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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. In our case, we chose to use a float[] as the input type and the built-in DJL classifications as the output type.

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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning Blog

of Large Model Inference (LMI) Deep Learning Containers (DLCs). For the TensorRT-LLM container, we use auto. option.tensor_parallel_degree=max option.max_rolling_batch_size=32 option.rolling_batch=auto option.model_loading_timeout = 7200 We package the serving.properties configuration file in the tar.gz

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Top Low-Code and No-Code Platforms for Data Science in 2023

ODSC - Open Data Science

Finally, H2O AutoML has the ability to support a wide range of machine learning tasks such as regression, time-series forecasting, anomaly detection, and classification. Auto-ViML : Like PyCaret, Auto-ViML is an open-source machine learning library in Python.

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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

2 The more interesting ones are the ones that don’t have the data science teams, or sometimes they don’t even have software developers in the way that they are companies that live in the 21st century. Obviously, different technologies are using what, for most of the time, deep learning, so different skills.

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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. It uses attention as the learning mechanism to achieve close to human-level performance. 24xlarge, ml.g5.48xlarge, ml.p4d.24xlarge,

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Deploying Large NLP Models: Infrastructure Cost Optimization

The MLOps Blog

These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera. 5 Leverage serverless computing for a pay-per-use model, lower operational overhead, and auto-scaling. 2020 or Hoffman et al.,

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems.