Tag: ML Engineers
Moderate audio and text chats using AWS AI services and LLMs | Amazon Web Services
Online gaming and social communities offer voice and text chat functionality for their users to communicate. Although voice and text chat often support friendly...
Breaking News
Hugging Face AI Platform Riddled With 100 Malicious Code-Execution Models
Researchers have discovered about 100 machine learning (ML) models that have been uploaded to the Hugging Face artificial intelligence (AI) platform and potentially enable...
Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation | Amazon Web Services
AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres...
Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access | Amazon Web Services
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs...
How Booking.com modernized its ML experimentation framework with Amazon SageMaker | Amazon Web Services
This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com.
As a global leader in...
Introducing Allora: A Self-Improving Decentralized AI Network
Polychain, Framework, Blockchain Capital, and CoinFund backed, Allora is poised to transform crypto through the power of decentralized AI
NEW YORK–(BUSINESS WIRE)–Upshot, a leader in...
Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs | Amazon Web Services
Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Examples...
Use mobility data to derive insights using Amazon SageMaker geospatial capabilities | Amazon Web Services
Geospatial data is data about specific locations on the earth’s surface. It can represent a geographical area as a whole or it can represent...
Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator | Amazon Web Services
This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine...
Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions | Amazon Web Services
Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering...
Experience the new and improved Amazon SageMaker Studio | Amazon Web Services
Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and...
Evaluate large language models for quality and responsibility | Amazon Web Services
The risks associated with generative AI have been well-publicized. Toxicity, bias, escaped PII, and hallucinations negatively impact an organization’s reputation and damage customer trust....
Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services | Amazon Web Services
In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with...
Latest Intelligence
