Remove APIs Remove Customer Support Remove Engineering
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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

Generative AI has transformed customer support, offering businesses the ability to respond faster, more accurately, and with greater personalization. AI agents , powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses.

APIs 129
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Unleashing the power of generative AI: Verisk’s journey to an Instant Insight Engine for enhanced customer support

AWS Machine Learning

In this post, we describe the development of the customer support process in FAST incorporating generative AI, the data, the architecture, and the evaluation of the results. Conversational AI assistants are rapidly transforming customer and employee support.

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

AWS Machine Learning

Lets say the task at hand is to predict the root cause categories (Customer Education, Feature Request, Software Defect, Documentation Improvement, Security Awareness, and Billing Inquiry) for customer support cases. Sonnet prediction accuracy through prompt engineering. client = boto3.client("bedrock-runtime",

Education 110
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Amazon Bedrock Flows is now generally available with enhanced safety and traceability

AWS Machine Learning

Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. Bedrock Flows makes it easier for developers and businesses to harness the power of generative AI, enabling you to create more sophisticated and efficient AI-driven solutions for your customers.

APIs 120
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Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

AWS Machine Learning

In this post, we describe the enhancements to the forecasting capabilities of SageMaker Canvas and guide you on using its user interface (UI) and AutoML APIs for time-series forecasting. While the SageMaker Canvas UI offers a code-free visual interface, the APIs empower developers to interact with these features programmatically.

APIs 119
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Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

AWS Machine Learning

With this launch, you can programmatically run notebooks as jobs using APIs provided by Amazon SageMaker Pipelines , the ML workflow orchestration feature of Amazon SageMaker. Furthermore, you can create a multi-step ML workflow with multiple dependent notebooks using these APIs.

APIs 112
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Improve the productivity of your customer support and project management teams using Amazon Q Business and Atlassian Jira

AWS Machine Learning

Effective customer support and project management are critical aspects of providing effective customer relationship management. You can authenticate Amazon Q Business to Jira using basic authentication with a Jira ID and Jira API token. For Jira ID , enter the user name for the API token. Choose Save.