ML Test Score

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• Evaluation
  • Evaluation - Measures
    • Accuracy
    • Precision & Recall (Sensitivity)
    • Specificity
    • Benchmarks
  • Bias and Variances
  • Explainable Artificial Intelligence (XAI)
  • Train, Validate, and Test
  • AI Verification and Validation
  • Model Monitoring
• Cybersecurity: Evaluating & Selling
• Strategy & Tactics
• Checklists
• AI Governance
  • Data Governance
    • Data Science
    • Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
• Automated Scoring
• Risk, Compliance and Regulation
• AIOps / MLOps
• Libraries & Frameworks

• 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.

Procuring

• Enterprise AI Buyer’s Guide | C3.ai
• AI Procurement in a Box: AI Government Procurement Guidelines - Toolkit | World Economic Forum
• Guidance Guidelines for AI procurement | Gov.UK
• A Buyer’s Guide to AI and Machine Learning | Erik Fogg - DevOps.com
• A Buyer’s Checklist for AI in Health and Care | NHSX
• Buyers Guide to Intelligent Virtual Agents and Chatbots | Liz Osborn

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)

Leadership

• Creatives

Artificial Intelligence: New Challenges for Leadership and Management The Future of Management in an Artificial Intelligence-Based World For more info about the conference: https://bit.ly/2J30TD3 -Dario Gil, Vice President of Science and Solutions, IBM Research -Tomo Noda, Founder and Chair, Shizenkan University Graduate School of Leadership and Innovation, Japan Moderator: Sandra Sieber, Professor, IESE

Herminia Ibarra: What Will Leadership Look Like In The Age of AI? Herminia Ibarra, the Charles Handy professor of organisational behaviour at the London Business School, delves into what talent looks like in the age of artificial intelligence. Leaders are people who move a company, organisation, or institution from its current to – ideally – something better. In the age of artificial intelligence and smart technologies, this means being able to actually make use of the vast technological capability that is out there, but is wildly under-used.

Who Makes AI Projects Successful Business leaders often have high expectations of AI/ML projects, and are sorely disappointed when things don't work out. AI implementations are more than just solving the technology problem. There are many other aspects to consider, and you'll need someone who has strong knowledge and background in business, technology (especially AI/ML), and data to guide the business on projects to take on, strategic direction, updates, and many other aspects. In this video, I call out the need for such a role because the underlying paradigm of software development is shifting. Here's what I can do to help you. I speak on the topics of architecture and AI, help you integrate AI into your organization, educate your team on what AI can or cannot do, and make things simple enough that you can take action from your new knowledge. I work with your organization to understand the nuances and challenges that you face, and together we can understand, frame, analyze, and address challenges in a systematic way so you see improvement in your overall business, is aligned with your strategy, and most importantly, you and your organization can incrementally change to transform and thrive in the future. If any of this sounds like something you might need, please reach out to me at dr.raj.ramesh@topsigma.com, and we'll get back in touch within a day. Thanks for watching my videos and for subscribing. www.topsigma.com www.linkedin.com/in/rajramesh

Lecture 2.7 Working with an AI team — [AI For Everyone | Andrew Ng] AI For Everyone lectures by Andrew Ng and our own Learning Notes.

Return on Investment (ROI)

How to compute the ROI on AI projects? Figuring out the ROI on AI implementations can be challenging. We offer some guidance on how to do that in this video. You can use this framework to make sure that you consider the many aspects of ROI that are especially required for AI projects. Contact the authors at: mehran.irdmousa@mziaviation.com, dr.raj.ramesh@gmail.com

Getting to AI ROI: Finding Value in Your Unstructured Content Artificial Intelligence is definitely having its moment, but if you’re like most companies, you haven’t yet been able to capture ROI from these exciting technologies. It seems complicated, expensive, requires specialized talent, crazy data requirements, and more. Your boss may have dropped a vague missive onto your desk asking you to “figure out how AI can help enhance our business.” You have piles and piles of unstructured content—contracts, documents, feedback, but you haven’t been able to drive value from your data. Where to even start?

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.

Using Historical Incident Data to Reduce Risks

• cfxGenie | CloudFabrix ...Find your IT blind spots, assess problem areas or gain new insights from a sampling of your IT incidents or tickets

CloudFabrix cfxGenie | Free IT Assessment Tool to Find Problem Areas & Accelerate AIOps Adoption CloudFabrix Software Inc Find your IT blind spots and accelerate AIOps adoption with cfxGenie - Map/Zone incidents into quadrants to identify problem areas for prioritization - Cluster incidents based on symptoms and features to understand key problem areas. Get started now with your AIOps transformation journey. Signup for free cfxGenie Cloud Access, visit http://www.cloudfabrix.com/cfxgenie/