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Announcing our $50M Series C to build superhuman Speech AI models

AssemblyAI

There also now exist incredibly capable LLMs that can be used to ingest accurately recognized speech and generate summaries, insights, takeaways, and classifications that are enabling entirely new products and workflows to be created with voice data for the first time ever.

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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. This makes Auto-ViML an ideal tool for beginners and experts alike.

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

AWS Machine Learning Blog

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 Similarly, you can use log_prob as measure of confidence score for classification use cases.

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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

In this case, because we’re training the dataset to predict whether the transaction is fraudulent or valid, we use binary classification. For Training method and algorithms , select Auto. For Select the machine learning problem type , choose Binary classification. For Target , choose Class as the column to predict.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices. It’s a binary classification problem where the goal is to predict whether a customer is a credit risk. After you have completed the data preparation step, it’s time to train the classification model.

ML 84
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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. Rahul Huilgol is a Senior Software Development Engineer in Distributed Deep Learning at Amazon Web Services.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab. Cost-sensitive classification – In some applications, the cost of misclassification for different classes can be different.