Find and compare the best Artificial Intelligence software for Amazon SageMaker Model Training in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Amazon SageMaker Model Training on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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TensorFlow
TensorFlow
Free 2 RatingsOpen source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test. -
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Discover the AI Pair Programming Extension for VSCode. Create your own AI Copilots using the Playground. Unleash new AI apps with the API. Unlock the Power Your Own AI Agents - Integrate Personalized Context & Knowledge Across All Coding Languages. You can easily train your AI Copilot using your own files on the Playground. Create and Share a Copilot within 5 Minutes or achieve Custom AI Copilot Solutions through the API A free extension that enhances coding skills by using code completion and chat assistant. Download the extension, enter your API key and begin AI-coding for FREE. Solution enhanced that allows AI agents to be created with context-specific information. You can create your own AI copilots, and integrate them wherever you like! API connection for AI-powered apps that handle all the complexities involved in fine-tuning LLMs. You can focus on the creative process without worrying about the technical issues.
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TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
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BERT is a large language model that can be used to pre-train language representations. Pre-training refers the process by which BERT is trained on large text sources such as Wikipedia. The training results can then be applied to other Natural Language Processing tasks (NLP), such as sentiment analysis and question answering. You can train many NLP models with AI Platform Training and BERT in just 30 minutes.
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DALL*E 2 can create realistic, original images and art from text descriptions. It can combine concepts and attributes. DALL*E2 can create new compositions by expanding images beyond what is on the original canvas. DALL*E 2 can edit existing images using a natural language caption. It can add or remove elements, while also taking into account shadows, reflections, textures, and other details. DALL*E 2 can understand the relationship between images, and the text that describes them. DALL*E 2 uses a process called "diffusion," where it starts with a pattern made up of random dots and then alters that pattern to create an image when it recognizes certain aspects of the image. Users cannot generate violent, political, or adult content according to our content policy. If our filters detect text prompts or image uploads that might violate our policies, we won't generate images. To guard against misuse, we have both automated and human monitoring systems.
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Amazon SageMaker
Amazon
Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility. -
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Hugging Face
Hugging Face
$9 per monthAutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool. -
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NVIDIA NeMo Megatron
NVIDIA
NVIDIA NeMo megatron is an end to-end framework that can be used to train and deploy LLMs with billions or trillions of parameters. NVIDIA NeMo Megatron is part of the NVIDIAAI platform and offers an efficient, cost-effective, and cost-effective containerized approach to building and deploying LLMs. It is designed for enterprise application development and builds upon the most advanced technologies of NVIDIA research. It provides an end-to–end workflow for automated distributed processing, training large-scale customized GPT-3 and T5 models, and deploying models to infer at scale. The validation of converged recipes that allow for training and inference is a key to unlocking the power and potential of LLMs. The hyperparameter tool makes it easy to customize models. It automatically searches for optimal hyperparameter configurations, performance, and training/inference for any given distributed GPU cluster configuration.
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