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Databricks DBRX is now available in Amazon SageMaker JumpStart

AWS Machine Learning

The documents provided show that the development of these systems had a profound effect on the way people and goods were able to move around the world. The documents show that the development of railroads and steamships made it possible for goods to be transported more quickly and efficiently than ever before.

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2020 Call Center Metrics: 6 Key Metrics for Your Call Center Dashboard

Callminer

Average Handle Time (AHT) gives an accurate, real-time measurement of the usual amount of time it takes to handle an interaction from start to finish, from the initiation of the call to the time your organization’s call center agents are spending on the phone with individual callers and handling any follow-up tasks, such as documentation.

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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. To access the code and documentation, refer to the GitHub repo.

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Amazon Comprehend announces lower annotation limits for custom entity recognition

AWS Machine Learning

Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. All you need to do is load your dataset of documents and annotations, and use the Amazon Comprehend console, AWS CLI, or APIs to create the model.

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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. testingRTC simulates any user behavior using our powerful Nightwatch scripting, you can manage these scripts via our handy git integration.

Scripts 98
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Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

AWS Machine Learning

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. In this section, we only call out a few main steps with code snippets from the ready-to-use training script train_gpt_simple.py. The notebook uses the script data_prep_512.py Benchmarking performance. return loss.

Scripts 76
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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning

To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts. Create a HyperPod cluster with an SSSD-enabled lifecycle script Next, you create a HyperPod cluster with LDAPS/Active Directory integration.

Scripts 114