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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

And it strikes the ideal balance between intelligence and speed – qualities especially critical for enterprise use cases. Current evaluations from Anthropic suggest that the Claude 3 model family outperforms comparable models in math word problem solving (MATH) and multilingual math (MGSM) benchmarks, critical benchmarks used today for LLMs.

Benchmark 133
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Databricks DBRX is now available in Amazon SageMaker JumpStart

AWS Machine Learning

Code generation DBRX models demonstrate benchmarked strengths for coding tasks. user Write a Python script to read a CSV file containing stock prices and plot the closing prices over time using Matplotlib. The file should have columns named 'Date' and 'Close' for this script to work correctly.

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Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

AWS Machine Learning

SageMaker LMI containers provide two ways to deploy the model: A no-code option where we just provide a serving.properties file with the required configurations Bring your own inference script We look at both solutions and go over the configurations and the inference script ( model.py ). The container requires your model.py

Scripts 79
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Customer Success in SaaS: A Complete Guide & Best Practices

Totango

SaaS success outcomes can be defined in terms of measurable digital benchmarks. Laying out a customer journey map allows CS teams to set measurable goals for each customer experience stage, set benchmarks, and implement automated strategies corresponding to mapped stages. Both automated and manual tracking serve a role.

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Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

AWS Machine Learning

Pre-trained language models (PLMs) are undergoing rapid commercial and enterprise adoption in the areas of productivity tools, customer service, search and recommendations, business process automation, and content creation. We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. training.py ).

Metrics 92
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Scaling distributed training with AWS Trainium and Amazon EKS

AWS Machine Learning

Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud. These images contain the Neuron SDK (excluding the Neuron driver, which runs directly on the Trn1 instances), PyTorch training script, and required dependencies.

Scripts 90
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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. These benchmarks have leaderboards that can be used to compare and contrast evaluated models.