Find and compare the best Artificial Intelligence software for Amazon SageMaker in 2024
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Use the comparison tool below to compare the top Artificial Intelligence software for Amazon SageMaker on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Union Cloud
Union.ai
Free (Flyte)Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness. -
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Domino Enterprise MLOps Platform
Domino Data Lab
1 RatingThe Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation. -
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Dataiku DSS
Dataiku
1 RatingData analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs. -
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Ray
Anyscale
FreeYou can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution. -
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JetBrains Datalore
JetBrains
$19.90 per monthDatalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time. -
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Flyte
Union.ai
FreeThe workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone. -
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Neptune.ai
Neptune.ai
$49 per monthAll your model metadata can be stored, retrieved, displayed, sorted, compared, and viewed in one place. Know which data, parameters, and codes every model was trained on. All metrics, charts, and other ML metadata should be organized in one place. Your model training will be reproducible and comparable with little effort. Do not waste time searching for spreadsheets or folders containing models and configs. Everything is at your fingertips. Context switching can be reduced by having all the information you need in one place. A dashboard designed for ML model management will help you quickly find the information you need. We optimize loggers/databases/dashboards to work for millions of experiments and models. We provide excellent examples and documentation to help you get started. You shouldn't run experiments again if you have forgotten to track parameters. Make sure experiments are reproducible and only run one time. -
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Qwak
Qwak
Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code. -
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Comet
Comet
$179 per user per monthManage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders. -
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Coral
Coral
$0.0000004 per tokenCoral is a knowledge assistant that helps enterprises boost the productivity of their strategic teams. Coral can be asked a question to find answers in your documents, backed up with citations. The generated responses can be verified with citations, reducing the risk of hallucinations. Explain large language models for a non-technical retailer executive. Coral can be tailored to fit your specific job functions, whether you are in finance, support, or sales. Connect data sources to its knowledge base to make it more powerful. Coral integrates with your ecosystem via 100+ integrations that span CRMs, collaboration tools and databases. Manage Coral in your own secure cloud, either through cloud partners (AWS GCP OCI etc.). Virtual private clouds. Coral data will never be sent to Cohere. It stays in your own environment. Coral's responses are based on your documents and data. Citations will be displayed to show where the responses are based. -
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Amazon Transcribe
Amazon
$0.00013Amazon Transcribe allows developers to add speech-to-text capabilities to their applications. Computers cannot search for and analyze audio data. Recorded speech must be converted into text before it can be used for applications. Customers used to have to work with transcription companies that required them to sign lengthy contracts and were difficult to integrate into their technology stacks. Many of these providers use outdated technology which is difficult to adapt to different situations, such as low-fidelity phone audio that is common in contact centers. This results in poor accuracy. Amazon Transcribe uses deep learning called automatic speech recognition (ASR), to quickly convert speech into text. Amazon Transcribe is a tool that can be used to transcribe customer calls, automate subtitles, and generate metadata to support media assets in order to create a searchable archive. -
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NVIDIA Triton Inference Server
NVIDIA
FreeNVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production. -
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BentoML
BentoML
FreeYour ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs. -
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Superwise
Superwise
FreeYou can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise. -
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Cameralyze
Cameralyze
$29 per monthEmpower your product with AI. Our platform provides a wide range of pre-built models, as well as a user-friendly interface with no-code for custom models. Integrate AI seamlessly into applications to gain a competitive advantage. Sentiment analysis is also known as opinion-mining. It is the process of extracting and categorizing subjective information from text, such as reviews, comments on social media, or customer feedback. In recent years, this technology has grown in importance as more companies use it to understand the opinions and needs of their customers and make data-driven decision that can improve products, services, or marketing strategies. Sentiment analysis helps companies to understand customer feedback, and make data-driven decision that can improve their products, service, and marketing strategies. -
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Akira AI
Akira AI
$15 per monthAkira AI provides the best explainability, accuracy and scalability in their application. Responsible AI can help you create applications that are transparent, robust, reliable, and fair. Transforming enterprise work with computer vision techniques, machine learning solutions and end-to-end deployment of models. ML model problems can be solved with actionable insights. Build AI systems that are compliant and responsible with proactive bias monitoring capabilities. Open the AI blackbox to optimize and understand the correct inner workings. Intelligent automation-enabled process reduce operational hindrances, and optimize workforce productivity. Build AI-quality AI solutions that optimize, monitor, and explain ML models. Improve performance, transparency and robustness. Model velocity can improve AI outcomes and model performance. -
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ZenML
ZenML
FreeSimplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code. -
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Amazon Augmented AI (A2I)
Amazon
Amazon Augmented AI (Amazon A2I), makes it easy to create the workflows needed for human review of ML prediction. Amazon A2I provides human review for all developers. This removes the undifferentiated work involved in building systems that require human review or managing large numbers. Machine learning applications often require humans to review low confidence predictions in order to verify that the results are accurate. In some cases, such as extracting information from scanned mortgage applications forms, human review may be required due to poor scan quality or handwriting. However, building human review systems can be costly and time-consuming because it involves complex processes or "workflows", creating custom software to manage review tasks, results, and managing large numbers of reviewers. -
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Privacera
Privacera
Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system. -
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Wallaroo.AI
Wallaroo.AI
Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models. -
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Aporia
Aporia
Our easy-to-use monitor builder allows you to create customized monitors for your machinelearning models. Get alerts for issues such as concept drift, model performance degradation and bias. Aporia can seamlessly integrate with any ML infrastructure. It doesn't matter if it's a FastAPI server built on top of Kubernetes or an open-source deployment tool such as MLFlow, or a machine-learning platform like AWS Sagemaker. Zoom in on specific data segments to track the model's behavior. Unexpected biases, underperformance, drifting characteristics, and data integrity issues can be identified. You need the right tools to quickly identify the root cause of problems in your ML models. Our investigation toolbox allows you to go deeper than model monitoring and take a deep look at model performance, data segments or distribution. -
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Fiddler
Fiddler
Fiddler is a pioneer in enterprise Model Performance Management. Data Science, MLOps, and LOB teams use Fiddler to monitor, explain, analyze, and improve their models and build trust into AI. The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler seamlessly integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale and increase revenue. -
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Amazon SageMaker Ground Truth
Amazon Web Services
$0.08 per monthAmazon SageMaker lets you identify raw data, such as images, text files and videos. You can also add descriptive labels to generate synthetic data and create high-quality training data sets to support your machine learning (ML). SageMaker has two options: Amazon SageMaker Ground Truth Plus or Amazon SageMaker Ground Truth. These options allow you to either use an expert workforce or create and manage your data labeling workflows. data labeling. SageMaker GroundTruth allows you to manage and create your data labeling workflows. SageMaker Ground Truth, a data labeling tool, makes data labeling simple. It also allows you to use human annotators via Amazon Mechanical Turk or third-party providers. -
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Label Studio
Label Studio
The most flexible data annotation software. Quickly installable. Create custom UIs, or use pre-built labeling template. Layouts and templates that can be customized to fit your dataset and workflow. Detect objects in images. Supported are boxes, polygons and key points. Partition an image into multiple segments. Use ML models to optimize and pre-label the process. Webhooks, Python SDK and API allow you authenticate, create tasks, import projects, manage model predictions and more. ML backend integration allows you to save time by using predictions as a tool for your labeling process. Connect to cloud object storage directly and label data there with S3 and GCP. Data Manager allows you to manage and prepare your datasets using advanced filters. Support multiple projects, use-cases, and data types on one platform. You can preview the labeling interface as you type in the configuration. You can see live serialization updates at the bottom of the page. -
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Cranium
Cranium
The AI revolution has arrived. The regulatory landscape is constantly changing, and innovation is moving at lightning speed. How can you ensure that your AI systems, as well as those of your vendors, remain compliant, secure, and trustworthy? Cranium helps cybersecurity teams and data scientists understand how AI impacts their systems, data, or services. Secure your organization's AI systems and machine learning systems without disrupting your workflow to ensure compliance and trustworthiness. Protect your AI models from adversarial threats while maintaining the ability to train, test and deploy them.
