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Driving Efficiency with MLOps


The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. Here's what a machine learning model lifecycle looks like: According to Wikipedia, "MLOps ('Machine Learning' 'Operations') is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics." So, is MLOps just another fancy name for DevOps?

Introduction To MLOps


In this article, we'll get introduced to MLOps. We'll learn what MLOps is, the Data Science Lifecycle, the Machine Learning Lifecycle, multiple challenges we face with Machine Learning and then get to understand the importance of MLOps. Finally, we'll make a brief comparison of MLOps to DevOps and learn about various principles of MLOps along with specific benefits and business values of MLOps for businesses and organizations. Machine Learning Operation shortly known as MLOps focuses on empowering data scientists and application developers to help bring ML models to production. The MLOps makes it faster for experimentation and in the development of machine learning models. Moreover, faster deployment of models into production can be made.

Algorithmia founder on MLOps' promise and pitfalls


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.

MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo


Important Note: The intention of this course is to teach MLOps fundamentals, core idea, its principles, standards etc and NOT Azure ML. Azure demo section is just included as a proof to show the working of an end-to-end MLOps project. "MLOps is a culture with set of principles, guidelines defined in a machine learning world for smooth implementation and productionization of Machine learning models." Data scientists have been experimenting with machine learning models from long time, but to provide the real business value, it must be operationalized i.e. push the models to production and measure their performance against business goals. Unfortunately, due to the current challenges and an non systemization in ML lifecycle 80% of the models never make it to production and remain stagnated as an academic experiment only.

Recharge Your AI Initiatives With MLOps: Start Experimenting Now


In this era of industrialization for Artificial Intelligence (AI), enterprises are scrambling to embed AI across a plethora of use cases in hopes of achieving higher productivity and enhanced experiences. However, as AI permeates through different functions of an enterprise, managing the entire charter gets tough. Working with multiple Machine Learning (ML) models in both pilot and production can lead to chaos, stretched timelines to market, and stale models. As a result, we see enterprises hamstrung to successfully scale AI enterprise-wide. To overcome the challenges enterprises face in their ML journeys and ensure successful industrialization of AI, enterprises need to shift from the current method of model management to a faster and more agile format.