Question: Can you suggest a cross-platform engine that accelerates machine learning training and inference?

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ONNX Runtime

For a cross-platform engine to accelerate machine learning training and inference, ONNX Runtime is a great option. It can handle training and inference on Windows, Linux, Mac, iOS, Android and web browsers. ONNX Runtime has hardware acceleration on CPU, GPU, NPU and other devices, and has multilanguage APIs that make it easy to embed into projects. It supports generative AI, on-device training for better privacy, and has a wide hardware support, so it can be used in a lot of different situations.

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PyTorch

Another top contender is PyTorch, a flexible and efficient end-to-end machine learning framework. PyTorch supports distributed training and performance optimization, with libraries for computer vision, natural language processing and more. It also supports deployment on iOS and Android devices and native support for the ONNX format, so it's good for both quick prototyping and large-scale production. It also has a large ecosystem and rich set of resources, including libraries for model interpretability.

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TensorFlow

TensorFlow is another popular machine learning foundation that works on many environments. It has multiple levels of abstraction, including high-level APIs for easy model creation and distributed training for different hardware configurations. TensorFlow supports on-device machine learning and several tools like TensorFlow Lite and TensorFlow.js for deployment. It's good for both beginners and experts, with abundant resources and libraries for domain-specific tasks.

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NVIDIA AI Platform

For those who want a platform that marries AI models and services, the NVIDIA AI Platform is a full-on package. It combines accelerated infrastructure with enterprise-grade software to simplify the AI workflow. Among its features are multi-node training, generative AI support, and data analytics improvements, so it's good for businesses that want to run AI at scale.

Additional AI Projects

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Cerebras

Accelerate AI training with a platform that combines AI supercomputers, model services, and cloud options to speed up large language model development.

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MLflow

Manage the full lifecycle of ML projects, from experimentation to production, with a single environment for tracking, visualizing, and deploying models.

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Lambda

Provision scalable NVIDIA GPU instances and clusters on-demand or reserved, with pre-configured ML environments and transparent pricing.

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TrueFoundry

Accelerate ML and LLM development with fast deployment, cost optimization, and simplified workflows, reducing production costs by 30-40%.

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Hugging Face

Explore and collaborate on over 400,000 models, 150,000 applications, and 100,000 public datasets across various modalities in a unified platform.

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Together

Accelerate AI model development with optimized training and inference, scalable infrastructure, and collaboration tools for enterprise customers.

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Mystic

Deploy and scale Machine Learning models with serverless GPU inference, automating scaling and cost optimization across cloud providers.

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Numenta

Run large AI models on CPUs with peak performance, multi-tenancy, and seamless scaling, while maintaining full control over models and data.

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Lamini

Rapidly develop and manage custom LLMs on proprietary data, optimizing performance and ensuring safety, with flexible deployment options and high-throughput inference.

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Predibase

Fine-tune and serve large language models efficiently and cost-effectively, with features like quantization, low-rank adaptation, and memory-efficient distributed training.

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Keras

Accelerate machine learning development with a flexible, high-level API that supports multiple backend frameworks and scales to large industrial applications.

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Replicate

Run open-source machine learning models with one-line deployment, fine-tuning, and custom model support, scaling automatically to meet traffic demands.

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Groq

Accelerates AI model inference with high-speed compute, flexible cloud and on-premise deployment, and energy efficiency for large-scale applications.

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Coral

Runs AI inference directly on devices, delivering efficient, private, fast, and offline AI applications for a wide range of use cases and industries.

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Hailo

High-performance AI processors for edge devices, enabling efficient deep learning, computer vision, and generative AI capabilities in various industries.

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Zerve

Securely deploy and run GenAI and Large Language Models within your own architecture, with fine-grained GPU control and accelerated data science workflows.

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Jina

Boost search capabilities with AI-powered tools for multimodal data, including embeddings, rerankers, and prompt optimizers, supporting over 100 languages.

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ThirdAI

Run private, custom AI models on commodity hardware with sub-millisecond latency inference, no specialized hardware required, for various applications.

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Ultralytics

Build and deploy accurate AI models without coding, leveraging pre-trained templates, mobile testing, and multi-format deployment for streamlined computer vision projects.

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Vespa

Combines search in structured data, text, and vectors in one query, enabling scalable and efficient machine-learned model inference for production-ready applications.