Remove 2012 Remove APIs Remove Metrics
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Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

AWS Machine Learning

This process enhances task-specific model performance, allowing the model to handle custom use cases with task-specific performance metrics that meet or surpass more powerful models like Anthropic Claude 3 Sonnet or Anthropic Claude 3 Opus. Under Output data , for S3 location , enter the S3 path for the bucket storing fine-tuning metrics.

APIs 130
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

APIs 78
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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Amazon Bedrock makes it straightforward to adopt any of these choices by providing a common set of APIs, industry-leading embedding models, security, governance, and observability. In this post, we discuss use cases, features, and steps to set up and retrieve information using these vector databases.

APIs 99
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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning

The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managed API.

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Deep demand forecasting with Amazon SageMaker

AWS Machine Learning

The input data is a multi-variate time series that includes hourly electricity consumption of 321 users from 2012–2014. Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis. For HPO, we use the RRSE as the evaluation metric for all the three algorithms.

Metrics 85
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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

By uploading a small set of training images, Amazon Rekognition automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. For more details on the different metrics such as precision, recall, and F1, refer to Metrics for evaluation your model.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

Upon a new model version registration, someone with the authority to approve the model based on the metrics should approve or reject the model. You can use Boto3 APIs as shown the following example, or you can use the AWS Management Console to create the model package. We combine both policies to get the following final policy.