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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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Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

As AI models grow and data volumes expand, databases must scale horizontally, to allow organisations to add capacity without significant downtime or performance degradation. Additionally, they accelerate time-to-market for AI-driven innovations by enabling rapid data ingestion and retrieval, facilitating faster experimentation.

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Automate the deployment of an Amazon Forecast time-series forecasting model

AWS Machine Learning Blog

You can implement this workflow in Forecast either from the AWS Management Console , the AWS Command Line Interface (AWS CLI), via API calls using Python notebooks , or via automation solutions. The console and AWS CLI methods are best suited for quick experimentation to check the feasibility of time series forecasting using your data.

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Foundational models at the edge

IBM Journey to AI blog

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps.

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The Three Big Announcements by Databricks AI Team in June 2024

Marktechpost

In June 2024, Databricks made three significant announcements that have garnered considerable attention in the data science and engineering communities. These announcements focus on enhancing user experience, optimizing data management, and streamlining data engineering workflows.

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Celebrating 40 years of Db2: Running the world’s mission critical workloads

IBM Journey to AI blog

Forrester’s 2022 Total Economic Impact Report for Data Management highlights the impact Db2 and the IBM data management portfolio is having for customers: Return on investment (ROI) of 241% and payback <6 months. Both services offer independent compute and storage scaling, high availability, and automated DBA tasks.

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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

Data scientists often spend up to 80% of their time on data engineering in data science projects. Objective of Data Engineering: The main goal is to transform raw data into structured data suitable for downstream tasks such as machine learning.

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