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Tecton.ai nabs $35M Series B as it releases machine learning feature store

TechCrunch

Tecton.ai , the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A. “We help organizations put machine learning into production.

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Arthur.ai snags $15M Series A to grow machine learning monitoring tool

TechCrunch

At a time when more companies are building machine learning models, Arthur.ai As CEO and co-founder Adam Wenchel explains, data scientists build and test machine learning models in the lab under ideal conditions, but as these models are put into production, the performance can begin to deteriorate under real world scrutiny.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. Choose the us-east-1 AWS Region from the top right corner. Choose Manage model access.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Implementation of dynamic routing In this section, we explore different approaches to implementing dynamic routing on AWS, covering both built-in routing features and custom solutions that you can use as a starting point to build your own. Virginia) AWS Region and receives 50,000 history questions and 50,000 math questions per day.

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The New Tech Experience: Innovation, Optimization, and Collaboration

Speaker: Paul Weald, Contact Center Innovator

Learn how to streamline productivity and efficiency across your organization with machine learning and artificial intelligence! How you can leverage innovations in technology and machine learning to improve your customer experience and bottom line.

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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API. These components are illustrated in the following diagram.