Data Pipeline Deep Learning Demo 
PyImageSearch
DECEMBER 18, 2023
But again, stick around for a surprise demo at the end. ? From healthcare and education to finance and arts, the demos covered a wide spectrum of industries and use cases. It was a chance for participants to learn from each other and explore potential collaborations.
AWS Machine Learning Blog
APRIL 19, 2023
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. Thirdly, there are improvements to demos and the extension for Spark. The architecture of DJL is engine agnostic.
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AWS Machine Learning Blog
OCTOBER 23, 2024
Boost productivity – Empowers knowledge workers with the ability to automatically and reliably summarize reports and articles, quickly find answers, and extract valuable insights from unstructured data. The following demo shows Agent Creator in action. He focuses on Deep learning including NLP and Computer Vision domains.
The MLOps Blog
JUNE 27, 2023
” – James Tu, Research Scientist at Waabi Play with this project live For more: Dive into documentation Get in touch if you’d like to go through a custom demo with your team Comet ML Comet ML is a cloud-based experiment tracking and optimization platform.
AUGUST 17, 2023
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml
Iguazio
JANUARY 22, 2024
Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets. This helps cleanse the data.
The MLOps Blog
MARCH 28, 2024
TL;DR GPUs can greatly accelerate deep learning model training, as they are specialized for performing the tensor operations at the heart of neural networks. We’ll explore how factors like batch size, framework selection, and the design of your data pipeline can profoundly impact the efficient utilization of GPUs.
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