Infinity
MARCH 3, 2022
So, in autumn 2021, when Facebook partnered up with Amazon and launched the Conversion API Gateway, it was a very exciting day for Facebook advertisers. When talking Facebook and data, you’re likely to come across two key models – the Conversion API Gateway and the Facebook Pixel, but what’s the difference?
AWS Machine Learning
JANUARY 10, 2024
ML Engineer at Tiger Analytics. The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. The approver approves the model by following the link in the email to an API Gateway endpoint.
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AWS Machine Learning
MARCH 1, 2024
Use hybrid search and semantic search options via SDK When you call the Retrieve API, Knowledge Bases for Amazon Bedrock selects the right search strategy for you to give you most relevant results. You have the option to override it to use either hybrid or semantic search in the API.
AWS Machine Learning
FEBRUARY 6, 2024
Companies face complex regulations and extensive approval requirements from governing bodies like the US Food and Drug Administration (FDA). Users then review and edit the documents, where necessary, and submit the same to the central governing bodies. His focus area is on Data, Analytics and Generative AI.
AWS Machine Learning
FEBRUARY 29, 2024
The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.
AWS Machine Learning
JULY 29, 2022
Large enterprises sometimes set up a center of excellence (CoE) to tackle the needs of different lines of business (LoBs) with innovative analytics and ML projects. To generate high-quality and performant ML models at scale, they need to do the following: Provide an easy way to access relevant data to their analytics and ML CoE.
AWS Machine Learning
JANUARY 5, 2024
MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models. Reusability – Without reusable MLOps frameworks, each model must be developed and governed separately, which adds to the overall effort and delays model operationalization.
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