Best Practices

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• Rules of Machine Learning: Best Practices for ML Engineering | Martin Zinkevich - Google
• Best practices for performance and cost optimization for machine learning | 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)

3 Best Practices for Overcoming AI Obstacles to Build the Future-ready Enterprise Tata Consultancy Services (TCS), an IT services, consulting and business solutions organization that has been partnering with the world’s largest businesses in their transformation journeys for the last 50 years. A part of the Tata group, India's largest multinational business group, TCS has over 436,000 of the world’s best-trained consultants in 46 countries. The company is listed on the BSE (formerly Bombay Stock Exchange) and the NSE (National Stock Exchange) in India.

Visualization Best Practices for Explainable AI How do you know if a machine learning model is trustworthy or potentially biased? One of the top trends in analytics is the rise of explainable AI, the practice of presenting transparent views into how algorithms make decisions. In this session, we will dive deeper into understanding and explaining machine learning terms and charts to the business using Tableau. We will walk through real-world examples from healthcare, retail, marketing, banking, and other industries.

Conversational AI: Best Practices for Building Bots Conversational digital affordances are fast becoming a norm for users everywhere – from the office to the kitchen; from the car to the living room. We can type, tap or talk to all manner of device, apps, bots and agents to do all manner of things. When designed well, conversational AI experiences are natural, intuitive and efficient. In this session, we’ll provide practical guidance for building great bots. We’ll put the guidance to work using the Bot Builder v4 SDK, bot development tools and Microsoft Cognitive Services.

007. Machine learning best practices we've learned from hundreds of competitions - Ben Hamner Ben Hamner is Chief Scientist at Kaggle, leading its data science and development teams. He is the principal architect of many of Kaggle's most advanced machine learning projects including current work in Eagle Ford and GE's flight arrival prediction and optimization modeling.

Geo for Good 2019: Machine Learning Best Practices With the pace of modern machine learning, building and training neural networks is hard. Learn some best practices and sift through the overwhelming amount of information available with a focus on remote sensing. Talk presented by Chris Brown from Google.

(M25-L) Machine Learning Best Practices From the Amazon ML Solutions Lab This session will dive into key considerations for executives to think about as they design, deploy, and create a machine learning strategy. We will share customer success stories, and how to get started quickly. Session Speakers: Ryan Gavin, Larry Pizette (Session M25-L)

Best Practices for Verification and Validation In this webinar you will learn techniques and practices in Model-Based Design to verify and validate software designs and embedded code using MathWorks tools. We will address requirements driven development, model coverage testing, and static code analysis of embedded software. About the Presenters: Nishaat Vasi is a product marketing manager at MathWorks. Since joining MathWorks in 2007, Nishaat has partnered with customers involved in high-integrity applications to promote the adoption of MathWorks tools for embedded software verification. He holds an M.S. in electrical engineering from University of Massachusetts and a B.E. in electronics engineering from University of Mumbai. Jay Abraham is a product marketing manager at MathWorks. His area of expertise is in software tools for the verification of critical embedded applications. He has over 20 years of software and hardware design experience. Jay has an M.S. in computer engineering from Syracuse University and a B.S. in electrical engineering from Boston University. See what's new in the latest release of MATLAB and Simulink: http://goo.gl/3MdQK1 Download a trial: http://goo.gl/PSa78r

Pareto

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• Pareto Principle | Wikipedia
• Pareto Distribution | Wikipedia
• Pareto Chart | Wikipedia
• https://en.wikipedia.org/wiki/Pareto_priority_index Pareto Priority Index | Wikipedia
• AI Is Going to Change the 80/20 Rule | Michael Schrage - Harvard Business Review
• The Pareto Principle for Data Scientists | Pradeep Gulipalli - Tiger Analytics KDnuggets
• Pareto Distribution to Normal Distribution | result of strain - Medium
• Pareto Principle & the 80/20 Rule (Updated for 2020) | Maciej Duszynski - ResumeLab

If we consider the Pareto Principle when leveraging AI, we would employ human skills such as strategy, creativity, and collaboration for the 20 percent of tasks that drive 80 percent of business impact. Then, apply AI to the 80 percent of tasks that are routine-oriented and structured, making them ideal for automation. Artificial Intelligence, jobs and the Pareto Principle | Erin Murphy - Marchex

COVID-19 nETSETOS This video describes Pareto Distribution with given topics:- - 80-20 rule with graph - Parameter of Pareto Distribution - Application of Pareto Principle' - COVID - 19 Analysis with Pareto - How to Plot Pareto On Jupyter - Testing Pareto with the help of QQPlot

AI for physics & physics for AI Max Tegmark, MIT Abstract: After briefly reviewing how machine learning is becoming ever-more widely used in physics, I explore how ideas and methods from physics can help improve machine learning, focusing on automated discovery of mathematical formulas from data. I present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video. I also describe progress on symbolic regression, i.e., finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in general, functions of practical interest often exhibit symmetries, separability, compositionality and other simplifying properties. In this spirit, we have developed a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques that discover and exploit these simplifying properties, enabling significant improvement of state-of-the-art performance. Related papers: AI Feynman: a Physics-Inspired Method for Symbolic Regression - http://arxiv.org/abs/1905.11481 AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity - http://arxiv.org/abs/2006.10782 Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video - http://arxiv.org/abs/2005.11212