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Oracle makes its pitch for the enterprise cloud. Should CIOs listen?

CIO

In a cloud market dominated by three vendors, once cloud-denier Oracle is making a push for enterprise share gains, announcing expanded offerings and customer wins across the globe, including Japan , Mexico , and the Middle East. Companies wanting cloud-optimized versions of existing Oracle deployments will flock to OCI.”

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AWS vs. Azure vs. Google Cloud: Comparing Cloud Platforms

Kaseya

In this blog, we’ll compare the three leading public cloud providers, namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud. A subsidiary of Amazon, AWS was launched in 2006 and offers on-demand cloud computing services on a metered, pay-as-you-go basis. Google Cloud Platform Overview.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

When speaking of machine learning, we typically discuss data preparation or model building. The fusion of terms “machine learning” and “operations”, MLOps is a set of methods to automate the lifecycle of machine learning algorithms in production — from initial model training to deployment to retraining against new data.

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A Comprehensive Guide: What are the most popular Machine Learning Tools in 2023?

Openxcell

Machine Learning has noticed rapid growth—resulting in the creation of numerous tools and platforms for creating, evaluating, and deploying Machine Learning Models. The most popular Machine Learning tools have earned wide adoption in different industry settings and have active user and contributor groups.

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Meta alumni’s startup TrueFoundry raises $2.3 million to accelerate ML deployments

TechCrunch

A group of former Meta engineers is building a platform to help enterprises deploy machine learning models at the speed of big tech companies. Built on Kubernetes, the custom platform works as a cloud-agnostic solution that can be deployed on Amazon Web Services (AWS), Google Cloud and TensorFlow.

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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization. “Given these challenges, organizations today need to choose between two flawed approaches when it comes to developing machine learning. .

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Sequoia India’s Surge backs healthtech startup RedBrick AI in $4.6M funding

TechCrunch

“We see ourselves building the foundational layer of artificial intelligence in healthcare. Image Credits: RedBrick AI additionally provides APIs that machine learning engineers can integrate with their cloud solutions and clinical data stores, including hospital enterprise PACS servers.