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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Deployment times stretched for months and required a team of three system engineers and four ML engineers to keep everything running smoothly. With just one part-time ML engineer for support, our average issue backlog with the vendor is practically non-existent.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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Bring your own AI using Amazon SageMaker with Salesforce Data Cloud

AWS Machine Learning Blog

As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

I see so many of these job seekers, especially on the MLOps side or the ML engineer side. You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” Aurimas: Was it content generation? It’s two things. It’s also not the product.

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Enabling production-grade generative AI: New capabilities lower costs, streamline production, and boost security

AWS Machine Learning Blog

As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. While these layers provide different points of entry, the fundamental truth is that every generative AI journey starts at the foundational bottom layer.

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Xavier Conort, Co-Founder and CPO of FeatureByte – Interview Series

Unite.AI

It became apparent to both Razi and me that we had the opportunity to make a significant impact by radically simplifying the feature engineering process and providing data scientists and ML engineers with the right tools and user experience for seamless feature experimentation and feature serving.