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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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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

You would address it in a completely different way, depending on what’s the problem. What I mean is when data scientists are working hand in hand with software engineers or MLOps engineers, that would then take over or wrap up the solution. What role have Auto ML models played in computer vision consultant capacity?

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?

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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

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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. Varun Shah is a Software Engineer working on Amazon SageMaker Studio at Amazon Web Services.

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Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.