Question: Can you recommend a platform that allows me to deploy machine learning models to autoscaling infrastructure with minimal setup?

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Modelbit

If you're looking for a platform that lets you easily deploy machine learning models to autoscaling infrastructure with minimal setup, Modelbit could be the way to go. It lets you deploy custom and open-source models to autoscaling infrastructure with built-in MLOps tools, automatic synchronization through Git and industry-standard security. The company also offers a variety of pricing plans, including an on-demand option and enterprise deals.

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Anyscale

Another good option is Anyscale, which offers a full-stack platform for building, deploying and scaling AI applications. It offers features like smart instance management, heterogeneous node control and GPU/CPU fractioning to optimize resource usage. Anyscale supports a variety of AI models and offers cost savings on spot instances, so it's a good option for those who need to scale up and down.

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RunPod

If you're looking for a cloud-native option, RunPod offers a globally distributed GPU cloud for training and running AI models. It offers serverless ML inference with autoscaling and job queuing, instant hot-reloading and support for a variety of GPU workloads. RunPod also offers a variety of preconfigured templates and a CLI tool for easy provisioning and deployment.

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Mystic

Last, Mystic offers a low-cost, scalable foundation for deploying and scaling ML models with serverless GPU inference. It works with major cloud providers and offers cost optimization options like spot instances and parallelized GPU usage. Mystic is geared for teams that focus on model development, with automated scaling and managed Kubernetes environments that handle the grunt work.

Additional AI Projects

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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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Cerebrium

Scalable serverless GPU infrastructure for building and deploying machine learning models, with high performance, cost-effectiveness, and ease of use.

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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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dstack

Automates infrastructure provisioning for AI model development, training, and deployment across multiple cloud services and data centers, streamlining complex workflows.

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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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Instill

Automates data, model, and pipeline orchestration for generative AI, freeing teams to focus on AI use cases, with 10x faster app development.

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Obviously AI

Automate data science tasks to build and deploy industry-leading predictive models in minutes, without coding, for classification, regression, and time series forecasting.

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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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KeaML

Streamline AI development with pre-configured environments, optimized resources, and seamless integrations for fast algorithm development, training, and deployment.

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Dataloop

Unify data, models, and workflows in one environment, automating pipelines and incorporating human feedback to accelerate AI application development and improve quality.

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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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PI.EXCHANGE

Build predictive machine learning models without coding, leveraging an end-to-end pipeline for data preparation, model development, and deployment in a collaborative environment.

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Keywords AI

Streamline AI application development with a unified platform offering scalable API endpoints, easy integration, and optimized tools for development and monitoring.

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Tromero

Train and deploy custom AI models with ease, reducing costs up to 50% and maintaining full control over data and models for enhanced security.

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AIML API

Access over 100 AI models through a single API, with serverless inference, flat pricing, and fast response times, to accelerate machine learning project development.

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Openlayer

Build and deploy high-quality AI models with robust testing, evaluation, and observability tools, ensuring reliable performance and trustworthiness in production.

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Airtrain AI

Experiment with 27+ large language models, fine-tune on your data, and compare results without coding, reducing costs by up to 90%.

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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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UBOS

Build and deploy custom Generative AI and AI applications in a browser with no setup, using low-code tools and templates, and single-click cloud deployment.

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MindsDB

Connects data to AI with 200+ integrations, allowing developers to create tailored AI solutions using their own enterprise data and multiple AI engines.