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Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI

AWS Machine Learning

For this post, we use a dataset called sql-create-context , which contains samples of natural language instructions, schema definitions and the corresponding SQL query. We also included a data exploration script to analyze the length of input and output tokens. We encourage you to read this post while running the code in the notebook.

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Fine-tune large multimodal models using Amazon SageMaker

AWS Machine Learning

The prospect of fine-tuning open source multimodal models like LLaVA are highly appealing because of their cost effectiveness, scalability, and impressive performance on multimodal benchmarks. It sets up a SageMaker training job to run the custom training script from LLaVA. For full parameter fine-tuning, ml.p4d.24xlarge

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Service Standards and Service Excellence….are Not the Same Thing!

Up Your Service

In such cases, standards provide a useful benchmark, especially for new employees learning how to do the job. But service standards can also be too rigid, or too scripted, and inadvertently degrade a service experience or cause damage to a service brand. Standards can also support your brand.

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11 Types of Bad Customer Service (and How To Avoid Them)

Help Scout

This is one situation in which the company should have definitely folded. Read Email Response Times: Benchmarks and Tips for Support for practical advice. This one was a robot, but there are plenty of real humans who aren’t able to break from the script even when the play suddenly has a new act. Allow for human judgement.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning

This was the perfect place to start for our prototype—not only would Axfood gain a new AI/ML platform, but we would also get a chance to benchmark our ML capabilities and learn from leading AWS experts. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

These include metrics such as ROUGE or cosine similarity for text similarity, and specific benchmarks for assessing toxicity (Detoxify), prompt stereotyping (cross-entropy loss), or factual knowledge (HELM, LAMA). When you evaluate a case, evaluate the definitions in order and label the case with the first definition that fits.

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