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How to Visualize Deep Learning Models

The MLOps Blog

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Get Maximum Value from Your Visual Data

DataRobot

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. Multimodal Clustering. Interested to learn more?

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12 Standout Deep Learning Talks Coming to ODSC East this May

ODSC - Open Data Science

Deep learning continues to be a hot topic as increased demands for AI-driven applications, availability of data, and the need for increased explainability are pushing forward. So let’s take a quick dive and see some big sessions about deep learning coming up at ODSC East May 9th-11th.

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Introducing the Next Generation of Text AI for AI Cloud Platform

DataRobot

Simply fire up DataRobot’s unsupervised mode and use clustering or anomaly detection to help you discover patterns and insights with your data. Allow the platform to handle infrastructure and deep learning techniques so that you can maximize your focus on bringing value to your organization. Request a Demo.

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Generative AI foundation model training on Amazon SageMaker

AWS Machine Learning Blog

You can use SageMaker to scale your training cluster to thousands of accelerators, with your own choice of compute and optimize your workloads for performance with SageMaker distributed training libraries. After the training is complete, SageMaker spins down the cluster and the customer is billed for the net training time in seconds.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Enter a stack name, such as Demo-Redshift. You should see a new CloudFormation stack with the name Demo-Redshift being created. yaml locally.

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