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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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Data Scientists in the Age of AI Agents and AutoML

Towards AI

In this regard, I believe the future of data science belongs to those: who can connect the dots and deliver results across the entire data lifecycle. You have to understand data, how to extract value from them and how to monitor model performances. These two languages cover most data science workflows.

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

By establishing standardized workflows, automating repetitive tasks, and implementing robust monitoring and governance mechanisms, MLOps enables organizations to accelerate model development, improve deployment reliability, and maximize the value derived from ML initiatives.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

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MLOps Helps Mitigate the Unforeseen in AI Projects

DataRobot Blog

You need full visibility and automation to rapidly correct your business course and to reflect on daily changes. Imagine yourself as a pilot operating aircraft through a thunderstorm; you have all the dashboards and automated systems that inform you about any risks. How long will it take to replace the model? Request a Demo.

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Josh Tobin of Gantry on Continual Learning Benefits and Challenges

ODSC - Open Data Science

As newer fields emerge within data science and the research is still hard to grasp, sometimes it’s best to talk to the experts and pioneers of the field. That’s the data drift problem, aka the performance drift problem. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.