Best Artificial Intelligence Software for Amazon SageMaker Unified Studio
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Compare the Top Artificial Intelligence Software that integrates with Amazon SageMaker Unified Studio as of August 2025
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This a list of Artificial Intelligence software that integrates with Amazon SageMaker Unified Studio. Use the filters on the left to add additional filters for products that have integrations with Amazon SageMaker Unified Studio. View the products that work with Amazon SageMaker Unified Studio in the table below.
What is Artificial Intelligence Software for Amazon SageMaker Unified Studio?
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics. Compare and read user reviews of the best Artificial Intelligence software for Amazon SageMaker Unified Studio currently available using the table below. This list is updated regularly.
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Cohere
Cohere AI
Cohere is an enterprise AI platform that enables developers and businesses to build powerful language-based applications. Specializing in large language models (LLMs), Cohere provides solutions for text generation, summarization, and semantic search. Their model offerings include the Command family for high-performance language tasks and Aya Expanse for multilingual applications across 23 languages. Focused on security and customization, Cohere allows flexible deployment across major cloud providers, private cloud environments, or on-premises setups to meet diverse enterprise needs. The company collaborates with industry leaders like Oracle and Salesforce to integrate generative AI into business applications, improving automation and customer engagement. Additionally, Cohere For AI, their research lab, advances machine learning through open-source projects and a global research community.Starting Price: Free -
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PyTorch
PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. -
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Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
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Hugging Face
Hugging Face
Hugging Face is a leading platform for AI and machine learning, offering a vast hub for models, datasets, and tools for natural language processing (NLP) and beyond. The platform supports a wide range of applications, from text, image, and audio to 3D data analysis. Hugging Face fosters collaboration among researchers, developers, and companies by providing open-source tools like Transformers, Diffusers, and Tokenizers. It enables users to build, share, and access pre-trained models, accelerating AI development for a variety of industries.Starting Price: $9 per month -
5
Amazon Q Developer
Amazon
The most capable generative AI–powered assistant for software development. To accelerate building across the entire software development lifecycle, Amazon Q agents can autonomously perform a range of tasks–everything from implementing features, documenting, testing, reviewing, and refactoring code, to performing software upgrades. Amazon Q Developer makes the whole development lifecycle easier. Amazon Q Developer is an expert on AWS and is in the console to help optimize your cloud costs and resources, provide guidance on architectural best practices, investigate operational incidents, and diagnose and resolve networking issues. Amazon Q Developer agents accelerate large-scale enterprise workload transformations, including .NET porting from Windows to Linux, mainframe application modernization, VMware workload migration and modernization, and Java upgrades to streamline processes and reduce costs.Starting Price: $19/month -
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Amazon SageMaker Ground Truth
Amazon Web Services
Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.Starting Price: $0.08 per month -
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Stability AI
Stability AI
Designing and implementing solutions using collective intelligence and augmented technology. Stability AI is building open AI tools that will let us reach our potential. We’re a company of builders who care deeply about real-world implications and applications. Many of our most considerable advances grow from working across multiple teams. We are unafraid to go against established norms and explore creativity. Our primary drive is to generate breakthrough ideas and convert them into solutions. We respect innovation over tradition. We trust that our differences make us more robust, and so we seek reason within every difference of perspective. -
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Amazon SageMaker Debugger
Amazon
Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values. -
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.
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Amazon SageMaker Canvas
Amazon
Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own, without requiring any ML experience or having to write a single line of code. Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions. Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks. Boost collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas. With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions. -
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Amazon SageMaker Edge
Amazon
The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis. We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker Edge. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow. Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. -
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Amazon SageMaker Clarify
Amazon
Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model. -
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
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Amazon SageMaker Autopilot
Amazon
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. -
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Amazon Bedrock
Amazon
Amazon Bedrock is a fully managed service that simplifies building and scaling generative AI applications by providing access to a variety of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can experiment with these models, customize them using techniques like fine-tuning and Retrieval Augmented Generation (RAG), and create agents that interact with enterprise systems and data sources. As a serverless platform, Amazon Bedrock eliminates the need for infrastructure management, allowing seamless integration of generative AI capabilities into applications with a focus on security, privacy, and responsible AI practices. -
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LightOn
LightOn
LightOn is a generative AI solution designed for enterprises, enabling seamless integration of AI capabilities into business workflows while ensuring data confidentiality. It offers features such as private chat with large language models, enhanced knowledge retrieval through Retrieval-Augmented Generation (RAG), and customizable business cases, allowing organizations to tailor AI tools to their specific needs. Paradigm supports secure hosting compliant with SOC 2, ISO 27001, and HIPAA standards, and provides robust user management, access controls, and audit logs. Flat pricing for predictable costs, with flexible plans to adapt to your usage. Expert guidance for successful implementation. Tailored to your organization and specific needs. Tracked system activities with dedicated reports. Effortlessly stay compliant with enterprise-grade standards. -
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Llama
Meta
Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.
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