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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. I’m joined by my co-host, Stephen, and with us today, we have Michal Tadeusiak , who will be answering questions about managing computer vision projects.

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Streamline diarization using AI as an assistive technology: ZOO Digital’s story

AWS Machine Learning Blog

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times. in a code subdirectory. in a code subdirectory.

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How Forethought saves over 66% in costs for generative AI models using Amazon SageMaker

AWS Machine Learning Blog

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Always make sure that sensitive data is handled securely to avoid potential security risks.

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LLM Fine-Tuning and Model Selection Using Neptune and Transformers

The MLOps Blog

But nowadays, it is used for various tasks, ranging from language modeling to computer vision and generative AI. Related post Tokenization in NLP: Types, Challenges, Examples, Tools Read more For training, we’ll create a so-called prompt that contains not only the question and the context but also the answer.

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How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

AWS Machine Learning Blog

In order to power these applications, as well as those using other data modalities like computer vision, we need a robust and efficient workflow to quickly annotate data, train and evaluate models, and iterate quickly. As part of this strategy, they developed an in-house passport analysis model to verify passenger IDs.

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning Blog

Although this post focuses on LLMs, most of its best practices are relevant for any kind of large-model training, including computer vision and multi-modal models, such as Stable Diffusion. The preparation of a natural language processing (NLP) dataset abounds with share-nothing parallelism opportunities.