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Scaling Enterprise Machine Learning Through Governance & MLOps

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In my roles as a customer success and business development executive covering Artificial Intelligence & Machine Learning (AIML) at leading tech companies, I've spoken with executives, data scientists and IT managers across startups, Fortune 500 and Global 1000 companies about their AIML needs. After discussing what is AIML, platform features or API services easiest to use for non-specialist, companies get stuck on an equally important component of enterprise AIML, governance of operations. Companies get caught up in the hype led by consultants and industry media outlets that promote AIML led digital transformation is happening across every industry, in companies of all sizes with millions of models being deployed to production weekly. AIML software vendors promise adoption of their solution enables instant production readiness enabling their customers to, "Build and deploy a machine learning model in 9 minutes," with limited or no expertise. The reality is not quite as advertised but I'll help you on your journey by discussing why deploying ML in production can be difficult, provide a way to assess your return on investment (ROI) with AIML, how to create a comprehensive ML platform and provide a framework for assessing your organization's AIML maturity to better determine the capabilities you need to acquire to improve your org's proficiency. There are many definitions for Machine Learning Operations (MLOps) and governance but to keep things simple, I'll define governance and MLOps as the best practices and policies for businesses to run AIML successfully.


Pinaki Laskar on LinkedIn: #MLOps #AI #machinelearning

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #MLOps is the key for productionized ML system? ML model code is only a small part ( 5–10%) of a successful ML system, and the objective should be to create value by placing ML models into production. F1 score) while stakeholders focus on business metrics (e.g. Improving labelling consistency is an iterative process, so consider repeating the process until disagreements are resolved as far as possible. For instance, partial automation with a human in the loop can be an ideal design for AI-based interpretation of medical scans, with human judgement coming in for cases where prediction confidence is low.


Algorithmia founder on MLOps' promise and pitfalls

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All the sessions from Transform 2021 are available on-demand now. MLOps, a compound of machine learning and information technology operations, sits at the intersection of developer operations (DevOps), data engineering, and machine learning. The goal of MLOps is to get machine learning algorithms into production. While similar to DevOps, MLOps relies on different roles and skill sets: data scientists who specialize in algorithms, mathematics, simulations, and developer tools, and operations administrators who focus on upgrades, production deployments, resource and data management, and security. While there is significant business value to MLOps, implementation can be difficult in the absence of a robust data strategy.


Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: 9781098115784: Amazon.com: Books

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In engineering disciplines, design patterns capture best practices and solutions to commonly occurring problems. They codify the knowledge and experience of experts into advice that all practitioners can follow. This book is a catalog of machine learning design patterns that we have observed in the course of working with hundreds of machine learning teams. Who Is This Book For? Introductory machine learning books usually focus on the what and how of machine learning (ML). They then explain the mathematical aspects of new methods from AI research labs and teach how to use AI frameworks to implement these methods.


MLOps: The Ultimate Guide

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You'll find conflicting definitions on MLOps: Is it a movement, a philosophy, a platform, or a job title? Most are either far too vague -- a "philosophy" -- or far too specific, just referring to one specific toolset. MLOps is a collection of industry-accepted best practices to manage code, data, and models in your machine learning team. MLOps is a broad field, so we'll take a high-level view of the landscape then dive into topics you'll encounter when adopting it. Deciding on the best tools to use and how to get started can be hard.


What is MLOps?

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I recently started a new job at a Machine Learning startup. I've given up trying to explain what I do to non-technical friends and family (my mum still just tells people I work with computers). For those of you who at least understand that "AI" is just an overused marketing term for Machine Learning, I can break it down for you using the latest buzzword in the field: The term "MLOps" (a compound of Machine Learning and Operations) refers to the practice of deploying, managing and monitoring machine learning models in production. It takes best practices from the field of DevOps and utilises them for the unique challenges that arise running machine learning systems in production. The term is relatively new and has grown rapidly in usage over the last year and is a direct result of a maturing Machine Learning landscape.


Best 9 Books To Start Your MLOps Journey

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MLOps is a systematic operationalization of machine learning workflows. It is the practice of applying DevOps and ITOps practice to data science, AI, machine learning workflows to make the process efficient, flexible, reproducible, and manageable. This article is a handpicked list of some of the best books you should read as a data scientist, machine learning engineer, DevOps engineer, and project manager to learn about the practice and practically apply it to machine learning workflows. Accelerated DevOps with AI, ML & RPA is a walkthrough story of how artificial intelligence and machine learning is applied to IT operations and how IT operations is applied to artificial intelligence and machine learning development workflow. It explores the impact of AI and machine learning in today's digital space and takes predictive speculation of the further effects the technology will have on IT operations.


The secret formula for MLOps success

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"I was a happy data scientist until we decided it was time for deploying our models." It is common among many DS/ML teams that when the time for productionizing the model comes, they are caught off guard due to poor planning. Of course, thinking solely about the end is far from enough, the stages beforehand are equally as important. To reach the end of any endeavor we need to be strategic, the same applies for succeeding with MLOps. One such strategy is to take on a less challenging problem or part of it in the beginning and find the easiest way it can be solved.


Check Out Five Leading AI Start-ups in MLops in 2021

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With technological advancements, AI applications have accelerated rapid growth as there is a huge demand for infrastructure and software that supports AI applications. Many start-ups have been joining this field of MLops. Data Robot wants to own a company's AI lifecycle starting from data preparation till the production deployment. The features of Data Robots include relating to the web UI which can simplify the data and it can also assist users by automatically clearing previous data. The Humble AI feature adds to the company as it lets the user place additional guardrails in case of any low probability event occurring during the prediction. The unique quality of Data Robots is that they can install their own data center and bare metal in Hadoop clusters and can deploy cloud services to private and managed companies.


Does MLOps Live Upto The Hype?

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Machine Learning (ML) model metrics are designed to monitor performance. But when a model goes into production, many factors influence its performance. The traditional checkpoints may no longer help as organisations look to scale these models (think: scaling from a million to billion credit card users). This is why experts advocate for MLOps, a branch of ML that brings together all the nice things from DevOps and ML. Though a few experts hold MLOps as the best solution available right now, it's still beset by ambiguities.