Category: FAANG

Editor’s note: This is the second post in a series that explores a range of topics about upcoming AI regulation, including an overview of the the EU AI Act and Palantir solutions that foster and support regulatory compliance when using AI. This blog post provides an overview on how Palantir AIP empowers organizations to meet …

Read more “AI Systems Governance through the Palantir Platform”

David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu**: Model Development Team, Machine Learning Platform^: Content Demand Modeling Team A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing …

Read more “Introducing Configurable Metaflow”

Generative AI offers many benefits for both you, as a software provider, and your end-users. AI assistants can help users generate insights, get help, and find information that may be hard to surface using traditional means. In addition, they can help your employees reduce repetitive tasks and focus on high-value work. However, adding generative AI …

Read more “Add a generative AI experience to your website or web application with Amazon Q embedded”

Today, generative AI is giving organizations new ways to process and analyze data, discover hidden insights, increase productivity and build new applications. However, data sovereignty, regulatory compliance, and low-latency requirements can be a challenge. The need to keep sensitive data in certain locations, adhere to strict regulations, and respond swiftly can make it difficult to …

Read more “Find sensitive data faster (but safely) with Google Distributed Cloud’s gen AI search solution”

This post is co-written with Marta Cavalleri and Giovanni Germani from Fastweb, and Claudia Sacco and Andrea Policarpi from BIP xTech. AI’s transformative impact extends throughout the modern business landscape, with telecommunications emerging as a key area of innovation. Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potential of AI technologies early on …

Read more “How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model”

Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications. But RAG can be a double-edged sword: while the concept is straightforward – find relevant information and feed it …

Read more “Optimizing RAG retrieval: Test, tune, succeed”