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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools.

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Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach

AWS Machine Learning Blog

In this post, we explain how to automate this process. By adopting this automation, you can deploy consistent and standardized analytics environments across your organization, leading to increased team productivity and mitigating security risks associated with using one-time images.

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Choosing the Right Python Environment Tool for Your Next Project

Analytics Vidhya

And also Python is a flexible language that can be applied in various domains, including scientific programming, DevOps, automation, and web development. Introduction Setting up an environment is the first step in Python development, and it’s crucial because package management can be challenging with Python.

Python 399
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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.

Big Data 266
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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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Top 6 Kubernetes use cases

IBM Journey to AI blog

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

DevOps 323
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Top 25 AI Tools for Software Development in 2025

Marktechpost

AI-powered tools have become indispensable for automating tasks, boosting productivity, and improving decision-making. It automates code documentation and integrates seamlessly with AWS services, simplifying deployment processes. It automates model development and scales predictive analytics for businesses across industries.