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Google Announces Agent Builder, Expanded Gemini 1.5, Open-Source Additions
At the Google Cloud Next 2024 event, Google announced the launch of Vertex AI Agent Builder, the public preview of Google's most advanced generative AI model, Gemini 1.5 Pro, and the addition of open-source language models to the Vertex AI platform.
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QCon London: Lessons Learned from Building LinkedIn’s AI/ML Data Platform
At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. He specifically delved into Venice DB, the NoSQL data store used for feature persistence. The presenter shared the lessons learned from evolving and operating the platform, including cluster management and library versioning.
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Netflix Uses Metaflow to Manage Hundreds of AI/ML Applications at Scale
Netflix recently published how its Machine Learning Platform (MLP) team provides an ecosystem around Metaflow, an open-source machine learning infrastructure framework. By creating various integrations for Metaflow, Netflix already has hundreds of Metaflow projects maintained by multiple engineering teams.
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Wear OS Gets New, More Efficient Text-to-Speech Engine
Google has announced a new text-to-speech engine for Wear OS, its Android variant aimed at smartwatches and other wearables, supporting over 50 languages and faster than its predecessor thanks to using smaller ML models.
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Google BigQuery Introduces Vector Search
Google recently announced that BigQuery now supports vector search. The new functionality enables vector similarity search required by data and AI use cases such as semantic search, similarity detection, and retrieval-augmented generation (RAG) with a large language model (LLM).
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Anthropic Unveils Claude 3 Models, Highlighting Opus and Its Near-Human Capabilities
Anthropic has introduced the Claude 3 family models, surpassing other industry models such as GPT-4. The Claude 3 family consists of three distinct models: Haiku, Sonnet, and Opus, arranged in ascending order of capability, each designed to cater to diverse user needs in terms of intelligence, speed, and cost.
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Baseline OpenAI End-to-End Chat Reference Architecture
Microsoft published the baseline OpenAI end-to-end chat reference architecture. This baseline contains information about components, flows and security. There are also details about performance, monitoring and deployment guidance. Microsoft also prepared the reference implementation to deploy and run the solution.
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Google Announces Multi-Modal Gemini 1.5 with Million Token Context Length
One week after announcing Gemini 1.0 Ultra, Google announced additional details about its next generation model, Gemini 1.5. The new iteration comes with an expansion of its context window and the adoption of a "Mixture of Experts" (MoE) architecture, promising to make the AI both faster and more efficient. The new model also includes expanded multimodal capabilities.
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NVIDIA Unveils Chat with RTX, a Locally Run AI Chatbot
NVIDIA has introduced Chat with RTX, allowing users to build their own personalized chatbot experience. Unlike many cloud-based solutions, Chat with RTX operates entirely on a local Windows PC or workstation, offering enhanced data privacy and control.
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Instacart Creates Real-Time Item Availability Architecture with ML and Event Processing
Instacart combined machine learning with event-based processing to create an architecture that provides customers with an indication of item availability in near real-time. The new solution helped to improve user satisfaction and retention by reducing order cancellations due to out-of-stock items. The team also created a multi-model experimentation framework to help enhance model quality.
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Hugging Face and Google Cloud Announce Collaboration
Hugging Face and Google Cloud have announced a strategic alliance to advance machine learning and open AI research. Google Cloud customers, Hugging Face Hub users, and open source are the three main focuses of the strategic partnership. Google wants to make cutting-edge AI discoveries available through Hugging Face's open-source frameworks.
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Spotify's Approach to Leverage Recursive Embedding and Clustering to Enhanced Data Explainability
One of the main challenges of any online business is to get actionable insight from their data for decision-making. Spotify shares its methodology and experience to solve this problem by clustering diverse data sets through a unique method involving dimensionality reduction, recursion, and supervised machine learning.
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Custom GPTs from OpenAI May Leak Sensitive Information
After it was reported that OpenAI has started rolling out its new GPT Store, it was also discovered that some of the data they’re built on is easily exposed. Multiple groups have begun finding that the system has the potential to leak otherwise sensitive information.
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How LinkedIn Uses Machine Learning to Address Content-Related Threats and Abuse
To help detect and remove content that violates their standard policies, LinkedIn has been using its AutoML framework, which trains classifiers and experiments with multiple model architectures in parallel, explain LinkedIn engineers Shubham Agarwal and Rishi Gupta.
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Griffin 2.0: Instacart Revamps Its Machine Learning Platform
Instacart created the next-generation platform based on experiences using the original Griffin machine-learning platform. The company wanted to improve user experience and help manage all ML workloads. The revamped platform leverages the latest developments in MLOps and introduces new capabilities for current and future applications.