83 Integrations with Amazon SageMaker
View a list of Amazon SageMaker integrations and software that integrates with Amazon SageMaker below. Compare the best Amazon SageMaker integrations as well as features, ratings, user reviews, and pricing of software that integrates with Amazon SageMaker. Here are the current Amazon SageMaker integrations in 2024:
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1
New Relic
New Relic
There are an estimated 25 million engineers in the world across dozens of distinct functions. As every company becomes a software company, engineers are using New Relic to gather real-time insights and trending data about the performance of their software so they can be more resilient and deliver exceptional customer experiences. Only New Relic provides an all-in-one platform that is built and sold as a unified experience. With New Relic, customers get access to a secure telemetry cloud for all metrics, events, logs, and traces; powerful full-stack analysis tools; and simple, transparent usage-based pricing with only 2 key metrics. New Relic has also curated one of the industry’s largest ecosystems of open source integrations, making it easy for every engineer to get started with observability and use New Relic alongside their other favorite applications.Starting Price: Free -
2
StrongDM
StrongDM
StrongDM is a People-First Access platform that gives technical staff a direct route to the critical infrastructure they need to be their most productive. End users enjoy fast, intuitive, and auditable access to the resources they need, and administrators leverage simplified workflows to enhance security and compliance postures. - We open up a clear, direct path that gives individualized access to the right people and keeps everyone else out. - Total visibility into everything that’s ever happened in your stack. Security and Compliance teams can easily answer who did what, where, and when. - Admins have precise control over what each user has access to—without these controls ever getting in the way of productivity - IT, InfoSec, and Administrators have precise controls. Unauthorized access is eliminated because users never see resources they don’t have permission to use. -All past, present, and future infrastructure is supported - Responsive 24/7/365 customer support.Starting Price: $70/user/month -
3
Amazon Web Services (AWS)
Amazon
Whether you're looking for compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability and reliability. Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWS has significantly more services, and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. This makes it faster, easier, and more cost effective to move your existing applications to the cloud. -
4
Amazon EC2
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers. Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 delivers the broadest choice of compute, networking (up to 400 Gbps), and storage services purpose-built to optimize price performance for ML projects. Build, test, and sign on-demand macOS workloads. Access environments in minutes, dynamically scale capacity as needed, and benefit from AWS’s pay-as-you-go pricing. Access the on-demand infrastructure and capacity you need to run HPC applications faster and cost-effectively. Amazon EC2 delivers secure, reliable, high-performance, and cost-effective compute infrastructure to meet demanding business needs. -
5
Domino Enterprise MLOps Platform
Domino Data Lab
The Domino 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. 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 allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation. -
6
Dataiku DSS
Dataiku
Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs. -
7
AWS IoT
Amazon
There are billions of devices in homes, factories, oil wells, hospitals, cars, and thousands of other places. With the proliferation of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. AWS has broad and deep IoT services, from the edge to the cloud. AWS IoT is the only cloud vendor to bring together data management and rich analytics in easy-to-use services designed for noisy IoT data. AWS IoT offers services for all layers of security, including preventive security mechanisms, like encryption and access control to device data, and service to continuously monitor and audit configurations. AWS brings AI and IoT together to make devices more intelligent. You can create models in the cloud and deploy them to devices where they run 2x faster compared to other offerings. Optimize operations by easily creating digital twins of real-world systems. Run analytics on volumes of IoT data easily—without building an analytics platform. -
8
Datasaur
Datasaur
Welcome to the best tool for managing your labeling team, improving data quality, and working 70% faster—all in one place.Starting Price: $349/month -
9
Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
10
Union Cloud
Union.ai
Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters. “Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.” — Arno, CTO at Blackshark.ai “With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.” — Krishna Yeramsetty, Principal Data Scientist at Infinome “Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that." — Pradithya Aria Pura, Principal Engineer at GojStarting Price: Free (Flyte) -
11
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 -
12
AWS Step Functions
Amazon
AWS Step Functions is a serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services into business-critical applications. Through its visual interface, you can create and run a series of checkpointed and event-driven workflows that maintain the application state. The output of one step acts as an input to the next. Each step in your application executes in order, as defined by your business logic. Orchestrating a series of individual serverless applications, managing retries, and debugging failures can be challenging. As your distributed applications become more complex, the complexity of managing them also grows. With its built-in operational controls, Step Functions manages sequencing, error handling, retry logic, and state, removing a significant operational burden from your team. AWS Step Functions lets you build visual workflows that enable fast translation of business requirements into technical requirements.Starting Price: $0.000025 -
13
JetBrains Datalore
JetBrains
Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.Starting Price: $19.90 per month -
14
Causal
Causal
Build models 10x faster, connect them directly to your data, and share them with interactive dashboards and beautiful visuals. Causal's formulas are in plain English— no cell references or obscure syntax and a single Causal formula can do the work of 10s, and even 100s of spreadsheet formulas. Causal's built-in scenarios feature lets you easily set up and compare what-if scenarios, and you can work with ranges ("5 to 10") to understand the full range of possible outcomes of your model. Startups use Causal to calculate runway, track KPIs, plan employee compensation, and build investor-ready financial models for fundraising. Generate beautiful charts and tables without spending hours on customisation and configuration. Easily switch between different time scales and summary views.Starting Price: $50 per user per month -
15
Flyte
Union.ai
The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.Starting Price: Free -
16
Neptune.ai
Neptune.ai
Log, store, query, display, organize, and compare all your model metadata in a single place. Know on which dataset, parameters, and code every model was trained on. Have all the metrics, charts, and any other ML metadata organized in a single place. Make your model training runs reproducible and comparable with almost no extra effort. Don’t waste time looking for folders and spreadsheets with models or configs. Have everything easily accessible in one place. Reduce context switching by having everything you need in a single dashboard. Find the information you need quickly in a dashboard that was built for ML model management. We optimize loggers/databases/dashboards to work for millions of experiments and models. We help your team get started with excellent examples, documentation, and a support team ready to help at any time. Don’t re-run experiments because you forgot to track parameters. Make experiments reproducible and run them once.Starting Price: $49 per month -
17
Qwak
Qwak
Qwak simplifies the productionization of machine learning models at scale. Qwak’s [ML Engineering Platform] empowers data science and ML engineering teams to enable the continuous productionization of models at scale. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate, and create competitive advantage. 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 c -
18
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
19
ZenML
ZenML
Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.Starting Price: Free -
20
Coral
Coral
Coral is a knowledge assistant for enterprises to supercharge the productivity of their most strategic teams. Engage Coral with a prompt to find answers from across your documents backed with citations. Generated responses are verifiable with citations to sources, mitigating against hallucinations. Explain large language models to a non-technical retail exec. Whether for finance, support, sales, or other teams, tailor Coral to your unique job functions. Make it more powerful by connecting data sources that augment its knowledge base. Coral connects to your ecosystem with 100+ integrations across CRMs, collaboration tools, databases, and more. Manage Coral within your own secure cloud, whether through cloud partners (AWS, GCP, OCI, etc.) or virtual private clouds. Coral data is never sent to Cohere, it stays within your own environment. Coral's responses can be grounded on your data and documents. Users will see citations to denote where responses are based from.Starting Price: $0.0000004 per token -
21
Protegrity
Protegrity
Our platform allows businesses to use data—including its application in advanced analytics, machine learning, and AI—to do great things without worrying about putting customers, employees, or intellectual property at risk. The Protegrity Data Protection Platform doesn't just secure data—it simultaneously classifies and discovers data while protecting it. You can't protect what you don't know you have. Our platform first classifies data, allowing users to categorize the type of data that can mostly be in the public domain. With those classifications established, the platform then leverages machine learning algorithms to discover that type of data. Classification and discovery finds the data that needs to be protected. Whether encrypting, tokenizing, or applying privacy methods, the platform secures the data behind the many operational systems that drive the day-to-day functions of business, as well as the analytical systems behind decision-making. -
22
Amazon Transcribe
Amazon
Amazon Transcribe makes it easy for developers to add speech to text capabilities to their applications. Audio data is virtually impossible for computers to search and analyze. Therefore, recorded speech needs to be converted to text before it can be used in applications. Historically, customers had to work with transcription providers that required them to sign expensive contracts and were hard to integrate into their technology stacks to accomplish this task. Many of these providers use outdated technology that does not adapt well to different scenarios, like low-fidelity phone audio common in contact centers, which results in poor accuracy. Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, automate subtitling, and generate metadata for media assets to create a fully searchable archive.Starting Price: $0.00013 -
23
NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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24
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
25
AWS App Mesh
Amazon Web Services
AWS App Mesh is a service mesh that provides application-level networking to facilitate communication between your services across various types of computing infrastructure. App Mesh offers comprehensive visibility and high availability for your applications. Modern applications are generally made up of multiple services. Each service can be developed using various types of compute infrastructure, such as Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. As the number of services within an application grows, it becomes difficult to pinpoint the exact location of errors, redirect traffic after errors, and safely implement code changes. Previously, this required creating monitoring and control logic directly in your code and redeploying your services every time there were changes.Starting Price: Free -
26
Superwise
Superwise
Get in minutes what used to take years to build. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy, maintain and improve ML in production. Superwise is an open platform that integrates with any ML stack and connects to your choice of communication tools. Want to take it further? Superwise is API-first and everything (and we mean everything) is accessible via our APIs. All from the comfort of the cloud of your choice. When it comes to ML monitoring you have full self-service control over everything. Configure metrics and policies through our APIs and SDK or simply select a monitoring template and set the sensitivity, conditions, and alert channels of your choice. Try Superwise out or contact us to learn more. Easily create alerts with Superwise’s ML monitoring policy templates and builder. Select from dozens of pre-build monitors ranging from data drift to equal opportunity, or customize policies to incorporate your domain expertise.Starting Price: Free -
27
DataOps.live
DataOps.live
DataOps.live, the Data Products company, delivers productivity and governance breakthroughs for data developers and teams through environment automation, pipeline orchestration, continuous testing and unified observability. We bring agile DevOps automation and a powerful unified cloud Developer Experience (DX) to modern cloud data platforms like Snowflake. DataOps.live, a global cloud-native company, is used by Global 2000 enterprises including Roche Diagnostics and OneWeb to deliver 1000s of Data Product releases per month with the speed and governance the business demands. -
28
Cameralyze
Cameralyze
Empower your product with AI. Our platform offers a vast selection of pre-built models and a user-friendly no-code interface for custom models. Integrate AI seamlessly into your application and gain a competitive edge. Sentiment analysis, also known as opinion mining, is the process of extracting subjective information from text data, such as reviews, social media posts, or customer feedback, and categorizing it as positive, negative, or neutral. This technology has gained increasing importance in recent years, as more and more companies are using it to understand their customers' opinions and needs, and to make data-driven decisions that can improve their products, services, and marketing strategies. Sentiment analysis is a powerful technology that helps companies understand customer feedback and make data-driven decisions to improve their products, services, and marketing strategies.Starting Price: $29 per month -
29
Akira AI
Akira AI
Akira AI gives best-in-class explainability, accuracy, scalability, stability, and speed in their application. Provide transparent, robust, trustworthy, and fair applications with responsible AI. Transforming the way enterprise work with end-to-end model deployment, computer vision techniques and machine learning solutions. Enable actionable insights to solve business-impacting ML model issues. Build compliant and responsible AI systems with proactive bias monitoring capabilities. Explainable ML and quality management solutions that open the AI black box to understand and optimize the correct inner workings of the model. Intelligent automation-enabled processes reduce operational hindrances and optimize workforce productivity. Build AI-quality solutions that optimize, explain, and monitor ML models. Improve performance, transparency, and robustness. Improve AI outcomes and drive model performance by increasing model velocity.Starting Price: $15 per month -
30
Deep Lake
activeloop
Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.Starting Price: $995 per month -
31
Kedro
Kedro
Kedro is the foundation for clean data science code. It borrows concepts from software engineering and applies them to machine-learning projects. A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems. Kedro standardizes how data science code is created and ensures teams collaborate to solve problems easily. Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments. A series of lightweight data connectors is used to save and load data across many different file formats and file systems.Starting Price: Free -
32
Comet LLM
Comet LLM
CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows. Log your prompts and responses, including prompt template, variables, timestamps and duration, and any metadata that you need. Visualize your prompts and responses in the UI. Log your chain execution down to the level of granularity that you need. Visualize your chain execution in the UI. Automatically tracks your prompts when using the OpenAI chat models. Track and analyze user feedback. Diff your prompts and chain execution in the UI. Comet LLM Projects have been designed to support you in performing smart analysis of your logged prompt engineering workflows. Each column header corresponds to a metadata attribute logged in the LLM project, so the exact list of the displayed default headers can vary across projects.Starting Price: Free -
33
Taipy
Taipy
From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability. Taipy enhances performance with caching control of graphical events, optimizing rendering by selectively updating graphical components only upon interaction. Effortlessly manage massive datasets with Taipy's built-in decimator for charts, intelligently reducing the number of data points to save time and memory without losing the essence of your data's shape. Struggle with sluggish performance and excessive memory usage, as every data point demands processing. Large datasets become cumbersome, complicating the user experience and data analysis. Scenarios are made easy with Taipy Studio. A powerful VS Code extension that unlocks a convenient graphical editor. Get your methods invoked at a certain time or intervals. Enjoy a variety of predefined themes or build your own.Starting Price: $360 per month -
34
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. -
35
Privacera
Privacera
At the intersection of data governance, privacy, and security, Privacera’s unified data access governance platform maximizes the value of data by providing secure data access control and governance across hybrid- and multi-cloud environments. The hybrid platform centralizes access and natively enforces policies across multiple cloud services—AWS, Azure, Google Cloud, Databricks, Snowflake, Starburst and more—to democratize trusted data enterprise-wide without compromising compliance with regulations such as GDPR, CCPA, LGPD, or HIPAA. Trusted by Fortune 500 customers across finance, insurance, retail, healthcare, media, public and the federal sector, Privacera is the industry’s leading data access governance platform that delivers unmatched scalability, elasticity, and performance. Headquartered in Fremont, California, Privacera was founded in 2016 to manage cloud data privacy and security by the creators of Apache Ranger™ and Apache Atlas™. -
36
TIBCO Data Science
TIBCO Software
Democratize, collaborate, and operationalize, machine learning across your organization. Data science is a team sport. Data scientists, citizen data scientists, data engineers, business users, and developers need flexible and extensible tools that promote collaboration, automation, and reuse of analytic workflows. But algorithms are only one piece of the advanced analytic puzzle. To deliver predictive insights, companies need to increase focus on the deployment, management, and monitoring of analytic models. Smart businesses rely on platforms that support the end-to-end analytics lifecycle while providing enterprise security and governance. TIBCO® Data Science software helps organizations innovate and solve complex problems faster to ensure predictive findings quickly turn into optimal outcomes. TIBCO Data Science allows organizations to expand data science deployments across the organization by providing flexible authoring and deployment capabilities. -
37
Securing against unknown threats through user and entity behavior analytics. Discover abnormalities and unknown threats that traditional security tools miss. Automate stitching of hundreds of anomalies into a single threat to simplify a security analyst’s life. Use deep investigative capabilities and powerful behavior baselines on any entity, anomaly or threat. Automate threat detection using machine learning so you can spend more time hunting with higher fidelity behavior-based alerts for quick review and resolution. Rapidly identify anomalous entities without human analysis. Rich set of anomaly types (65+) and threat classifications (25+) across users, accounts, devices and applications. Rapidly identify anomalous entities without human analysis. Rich set of anomaly types and threat classifications (25+) across users, accounts, devices and applications. Organizations gain maximum value to detect and resolve threats and anomalies via the power of human and machine-driven solutions.
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38
Okera
Okera
Okera, the Universal Data Authorization company, helps modern, data-driven enterprises accelerate innovation, minimize data security risks, and demonstrate regulatory compliance. The Okera Dynamic Access Platform automatically enforces universal fine-grained access control policies. This allows employees, customers, and partners to use data responsibly, while protecting them from inappropriately accessing data that is confidential, personally identifiable, or regulated. Okera’s robust audit capabilities and data usage intelligence deliver the real-time and historical information that data security, compliance, and data delivery teams need to respond quickly to incidents, optimize processes, and analyze the performance of enterprise data initiatives. Okera began development in 2016 and now dynamically authorizes access to hundreds of petabytes of sensitive data for the world’s most demanding F100 companies and regulatory agencies. The company is headquartered in San Francisco. -
39
Wallaroo.AI
Wallaroo.AI
Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale. -
40
Aporia
Aporia
Create customized monitors for your machine learning models with our magically-simple monitor builder, and get alerts for issues like concept drift, model performance degradation, bias and more. Aporia integrates seamlessly with any ML infrastructure. Whether it’s a FastAPI server on top of Kubernetes, an open-source deployment tool like MLFlow or a machine learning platform like AWS Sagemaker. Zoom into specific data segments to track model behavior. Identify unexpected bias, underperformance, drifting features and data integrity issues. When there are issues with your ML models in production, you want to have the right tools to get to the root cause as quickly as possible. Go beyond model monitoring with our investigation toolbox to take a deep dive into model performance, data segments, data stats or distribution. -
41
Fiddler
Fiddler
Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler 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, build trusted AI solutions, and increase revenue. -
42
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 -
43
Wizata
Wizata
With Digital Twin & Data Explorer, AI Solutions Builder and Automation of Production features, the Wizata Platform empowers the manufacturing industry to drive its digital transformation and facilitates the development of AI solutions from proof of concept to real-time production recommendations for a complete loop process control through AI. This open architecture platform (SaaS– Software as a Service) acts as an orchestrator of your different assets (machines, sensors, AI, cloud, edge,) and ensures you gather and explore easily your data that stays under your sole control. Control resources invested into AI experiments step by step and prioritize your projects depending on how your AI solutions solve your business pains and improve production processes, their return of investment and according to data science best practices in metallurgy that we developed since 4 years around the world. -
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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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Cranium
Cranium
The AI revolution is here. Innovation is moving at light speed, and the regulation landscape is constantly evolving. How can you make sure that your AI systems — and those of your vendors — remain secure, trustworthy, and compliant? Cranium helps cybersecurity and data science teams understand everywhere that AI is impacting their systems, data or services. Secure your organization’s AI and machine learning systems to ensure they are compliant and trustworthy, without interrupting your workflow. Protect against adversarial threats without impacting how your team trains, tests and deploys AI models. Increase AI regulatory awareness and alignment within your organization. Showcase the security and trustworthiness of your AI systems. -
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Determined AI
Determined AI
Distributed training without changing your model code, determined takes care of provisioning machines, networking, data loading, and fault tolerance. Our open source deep learning platform enables you to train models in hours and minutes, not days and weeks. Instead of arduous tasks like manual hyperparameter tuning, re-running faulty jobs, and worrying about hardware resources. Our distributed training implementation outperforms the industry standard, requires no code changes, and is fully integrated with our state-of-the-art training platform. With built-in experiment tracking and visualization, Determined records metrics automatically, makes your ML projects reproducible and allows your team to collaborate more easily. Your researchers will be able to build on the progress of their team and innovate in their domain, instead of fretting over errors and infrastructure. -
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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
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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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TruEra
TruEra
A machine learning monitoring solution that helps you easily oversee and troubleshoot high model volumes. With explainability accuracy that’s unparalleled and unique analyses that are not available anywhere else, data scientists avoid false alarms and dead ends, addressing critical problems quickly and effectively. Your machine learning models stay optimized, so that your business is optimized. TruEra’s solution is based on an explainability engine that, due to years of dedicated research and development, is significantly more accurate than current tools. TruEra’s enterprise-class AI explainability technology is without peer. The core diagnostic engine is based on six years of research at Carnegie Mellon University and dramatically outperforms competitors. The platform quickly performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly how and why a model makes predictions. -
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Vectice
Vectice
Enabling all enterprise’s AI/ML initiatives to result in consistent and positive impact. Data scientists deserve a solution that makes all their experiments reproducible, every asset discoverable and simplifies knowledge transfer. Managers deserve a dedicated data science solution. to secure knowledge, automate reporting and simplify reviews and processes. Vectice is on a mission to revolutionize the way data science teams work and collaborate. The goal is to ensure consistent and positive AI/ML impact for all organizations. Vectice is bringing the first automated knowledge solution that is both data science aware, actionable and compatible with the tools data scientists use. Vectice auto-captures all the assets that AI/ML teams create such as datasets, code, notebooks, models or runs. Then it auto-generates documentation from business requirements to production deployments.
