Keras Alternatives

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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PyTorch

If you're looking for a Keras alternative, PyTorch is a popular choice. It offers fast experimentation and production with a high-level front-end and distributed training abilities. PyTorch can be used for a variety of tasks, from rapid prototyping to large-scale production workloads, and it comes with a wealth of libraries and tools, including support for the ONNX format and a C++ front-end for high-performance work.

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TensorFlow

Another top contender is TensorFlow. The open-source software offers a flexible environment with multiple levels of abstraction, including the high-level Keras API, eager execution for rapid iteration, and the Distribution Strategy API for distributed training. TensorFlow can be used for a variety of tasks, including on-device machine learning and graph neural networks, and it offers tools to accelerate modeling and deployment, making it good for beginners and experts.

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MLflow

If you want a full MLOps package, MLflow is a good choice. It makes it easier to develop and deploy machine learning and generative AI projects by providing a unified environment for managing the entire ML project lifecycle. MLflow supports popular deep learning frameworks like PyTorch and TensorFlow, and offers a wealth of resources for experiment tracking, model management and workflow optimization.

More Alternatives to Keras

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Clarifai

Rapidly develop, deploy, and operate AI projects at scale with automated workflows, standardized development, and built-in security and access controls.

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LastMile AI

Streamline generative AI application development with automated evaluators, debuggers, and expert support, enabling confident productionization and optimal performance.

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Anyscale

Instantly build, run, and scale AI applications with optimal performance and efficiency, leveraging automatic resource allocation and smart instance management.

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DataRobot AI Platform

Centralize and govern AI workflows, deploy at scale, and maximize business value with enterprise monitoring and control.

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Humanloop

Streamline Large Language Model development with collaborative workflows, evaluation tools, and customization options for efficient, reliable, and differentiated AI performance.

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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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Athina

Experiment, measure, and optimize AI applications with real-time performance tracking, cost monitoring, and customizable alerts for confident deployment.

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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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MindsDB

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

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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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Braintrust

Unified platform for building, evaluating, and integrating AI, streamlining development with features like evaluations, logging, and proxy access to multiple models.

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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.

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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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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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Dify

Build and run generative AI apps with a graphical interface, custom agents, and advanced tools for secure, efficient, and autonomous AI development.

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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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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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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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HoneyHive

Collaborative LLMOps environment for testing, evaluating, and deploying GenAI applications, with features for observability, dataset management, and prompt optimization.

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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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Aible

Deploys custom generative AI applications in minutes, providing fast time-to-delivery and secure access to structured and unstructured data in customers' private clouds.