBM Research • Attack-agnostic measure • Efficient computation https://medium.com/@MITIBMLab/getting-clever-er-expanding- the-scope-of-a-robustness-metric-for-neural-networks-81c6c6ecb https://arxiv.org/abs/1801.10578 https://github.com/IBM/CLEVER-Robustness-Score
  • 43. CODAIT: End-to-end Enterprise AI in Open Source
  • 44. 44 codait.org twitter.com/MLnick github.com/MLnick developer.ibm.com FfDL Sign up for IBM Cloud and try Watson Studio! https://ibm.biz/Bd2Gb7 MAX