Alternatives to Amazon SageMaker Ground Truth
Compare Amazon SageMaker Ground Truth alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Amazon SageMaker Ground Truth in 2026. Compare features, ratings, user reviews, pricing, and more from Amazon SageMaker Ground Truth competitors and alternatives in order to make an informed decision for your business.
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Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
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Ango Hub
iMerit
Ango Hub is a quality-focused, enterprise-ready data annotation platform for AI teams, available on cloud and on-premise. It supports computer vision, medical imaging, NLP, audio, video, and 3D point cloud annotation, powering use cases from autonomous driving and robotics to healthcare AI. Built for AI fine-tuning, RLHF, LLM evaluation, and human-in-the-loop workflows, Ango Hub boosts throughput with automation, model-assisted pre-labeling, and customizable QA while maintaining accuracy. Features include centralized instructions, review pipelines, issue tracking, and consensus across up to 30 annotators. With nearly twenty labeling tools—such as rotated bounding boxes, label relations, nested conditional questions, and table-based labeling—it supports both simple and complex projects. It also enables annotation pipelines for chain-of-thought reasoning and next-gen LLM training and enterprise-grade security with HIPAA compliance, SOC 2 certification, and role-based access controls. -
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Amazon Web Services (AWS)
Amazon
Amazon Web Services (AWS) is the world’s most comprehensive cloud platform, trusted by millions of customers across industries. From startups to global enterprises and government agencies, AWS provides on-demand solutions for compute, storage, networking, AI, analytics, and more. The platform empowers organizations to innovate faster, reduce costs, and scale globally with unmatched flexibility and reliability. With services like Amazon EC2 for compute, Amazon S3 for storage, SageMaker for AI/ML, and CloudFront for content delivery, AWS covers nearly every business and technical need. Its global infrastructure spans 120 availability zones across 38 regions, ensuring resilience, compliance, and security. Backed by the largest community of customers, partners, and developers, AWS continues to lead the cloud industry in innovation and operational expertise. -
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Dataloop AI
Dataloop AI
Manage unstructured data and pipelines to develop AI solutions at amazing speed. Enterprise-grade data platform for vision AI. Dataloop is a one-stop shop for building and deploying powerful computer vision pipelines data labeling, automating data ops, customizing production pipelines and weaving the human-in-the-loop for data validation. Our vision is to make machine learning-based systems accessible, affordable and scalable for all. Explore and analyze vast quantities of unstructured data from diverse sources. Rely on automated preprocessing and embeddings to identify similarities and find the data you need. Curate, version, clean, and route your data to wherever it’s needed to create exceptional AI applications. -
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Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service. Customers such as Duolingo, Samsung, GE, and Cook Pad use ECS to run their most sensitive and mission-critical applications because of its security, reliability, and scalability. ECS is a great choice to run containers for several reasons. First, you can choose to run your ECS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Second, ECS is used extensively within Amazon to power services such as Amazon SageMaker, AWS Batch, Amazon Lex, and Amazon.com’s recommendation engine, ensuring ECS is tested extensively for security, reliability, and availability.
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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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Labelbox
Labelbox
The training data platform for AI teams. A machine learning model is only as good as its training data. Labelbox is an end-to-end platform to create and manage high-quality training data all in one place, while supporting your production pipeline with powerful APIs. Powerful image labeling tool for image classification, object detection and segmentation. When every pixel matters, you need accurate and intuitive image segmentation tools. Customize the tools to support your specific use case, including instances, custom attributes and much more. Performant video labeling editor for cutting-edge computer vision. Label directly on the video up to 30 FPS with frame level. Additionally, Labelbox provides per frame label feature analytics enabling you to create better models faster. Creating training data for natural language intelligence has never been easier. Label text strings, conversations, paragraphs, and documents with fast & customizable classification. -
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Amazon Redshift
Amazon
More customers pick Amazon Redshift than any other cloud data warehouse. Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Companies like Lyft have grown with Redshift from startups to multi-billion dollar enterprises. No other data warehouse makes it as easy to gain new insights from all your data. With Redshift you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. Redshift lets you easily save the results of your queries back to your S3 data lake using open formats like Apache Parquet to further analyze from other analytics services like Amazon EMR, Amazon Athena, and Amazon SageMaker. Redshift is the world’s fastest cloud data warehouse and gets faster every year. For performance intensive workloads you can use the new RA3 instances to get up to 3x the performance of any cloud data warehouse.Starting Price: $0.25 per hour -
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Amazon Augmented AI (A2I)
Amazon
Amazon Augmented AI (Amazon A2I) makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers. Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. For example, extracting information from scanned mortgage application forms can require human review in some cases due to low-quality scans or poor handwriting. But building human review systems can be time consuming and expensive because it involves implementing complex processes or “workflows”, writing custom software to manage review tasks and results, and in many cases, managing large groups of reviewers. -
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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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 SageMaker Pipelines
Amazon
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK. -
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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 Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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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 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 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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With Amazon SageMaker Model Monitor, you can select the data you would like to monitor and analyze without the need to write any code. SageMaker Model Monitor lets you select data from a menu of options such as prediction output, and captures metadata such as timestamp, model name, and endpoint so you can analyze model predictions based on the metadata. You can specify the sampling rate of data capture as a percentage of overall traffic in the case of high volume real-time predictions, and the data is stored in your own Amazon S3 bucket. You can also encrypt this data, configure fine-grained security, define data retention policies, and implement access control mechanisms for secure access. Amazon SageMaker Model Monitor offers built-in analysis in the form of statistical rules, to detect drifts in data and model quality. You can also write custom rules and specify thresholds for each rule.
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. Built on Amazon DataZone, it integrates various AWS analytics and AI/ML services, such as Amazon EMR, AWS Glue, and Amazon Bedrock, into a single platform. Users can discover, access, and process data from various sources like Amazon S3 and Redshift, and develop generative AI applications. With tools for model development, governance, MLOps, and AI customization, SageMaker Unified Studio provides an efficient, secure, and collaborative environment for data teams.
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Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning 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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AWS Deep Learning Containers
Amazon
Deep Learning Containers are Docker images that are preinstalled and tested with the latest versions of popular deep learning frameworks. Deep Learning Containers lets you deploy custom ML environments quickly without building and optimizing your environments from scratch. Deploy deep learning environments in minutes using prepackaged and fully tested Docker images. Build custom ML workflows for training, validation, and deployment through integration with Amazon SageMaker, Amazon EKS, and Amazon ECS. -
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Appen
Appen
The Appen platform combines human intelligence from over one million people all over the world with cutting-edge models to create the highest-quality training data for your ML projects. Upload your data to our platform and we provide the annotations, judgments, and labels you need to create accurate ground truth for your models. High-quality data annotation is key for training any AI/ML model successfully. After all, this is how your model learns what judgments it should be making. Our platform combines human intelligence at scale with cutting-edge models to annotate all sorts of raw data, from text, to video, to images, to audio, to create the accurate ground truth needed for your models. Create and launch data annotation jobs easily through our plug and play graphical user interface, or programmatically through our API. -
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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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Innodata
Innodata
We Make Data for the World's Most Valuable Companies Innodata solves your toughest data engineering challenges using artificial intelligence and human expertise. Innodata provides the services and solutions you need to harness digital data at scale and drive digital disruption in your industry. We securely and efficiently collect & label your most complex and sensitive data, delivering near-100% accurate ground truth for AI and ML models. Our easy-to-use API ingests your unstructured data (such as contracts and medical records) and generates normalized, schema-compliant structured XML for your downstream applications and analytics. We ensure that your mission-critical databases are accurate and always up-to-date. -
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AWS AI Factories
Amazon
AWS AI Factories is a fully-managed solution that embeds high-performance AI infrastructure directly into a customer’s own data center. You supply the space and power, and AWS deploys a dedicated, secure AI environment optimized for training and inference. It includes leading AI accelerators (such as AWS Trainium chips or NVIDIA GPUs), low-latency networking, high-performance storage, and integration with AWS’s AI services, such as Amazon SageMaker and Amazon Bedrock, giving immediate access to foundational models and AI tools without separate licensing or contracts. AWS handles the full deployment, maintenance, and management, eliminating the typical months-long effort to build comparable infrastructure. Each deployment is isolated, operating like a private AWS Region, which meets strict data sovereignty, compliance, and regulatory requirements, making it particularly suited for sectors with sensitive data. -
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Label Studio
Label Studio
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input. -
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Labellerr
Labellerr
Labellerr is a data annotation platform designed to expedite the preparation of high-quality labeled datasets for AI and machine learning models. It supports various data types, including images, videos, text, PDFs, and audio, catering to diverse annotation needs. The platform offers automated annotation features, such as model-assisted labeling and active learning, to accelerate the labeling process. Additionally, Labellerr provides advanced analytics and smart quality assurance tools to ensure the accuracy and reliability of annotations. For projects requiring specialized knowledge, Labellerr offers expert-in-the-loop services, including access to professionals in fields like healthcare and automotive. -
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AWS HealthLake
Amazon
Extract meaning from unstructured data with integrated Amazon Comprehend Medical for easy search and querying. Make predictions on health data using Amazon Athena queries, Amazon SageMaker ML models, and Amazon QuickSight analytics. Support interoperable standards such as the Fast Healthcare Interoperability Resources (FHIR). Run medical imaging applications in the cloud to increase scale and reduce costs. AWS HealthLake is a HIPAA-eligible service offering healthcare and life sciences companies a chronological view of individual or patient population health data for query and analytics at scale. Analyze population health trends, predict outcomes, and manage costs with advanced analytics tools and ML models. Identify opportunities to close gaps in care and deliver targeted interventions with a longitudinal view of patient journeys. Apply advanced analytics and ML to newly structured data to optimize appointment scheduling and reduce unnecessary procedures. -
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Encord
Encord
Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own. Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance. Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models. Improve model accuracy by identifying errors and biases in your data, labels and models. -
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Umbrelly Cloud
Umbrelly.cloud
Umbrelly Cloud is an AWS optimization platform designed to significantly reduce cloud expenditures. By leveraging shared AWS plans, Umbrelly unlocks savings of up to 25% on services like EC2, EBS, ECS, SageMaker, ElastiCache, Redshift, OpenSearch, RDS, and Lambda. Customers typically realize average cost reductions of 19.3% without compromising service levels or performance. Umbrelly’s automated optimization process ensures full compliance with AWS Terms of Service. Experience tangible cost savings, improved resource utilization, and enhanced financial predictability with Umbrelly Cloud. -
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Cohere Rerank
Cohere
Cohere Rerank is a powerful semantic search tool that refines enterprise search and retrieval by precisely ranking results. It processes a query and a list of documents, ordering them from most to least semantically relevant, and assigns a relevance score between 0 and 1 to each document. This ensures that only the most pertinent documents are passed into your RAG pipeline and agentic workflows, reducing token use, minimizing latency, and boosting accuracy. The latest model, Rerank v3.5, supports English and multilingual documents, as well as semi-structured data like JSON, with a context length of 4096 tokens. Long documents are automatically chunked, and the highest relevance score among chunks is used for ranking. Rerank can be integrated into existing keyword or semantic search systems with minimal code changes, enhancing the relevance of search results. It is accessible via Cohere's API and is compatible with various platforms, including Amazon Bedrock and SageMaker. -
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AWS IoT Core
Amazon
AWS IoT Core lets you connect IoT devices to the AWS cloud without the need to provision or manage servers. AWS IoT Core can support billions of devices and trillions of messages, and can process and route those messages to AWS endpoints and to other devices reliably and securely. With AWS IoT Core, your applications can keep track of and communicate with all your devices, all the time, even when they aren’t connected. AWS IoT Core also makes it easy to use AWS and Amazon services like AWS Lambda, Amazon Kinesis, Amazon S3, Amazon SageMaker, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, Amazon QuickSight, and Alexa Voice Service to build IoT applications that gather, process, analyze and act on data generated by connected devices, without having to manage any infrastructure. AWS IoT Core allows you to connect any number of devices to the cloud and to other devices without requiring you to provision or manage servers. -
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Amazon S3 Express One Zone
Amazon
Amazon S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. It offers data access speeds up to 10 times faster and requests costs up to 50% lower than S3 Standard. With S3 Express One Zone, you can select a specific AWS Availability Zone within an AWS Region to store your data, allowing you to co-locate your storage and compute resources in the same Availability Zone to further optimize performance, which helps lower compute costs and run workloads faster. Data is stored in a different bucket type, an S3 directory bucket, which supports hundreds of thousands of requests per second. Additionally, you can use S3 Express One Zone with services such as Amazon SageMaker Model Training, Amazon Athena, Amazon EMR, and AWS Glue Data Catalog to accelerate your machine learning and analytics workloads. -
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Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
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LinkedAI
LinkedAi
We label your data with the higher quality standards to fulfill the needs of the most complex AI projects, using our proprietary labeling platform. Now you can get back to creating the products your customers love. We provide an end-to-end solution for image annotation with fast labeling tools, synthetic data generation, data management, automation features and annotation services on-demand with integrated tooling to accelerate and finish computer vision projects. When every pixel matters, you need accurate, AI-powered intuitive image annotation tools to support your specific use case, including instances, attributes and much more. Our in-house highly trained data labelers are able to deal with any data challenge. As your data labeling needs grow over time, you can count on us to scale the workforce necessary to meet your goals, and in contrast to crowdsourcing platforms your data quality will not suffer. -
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GRAX
GRAX
GRAX is the leader in Salesforce Data Protection throughout its lifecycle, where you can own and control 100% of your Salesforce data. GRAX is disrupting the Salesforce data protection industry by combining Salesforce data backup, recovery, and archiving with scalable data operations (Data Management - DataOps) for a true Salesforce Data Lifecycle Management solution. The same tool that lets you backup, archive, and recover your Salesforce historical data is also the best way to capture, ingest, and pipe those historical versions of your data into your DataOps ecosystem. Reuse and consume your CRM data in industry tools, such as AWS SageMaker, Power BI by Azure, AWS Redshift, Tableau, AWS QuickSight, Snowflake, Google BigQuery, and many others.Starting Price: $9,000/mo per Salesforce Org -
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Amazon SageMaker HyperPod
Amazon
Amazon SageMaker HyperPod is a purpose-built, resilient compute infrastructure that simplifies and accelerates the development of large AI and machine-learning models by handling distributed training, fine-tuning, and inference across clusters with hundreds or thousands of accelerators, including GPUs and AWS Trainium chips. It removes the heavy lifting involved in building and managing ML infrastructure by providing persistent clusters that automatically detect and repair hardware failures, automatically resume workloads, and optimize checkpointing to minimize interruption risk, enabling months-long training jobs without disruption. HyperPod offers centralized resource governance; administrators can set priorities, quotas, and task-preemption rules so compute resources are allocated efficiently among tasks and teams, maximizing utilization and reducing idle time. It also supports “recipes” and pre-configured settings to quickly fine-tune or customize foundation models. -
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Amazon EC2 G4 Instances
Amazon
Amazon EC2 G4 instances are optimized for machine learning inference and graphics-intensive applications. It offers a choice between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad). G4dn instances combine NVIDIA T4 GPUs with custom Intel Cascade Lake CPUs, providing a balance of compute, memory, and networking resources. These instances are ideal for deploying machine learning models, video transcoding, game streaming, and graphics rendering. G4ad instances, featuring AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, deliver cost-effective solutions for graphics workloads. Both G4dn and G4ad instances support Amazon Elastic Inference, allowing users to attach low-cost GPU-powered inference acceleration to Amazon EC2 and reduce deep learning inference costs. They are available in various sizes to accommodate different performance needs and are integrated with AWS services such as Amazon SageMaker, Amazon ECS, and Amazon EKS. -
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Nexdata
Nexdata
Nexdata's AI Data Annotation Platform is a robust solution designed to meet diverse data annotation needs, supporting various types such as 3D point cloud fusion, pixel-level segmentation, speech recognition, speech synthesis, entity relationship, and video segmentation. The platform features a built-in pre-recognition engine that facilitates human-machine interaction and semi-automatic labeling, enhancing labeling efficiency by over 30%. To ensure high-quality data output, it incorporates multi-level quality inspection management functions and supports flexible task distribution workflows, including package-based and item-based assignments. Data security is prioritized through multi-role, multi-level authority management, template watermarking, log auditing, login verification, and API authorization management. The platform offers flexible deployment options, including public cloud deployment for rapid, independent system setup with exclusive computing resources. -
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Mindkosh
Mindkosh AI
Mindkosh is the data platform for curating, labeling and validating datasets for your AI projects. Our industry leading data annotation platform combines collaborative features with AI-assisted annotation features to provide a comprehensive suite of tools to label any kind of data, be it Images, videos or 3D pointclouds such as those from Lidar. For images, Mindkosh offers semi-automatic segmentation, pre-labeling for bounding boxes and automatic OCR. For videos, automatic interpolation can reduce massive amounts of manual annotation. And for lidar, 1-click annotation allows you to create cuboids in just 1 click! If you are simply looking to get your data labeled, our high quality data annotation services combined with an easy to use Python SDK and web-based review platform, provide an unmatched experience.Starting Price: $30/user/month -
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Scale Data Engine
Scale AI
Scale Data Engine helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Data Engine. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Scale Data Engine supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata. -
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V7 Darwin
V7
V7 Darwin is a powerful AI-driven platform for labeling and training data that streamlines the process of annotating images, videos, and other data types. By using AI-assisted tools, V7 Darwin enables faster, more accurate labeling for a variety of use cases such as machine learning model training, object detection, and medical imaging. The platform supports multiple types of annotations, including keypoints, bounding boxes, and segmentation masks. It integrates with various workflows through APIs, SDKs, and custom integrations, making it an ideal solution for businesses seeking high-quality data for their AI projects.Starting Price: $150 -
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SUPA
SUPA
Supercharge your AI with human expertise. SUPA is here to help you streamline your data at any stage: collection, curation, annotation, model validation and human feedback. Better data, better AI. SUPA is trusted by AI teams to solve their human data needs. Our lightning-fast machine-led labeling platform integrates with our diverse workforce to provide high-quality data at scale, making it the most cost-efficient solution for your AI. We do next-gen labeling for next-gen AI. Our use cases range from LLM generation, data curation, Segment Anything (SAM) output validation to sketch generation and semantic segmentation. -
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Label Your Data
Label Your Data
Label Your Data stands for exceptional data annotation service. With PCI DSS (level 1) and ISO:27001 certifications, and adherence to GDPR, CCPA, and HIPAA, we guarantee your data is handled securely. Our services cover Automotive, Robotics, Fintech, Healthcare, E-commerce, Manufacturing, and Insurance industries. On a mission to co-build an AI-driven economy, we offer customized solutions for both enterprise and R&D projects with 500+ annotators on board. From Computer Vision and NLP annotation to data processing, Label Your Data delivers accurate and secure results to scale your AI projects. -
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Hive Data
Hive
Create training datasets for computer vision models with our fully managed solution. We believe that data labeling is the most important factor in building effective deep learning models. We are committed to being the field's leading data labeling platform and helping companies take full advantage of AI's capabilities. Organize your media with discrete categories. Identify items of interest with one or many bounding boxes. Like bounding boxes, but with additional precision. Annotate objects with accurate width, depth, and height. Classify each pixel of an image. Mark individual points in an image. Annotate straight lines in an image. Measure, yaw, pitch, and roll of an item of interest. Annotate timestamps in video and audio content. Annotate freeform lines in an image.Starting Price: $25 per 1,000 annotations -
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Supervisely
Supervisely
The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more.
