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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,

APIs 139
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Automate cloud security vulnerability assessment and alerting using Amazon Bedrock

AWS Machine Learning

This solution uses a GuardDuty findings notification through EventBridge to invoke AWS Step Functions , a serverless orchestration engine, which runs a state machine. The Lambda function calls Anthropic’s Claude 3 Sonnet model through Amazon Bedrock APIs with the input request.

APIs 73
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Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock

AWS Machine Learning

We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. Solution overview The batch inference feature in Amazon Bedrock provides a scalable solution for processing large volumes of data across various domains.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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Streamline Sales Processes with Enterprise CPQ

Cincom

Integration with Existing Systems: APIs facilitate data sharing between CPQ and other core platforms like CRM, ERP, accounting, e-commerce, and more. These Configure, Price, Quote engines can encode deeply technical manufacturing specifications required for specialized equipment. What Makes Cincom’s CPQ Solutions Stand Out?

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

Together, these additions help agronomists, software developers, ML engineers, data scientists, and remote sensing teams provide scalable, valuable decision-making support systems to farmers. These differences in satellite images and frequencies also lead to differences in API capabilities and features.

APIs 86
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

Now we have low-code and no-code tools like Amazon SageMaker Data Wrangler , AWS Glue DataBrew , and Amazon SageMaker Canvas to assist with data feature engineering. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools.