Evaluation

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• Evaluation
  • Evaluation - Measures
    • Accuracy
    • Precision & Recall (Sensitivity)
    • Specificity
    • Benchmarks
  • Bias and Variances
  • Explainable Artificial Intelligence (XAI)
  • Train, Validate, and Test
  • Model Monitoring
• AIOps / MLOps
• Automated Scoring
• Imbalanced Data
• Five ways to evaluate AI systems | Felix Wetzel - Recruiting Daily
• Cyber Security Evaluation Tool (CSET®) ...provides a systematic, disciplined, and repeatable approach for evaluating an organization’s security posture.
• 3 Common Technical Debts in Machine Learning and How to Avoid Them | Derek Chia - Towards Data Science

Many products today leverage artificial intelligence for a wide range of industries, from healthcare to marketing. However, most business leaders who need to make strategic and procurement decisions about these technologies have no formal AI background or academic training in data science. The purpose of this article is to give business people with no AI expertise a general guideline on how to assess an AI-related product to help decide whether it is potentially relevant to their business. How to Assess an Artificial Intelligence Product or Solution (Even if You’re Not an AI Expert) | Daniel Faggella - Emerj

• What challenge does the Machine Learning (ML) solve?
• Is the intent of Machine Learning (ML) to increase performance (detection), reduce costs (predictive maintenance, reduce inventory) , decrease response time, or other outcome(s)?
• What is the clear and realistic way of measuring the success of the Machine Learning (ML) initiative?
• Does the Machine Learning (ML) reside in a procured item/application/solution or developed in house?
• If the Machine Learning (ML) is procured, e.g. embedded in sensor product, what items are included in the contract to future proof the solution? * Let the organization use implementation to gain better capability in the future?
• Contract items to protect organization reuse data rights?
• What analytics is the Machine Learning (ML) resolving? Descriptive (what happened?), Diagnostic (why did it happen?), Predictive/Preventive (what could happen?), Prescriptive (what should happen?), Cognitive (what steps should be taken?)
• What is the current inference/prediction/true positive rate (TPR) rate?
• How perfect does Machine Learning (ML) have to be to trust it? What is the inference/prediction rate performance metric for the Program?
• What is the false-positive rate? How does Machine Learning (ML) reduce false-positives without increasing false negatives? What is the false-positive rate performance metric for the Program? Is there a Receiver Operating Characteristic (ROC) curve; plotting the true positive rate (TPR) against the false positive rate (FPR) ?
• Has the data been identified for Machine Learning (ML) (current application or for future use) initiative(s)? Is the data labelled, or require manual labeling?
• Have the key features to be used in the Machine Learning (ML) model been identified? If needed, what are the algorithms used to combine ML features? What is the approximate number of features used?
• How are the dataset(s) used for Machine Learning (ML) training, testing and Validation managed? Are logs kept on which data is used for different executions/training so that the information used is traceable? How is the access to the information guaranteed?
• Are the dataset(s) for Machine Learning (ML) published (repo, marketplace) for reuse, if so where?
• What Machine Learning (ML) model type(s) are used? Regression, K-Nearest Neighbors (KNN), Graph Neural Networks, reinforcement, rule-based
• What are the Machine Learning (ML) architecture specifics, e.g. ensemble methods used, graph network, or distributed learning?
• Are the Machine Learning (ML) models published (repo, marketplace) for reuse, if so where?
• Is the Machine Learning (ML) model reused from a repository (repo, marketplace)? If so, which one? How are you notified of updates? How often is the repository checked for updates?
• Is transfer learning used? If so, which Machine Learning (ML) models are used? What mission specific dataset(s) are used to tune the ML model?
• Are Machine Learning (ML) service(s) are used for inference/prediction?
• What Machine Learning (ML) languages, libraries, scripting, are implemented?
• What tools are used for the AIOps? Please identify those on-premises and online services?
• Are the Machine Learning (ML) languages, libraries, scripting, and AIOps applications registered in the DHS Technical Reference Model (TRM)?
• What optimizers are used? Is augmented machine learning (AugML) or automated machine learning (AutoML) used?
• When the Machine Learning (ML) model is updated, how is it determined that the performance was indeed increased for the better?
• What benchmark standard(s) are the Machine Learning (ML) model compared/scored? e.g. General Language Understanding Evaluation (GLUE)
• How often is the deployed Machine Learning (ML) process monitored or measures re-evaluated?
• How is bias accounted for in the Machine Learning (ML) process? How are the dataset(s) used are assured to represent the problem space? What is the process of the removal of features/data that is believed are not relevant? What assurance is provided that the model (algorithm) is not biased?
• Is the model (implemented or to be implemented) explainable? How so?
• Has role/job displacement due to automation and/or Machine Learning (ML) implementation being addressed?
• Are User and Entity Behavior Analytics (UEBA) and machine learning (ML) used to help to create a baseline for trusted workload access?
• Is machine learning (ML) being used for abnormality detection? Security?
• Is machine learning (ML) used protect the Program against targeted attacks, often referred to as advanced targeted attacks (ATAs) or advanced persistent threats (APTs)?
• If the Program is implementing machine learning (ML), is the Program implementing an AIOps pipeline/toolchain?
• Does the Program depict the AIOps pipeline/toolchain applications in their tech stack?
• Has the Program where AI is used in the SecDevOps architecture? e.g. software testing
• Does data management reflected in the AIOps pipeline/toolchain processes/architecture?
• Are the end-to-end visability and bottleneck risks for AIOps pipeline/toolchain reflected in the risk register with mitigation strategy for each risk?

Evaluating AI- and ML-Based Security Products Anup Ghosh, Founder and CEO, Invincea Liam Randall, President, Critical Stack, A Division of Capital One Chad Skipper, VP Competitive Intelligence and Product Testing, Cylance Mike Spanbauer, Vice President of Research and Strategy, NSS Labs With endless AI or machine learning product claims, buyers are left bewildered with how to test these claims. It falls to independent third-party test organizations to develop and update traditional test protocols to test and validate AI and ML product capability claims. This panel will tackle the key issues that third-party testing must address to validate AI and ML security products.

How Should We Evaluate Machine Learning for AI?: Percy Liang Machine learning has undoubtedly been hugely successful in driving progress in AI, but it implicitly brings with it the train-test evaluation paradigm. This standard evaluation only encourages behavior that is good on average; it does not ensure robustness as demonstrated by adversarial examples, and it breaks down for tasks such as dialogue that are interactive or do not have a correct answer. In this talk, I will describe alternative evaluation paradigms with a focus on natural language understanding tasks, and discuss ramifications for guiding progress in AI in meaningful directions. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

ML Test Score

• Machine Learning: The High Interest Credit Card of Technical Debt | | D. Sculley, G Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young - Google Research
• Hidden Technical Debt in Machine Learning Systems D. Sculley, G Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J. Crespo, and D. Dennison - Google Research

Creating reliable, production-level machine learning systems brings on a host of concerns not found in small toy examples or even large offline research experiments. Testing and monitoring are key considerations for ensuring the production-readiness of an ML system, and for reducing technical debt of ML systems. But it can be difficult to formulate specific tests, given that the actual prediction behavior of any given model is difficult to specify a priori. In this paper, we present 28 specific tests and monitoring needs, drawn from experience with a wide range of production ML systems to help quantify these issues and present an easy to follow road-map to improve production readiness and pay down ML technical debt. The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction | E. Breck, S. Cai, E. Nielsen, M. Salib, and D. Sculley - Google Research Full Stack Deep Learning

ML Test Score (2) - Testing & Deployment - Full Stack Deep Learning How can you test your machine learning system?  A Rubric for Production Readiness and Technical Debt Reduction  is an exhaustive framework/checklist from practitioners at Google. - The paper presents a rubric as a set of 28 actionable tests and offers a scoring system to measure how ready for production a given machine learning system is. These are categorized into 4 sections: (1) data tests, (2) model tests, (3) ML infrastructure tests, and (4) monitoring tests. - The scoring system provides a vector for incentivizing ML system developers to achieve stable levels of reliability by providing a clear indicator of readiness and clear guidelines for how to improve.

What is Your ML Score? - Tania Allard Developer Advocate at Microsoft Using machine learning in real-world applications and production systems is complex. Testing, monitoring, and logging are key considerations for assessing the decay, current status, and production-readiness of machine learning systems. Where do you get started? Who is responsible for testing and monitoring? I’ll discuss the most frequent issues encountered in real-life ML applications and how you can make systems more robust. I’ll also provide a rubric with actionable examples to ensure quality and adequacy of a model in production.

Buying

Build or buy AI? You're asking the wrong question Evan Kohn, chief business officer and head of marketing at Pypestream, talks with Tonya Hall about why companies need to turn to staffing for AI and building data sets.

Why you should Buy Open-Source AI Considering an AI assistant in your home? Before you auto-buy that pretty picture in front of you, be sure to check out the open-source offerings as well.

Best Practices

• Rules of Machine Learning: Best Practices for ML Engineering | Martin Zinkevich - Google

Rules of ML Google research scientist Martin Zinkevich

Best Practices of In-Platform AI/ML Webinar Productive use of machine learning and artificial intelligence technologies is impossible without a platform that allows autonomous functioning of AI/ML mechanisms. In-platform AI/ML has a number of advantages that can be obtained via best practices by InterSystems. On this webinar, we will present: • MLOps as the natural paradigm for in-platform AI/ML • A full cycle of AI/ML content development and in-platform deployment (including bidirectional integration of Jupyter with InterSystems IRIS) • New toolset added to ML Toolkit: integration and orchestration for Julia mathematical modeling environment • Automated AI/ML model selection and parameter determination via an SQL query • Cloud-enhanced ML • Featured use case demo: hospital readmission prediction (addresses running in InterSystems IRIS of the models trained outside the platform's control)

Model Deployment Scoring

ML Model Deployment and Scoring on the Edge with Automatic ML & DF / Flink2Kafka recorded on June 18, 2020. Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge. Speakers: James Medel (H2O.ai - Technical Community Maker) Greg Keys (H2O.ai - Solution Engineer) Kafka 2 Flink - An Apache Love Story This project has heavily inspired by two existing efforts from Data In Motion's FLaNK Stack and Data Artisan's blog on stateful streaming applications. The goal of this project is to provide insight into connecting an Apache Flink applications to Apache Kafka. Speaker: Ian R Brooks, PhD (Cloudera - Senior Solutions Engineer & Data)

Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models PyData NYC 2015 Machine learning has become the key component in building intelligence-infused applications. However, as companies increase the number of such deployments, the number of machine learning models that need to be created, maintained, monitored, tracked, and improved grow at a tremendous pace. This growth has lead to a huge (and well-documented) accumulation of technical debt. Developing a machine learning application is an iterative process that involves building multiple models over a dataset. The dataset itself evolves over time as new features and new data points are collected. Furthermore, once deployed, the models require updates over time. Changes in models and datasets become difficult to track over time, and one can quickly lose track of which version of the model used which data and why it was subsequently replaced. In this talk, we outline some of the key challenges in large-scale deployments of many interacting machine learning models. We then describe a methodology for management, monitoring, and optimization of such models in production, which helps mitigate the technical debt. In particular, we demonstrate how to: Track models and versions, and visualize their quality over time Track the provenance of models and datasets, and quantify how changes in data impact the models being served Optimize model ensembles in real time, based on changing data, and provide alerts when such ensembles no longer provide the desired accuracy.