Don’t forget MLOps when deploying AI/ML Models [with Video]

MLOps navigate risk

Have you ever ridden a bike without brakes, or tried to ride with a flat? The best-case scenario is that you don’t go very far. Worse yet, it can hurt badly. I use this model performance analogy to describe the consequences of deploying artificial intelligence (AI) or machine learning (ML) models without MLOps. When models don’t perform optimally, it presents big challenges.

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Accelerating ML within CNN

At CNN, our mission is to inform, engage, and empower the world in a way that is trusted, timely, and transparent. This mission is more critical than ever as we face some of the most challenging times of our generation. As the world is becoming increasingly digital in nature, we are relentlessly focusing our mission to directly connect with our audience, understand what they care about most, and reach them in a way that is most accessible for their lifestyle. Our Data Intelligence team, in particular, leverages data and machine-learning capabilities to build innovative experiences for our audience and provides scalable solutions to CNN’s operations. As the world’s largest digital news destination, we averaged more than 200 million unique global visitors every month of 2020. Our catalog of raw audio and video footage also goes back several decades. Clearly, we have a lot of data!

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Get started with MLOps

A comprehensive MLOps tutorial with open source toolsPhoto by Stephen Dawson on UnsplashGetting machine learning (ML) models into production is hard work. Depending on the level of ambition, it can be surprisingly hard, actually. In this post I’ll go over my personal thoughts (with implementation examples) on principles suitable for the journey of putting ML models into production within a regulated industry; i.e. when everything needs to be auditable, compliant and in control — a situation where a hacked together API deployed on an EC2 instance is not going to cut it.

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Importance of the Big Picture in MLOps and the Deployment Ecosystem

mlops big picture

A study published in 2019 was highly cited (link) by various articles, it talks about why more than 80% of data science projects never make it to production. Since then Machine learning operationalization is catching pace, we could refer to Oct 2020 report, where MLOps seems to be trending across all platforms. Various organizations are setting up their data science teams composition from a development to deployment perspective.

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Towards MLOps: Technical capabilities of a Machine Learning platform

Large organisations rely increasingly on continuous ML pipelines to keep their ever-increasing number of machine-learned models up-to-date. Reliability and scalability of these ML pipelines are needed because disruptions in the pipeline can result in model staleness and other negative effects on the quality of downstream services supported by these models¹.

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Introduction to Observability in ITOM and AIOps

Observability ITOM AIops

First things first. Observability is inherent as a principle to a system and not something that is instilled. Here, we address observability as an open source-based solution in the context of insightful monitoring within the ITOM landscape. ITOM is now in the middle of addressing the needs of the expanding and dynamic nature of IT infrastructure as a function. It is no longer about being a monolithic computing stack. It is now beyond monitoring discrete infrastructure elements. 

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Best tools to Log and Manage ML Model building Metadata

When you’re developing machine learning models, you absolutely need to be able to reproduce experiments. It would be very unlucky to get a model with great results, which you can’t reproduce because you didn’t log the experiment. 

You can make your experiments reproducible by logging everything. There are several tools you can use for this. In this article, let’s look at some of the most popular tools, and see how to start logging your experiments’ metadata with them. You’ll learn how you can run a LightGBM experiment using these tools. 

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MLOps Vs Data Engineering: A guide for the perplexed

MLOps vs Data Engineering

Machine learning involves multiple stages and calls for a broad spectrum of skills. Advances in ML have led to the creation of new specialisations. The ML scene has many specialist roles, and their functionalities overlap to the extent that these designations are sometimes used interchangeably. Case in point — MLOps and data engineering.

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Best Workflow and Pipeline Orchestration Tools – Machine Learning Guide

Workflow/ Pipeline Orchestration tools

Machine learning is rampaging through the IT world, and driving a lot of high-end tech. It created a revolution of automation and flexibility for researchers and businesses. When it comes to machine learning, workflows (or pipelines) are an essential component that drives the overall project. In this article, we’ll explore what exactly workflows and pipelines are, and more than 10 tools that we can use to orchestrate workflows and pipelines.

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Why you should use Continuous Integration and Continuous Deployment in your Machine Learning projects

CI-CD - Continuous Integration/ Continuous Delivery

Continuous integration (CI), continuous delivery (CD) and continuous testing (CT) are at the core of Machine Learning Operation (MLOps) principles. If you’re a data scientist or machine learning engineer or an IT business leader investing in data science teams, and willing to extend their ML capabilities. MLOps might be the next step that delivers significant value to your business, speeding up development and implementation phases for any machine learning project.

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Optimizing Machine Learning: MLOps and its significant benefits

Machine woman with orb

MLOps ensures effective lifecycle management of ML models. Machine learning operations (MLOps) is a procedure that has recently entered the dictionary of technology organizations. More or less, MLOps is a method of optimizing the work process of data science and machine learning teams. It’s like DevOps from numerous points of view, additionally focusing on automation, continuous processes for testing and delivery, and collaboration between teams.

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What’s new about

Over time we got a tremendous amount of feedback from you. You asked how to use Neptune with spot instances and pipelines, how to organize things in a more hierarchical way, and many more and we wanted to do that. But adding those improvements to the current product in small increments was getting harder (and slower). Don’t get me wrong we love to iterate, but sometimes you need to take a deep deep deep breath, take a step back and rebuild the foundations. 

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What‌ ‌is‌ ‌DataOps,‌ ‌and‌ ‌why‌ ‌it’s‌ ‌a‌ ‌top‌ ‌trend‌

Platform Ops for AI

DataOps‌ ‌emerged‌ ‌seven‌ ‌years‌ ‌ago‌ to refer to ‌best‌ ‌practices‌ for ‌getting‌ ‌proper‌ ‌analytics,‌ ‌and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just‌ as‌ ‌the‌ ‌DevOps‌ ‌trend‌ ‌led‌ ‌to‌ ‌a‌ ‌better‌ ‌process‌ ‌for‌ ‌collaboration‌ ‌between‌ ‌‌developers‌ ‌and‌ ‌operations‌ ‌teams,‌ ‌DataOps‌ ‌refers‌ ‌to closer collaboration between various teams handling data and operations teams deploying data into applications.

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What is MLOps? Machine Learning Operations explained

Until recently, all of us were learning about the standard software development lifecycle (SDLC). It goes from requirement elicitation to designing to development to testing to deployment, and all the way down to maintenance. Now, we are at a stage where almost every organisation is trying to incorporate Machine Learning (ML) – often called Artificial Intelligence – into their product.

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