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Uber Creates GenAI Gateway Mirroring OpenAI API to Support over 60 LLM Use Cases
Uber created a unified platform for serving large language models (LLMs) from external vendors and self-hosted ones and opted to mirror OpenAI API to help with internal adoption. GenAI Gateway provides a consistent and efficient interface and serves over 60 distinct LLM use cases across many areas.
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AI Lab Extension Allows Podman Desktop Users to Experiment with LLMs Locally
One year after its 1.0 release, Podman Desktop announced the Podman AI Lab plugin promising to help developers start working with Large Language Models on their machines. Podman AI Lab streamlines LLM workflows featuring generative AI exploration, built-in recipe catalogue, curated models, local model serving, OpenAI-compatible API, code snippets, and playground environments.
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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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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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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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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.
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Cloudflare's Journey in ML and AI: MLOps Platform and Best Practices
Cloudflare's blog described its MLOps platform and best practices for running Artificial Intelligence (AI) deployment at scale. Cloudflare's products, including WAF attack scoring, bot management, and global threat identification, rely on constantly evolving Machine Learning (ML) models. These models are pivotal in enhancing customer protection and augmenting support services.
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Google Cloud Ops Agent Can Now Monitor Nvidia GPUs
Google Cloud announced that Ops Agent, the agent for collecting telemetry from Compute Engine instances, can now collect and aggregate metrics from NVIDIA GPUs on VMs.
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Kubeflow, the Machine Learning Toolkit for Kubernetes, Has Been Accepted as CNCF Incubation Project
The Cloud Native Computing Foundations (CNCF) has recently announced that Kubeflow, the toolkit to deploy machine learning (ML) workflow onto Kubernetes, was accepted as a CNCF incubating project after the vote of the Technical Oversight Committee (TOC).
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Feature Engineering at AirBnb Using Chronon
To increase productivity and scalability when creating new features to use in machine learning models, AirBnb has built Chronon, a solution to create the infrastructure required to turn raw data into features for training and inference.
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EU AI Act: the Regulatory Framework on the Usage of Machine Learning in the European Union
After the first publication of the proposal on the operation of machine learning applications in 2021, on June 14th negotiations have started for the realization of the legislation in the EU Council. The EU countries are expected to reach an agreement by the end of 2023. The EU Act takes a risk-based approach and plans to avoid disproportionate prescriptions when executing the regulations.
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Strategies and Principles to Scale and Evolve MLOps - at QCon London
At the QCon London conference, Hien Luu, senior engineering manager for the Machine Learning Platform at DoorDash, discussed strategies and principles for scaling and evolving MLOps. With 85% of ML projects failing, understanding MLOps at an engineering level is crucial. Luu shared three core principles: "Dream Big, Start Small," "1% Better Every Day," and "Customer Obsession."