InfoQ Homepage Machine Learning Content on InfoQ
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Challenges and Solutions for Building Machine Learning Systems
According to Camilla Montonen, the challenges of building machine learning systems are mostly creating and maintaining the model. MLOps platforms and solutions contain components needed to build machine systems. MLOps is not about the tools; it is a culture and a set of practices. Montonen suggests that we should bridge the divide between practices of data science and machine learning engineering.
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Ines Montani at QCon London: Economies of Scale Can’t Monopolise the AI Revolution
During her presentation at QCon London, Ines Montani, co-founder and CEO of explosion.ai (the maker of spaCy), stated that economies of scale are not enough to create monopolies in the AI space and that open-source techniques and models will allow everybody to keep up with the “Gen AI revolution”.
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Mistral Large Foundation Model Now Available on Amazon Bedrock
AWS announced the availability of the Mistral Large Foundation Model on Amazon Bedrock during the recent AWS Paris Summit. This announcement comes days after the release of Mistral AI Models on Amazon Bedrock.
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Airbnb Open-Sources its ML Feature Platform Chronon
Chronon, Airbnb's platform which creates the infrastructure required to transform raw data into ML-ready features, is now open source. As Airbnb ML infrastructure engineer Varant Zanoyan explains, Chronon supports a variety of data sources and aims to provide low-latency streaming.
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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.