Alternatives to neptune.ai

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.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Visdom is an interactive visualization tool that helps researchers and developers keep track of their remote servers-based scientific experiments. Visdom visualizations can be viewed and shared in browsers. Visdom is an interactive visualization tool to support scientific experimentation. Visualizations can be broadcast to collaborators and yourself. Visdom's UI allows researchers and developers alike to organize the visualization space, allowing them to debug code and inspect results from multiple projects. Windows, environments, filters, and views are also available to organize and view important experimental data. Create and customize visualizations to suit your project.

TensorBoard, TensorFlow’s comprehensive visualization toolkit, is designed to facilitate machine-learning experimentation. It allows users to track and visual metrics such as accuracy and loss, visualize the model graph, view histograms for weights, biases or other tensors over time, display embeddings in a lower-dimensional area, and display images and text. TensorBoard also offers profiling capabilities for optimizing TensorFlow programmes. These features provide a suite to help understand, debug and optimize TensorFlow, improving the machine learning workflow. To improve something in machine learning, you need to be able measure it. TensorBoard provides the measurements and visualisations required during the machine-learning workflow. It allows tracking experiment metrics, visualizing model graphs, and projecting embedded embeddings into a lower-dimensional space.

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

Manage 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.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

A platform for machine learning and deep learning applications that is reproducible and scaleable. Learn more about the products and features that make up today's most innovative platform to manage data science workflows. Polyaxon offers an interactive workspace that includes notebooks, tensorboards and visualizations. You can collaborate with your team and share and compare results. Reproducible results are possible with the built-in version control system for code and experiments. Polyaxon can be deployed on-premises, in the cloud, or in hybrid environments. This includes single laptops, container management platforms, and Kubernetes. You can spin up or down, add nodes, increase storage, and add more GPUs.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Aim logs your AI metadata (experiments and prompts) enables a UI for comparison & observation, and SDK for programmatic querying. Aim is a self-hosted, open-source AI Metadata tracking tool that can handle 100,000s tracked metadata sequences. The two most famous AI metadata applications include experiment tracking and prompting engineering. Aim offers a beautiful and performant UI for exploring, comparing and exploring training runs and prompt sessions.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

Amazon SageMaker offers all the tools and libraries needed to build ML models. It allows you to iteratively test different algorithms and evaluate their accuracy to determine the best one for you. Amazon SageMaker allows you to choose from over 15 algorithms that have been optimized for SageMaker. You can also access over 150 pre-built models available from popular model zoos with just a few clicks. SageMaker offers a variety model-building tools, including RStudio and Amazon SageMaker Studio Notebooks. These allow you to run ML models on a small scale and view reports on their performance. This allows you to create high-quality working prototypes. Amazon SageMaker Studio Notebooks make it easier to build ML models and collaborate with your team. Amazon SageMaker Studio notebooks allow you to start working in seconds with Jupyter notebooks. Amazon SageMaker allows for one-click sharing of notebooks.

Data Version Control (DVC), an open-source version control system, is tailored for data science and ML projects. It provides a Git-like interface for organizing data, models, experiments, and allowing users to manage and version audio, video, text, and image files in storage. Users can also structure their machine learning modelling process into a reproducible work flow. DVC integrates seamlessly into existing software engineering tools. Teams can define any aspect of machine learning projects in metafiles that are readable by humans. This approach reduces the gap between software engineering and data science by allowing the use of established engineering toolsets and best practices. DVC leverages Git to enable versioning and sharing for entire machine learning projects. This includes source code, configurations and parameters, metrics and data assets.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

Guild AI is a free, open-source toolkit for experiment tracking. It allows users to build faster and better models by bringing systematic control to machine-learning workflows. It captures all details of training runs and treats them as unique experiments. This allows for comprehensive tracking and analysis. Users can compare and analyse runs to improve their understanding and incrementally enhance models. Guild AI simplifies hyperparameter optimization by applying state-of the-art algorithms via simple commands, eliminating complex trial setups. It also supports pipeline automation, accelerating model creation, reducing errors and providing measurable outcomes. The toolkit runs on all major operating system platforms and integrates seamlessly with existing software engineering applications. Guild AI supports a variety of remote storage types including Amazon S3, Google Cloud Storage and Azure Blob Storage.

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

What if your data had a recommendation engine? Automated data inventory was created. A searchable catalog showed user behavior. Smart recommendations were made inline by the system as you typed queries. Alation, the first enterprise-wide collaborative data catalog, makes all this possible. It's a powerful tool that dramatically increases the productivity of analysts and the accuracy of analytics. It also empowers business decision-making for everyone. Alation provides proactive recommendations to data users through applications. Google inspired us to create a simple interface that connects the language of your business with the technical schema of your data. No more is it difficult to find the data you need due to complicated semantic translations. Are you unfamiliar with the data environment and unsure which data to use in your query. Alation allows you to build your query and provides inline recommendations that indicate whether data is trustworthy.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

Data Lake Governance Center (DGC) is a one-stop platform for managing data design, development and integration. It simplifies big data operations and builds intelligent knowledge libraries. A simple visual interface allows you to build an enterprise-class platform for data lake governance. Streamline your data lifecycle, use metrics and analytics, and ensure good corporate governance. Get real-time alerts and help to define and monitor data standards. To create data lakes faster, you can easily set up data models, data integrations, and cleaning rules to facilitate the discovery of reliable data sources. Maximize data's business value. DGC can be used to create end-to-end data operations solutions for smart government, smart taxation and smart campus. Gain new insights into sensitive data across your entire organization. DGC allows companies to define business categories, classifications, terms.

JFrog ML (formerly Qwak) is a comprehensive MLOps platform that provides end-to-end management for building, training, and deploying AI models. The platform supports large-scale AI applications, including LLMs, and offers capabilities like automatic model retraining, real-time performance monitoring, and scalable deployment options. It also provides a centralized feature store for managing the entire feature lifecycle, as well as tools for ingesting, processing, and transforming data from multiple sources. JFrog ML is built to enable fast experimentation, collaboration, and deployment across various AI and ML use cases, making it an ideal platform for organizations looking to streamline their AI workflows.

Our intelligent data and analytics platform will help you scale faster by overcoming application silos, cost overruns, and skill obsolescence. A platform that is intelligently designed to perform advanced analytics and data management. Scales across all technology landscapes. Our architecture understands the data flow and transformations throughout its entire lifecycle. Developing future-proof enterprise AI apps. A highly extensible and modular platform that allows the handling of multiple components. Scalable architecture with multi-tenant design. Advanced data visualization makes it easy to analyze complex data structures. This results in enhanced enterprise AI apps in a low-code, intuitive platform. Our hybrid multi-cloud platform was built using community open source software, making it highly adaptable, secure, and low-cost.

Anzo is a modern data integration and discovery platform that allows anyone to find, connect, and blend enterprise data into analytics-ready datasets. Anzo's unique use semantics and graph models makes it possible for anyone in your company - from data scientists to novice business users to drive data discovery and integration and create their own analytics-ready data sets. Anzo's graph models give business users a visual map of enterprise data that is easy for them to understand and navigate, regardless of how complex, siloed, or large their data may be. Semantics adds business content to data. It allows users to harmonize data using shared definitions and create blended, business-ready data upon demand.

Coactive supercharges data-driven businesses. We help analysts make video and image data useful by bringing structure to unstructured information. We can make machine-learning your new superpower by providing unprecedented insights, ease-of-use, and blistering speed. You don't have to waste time flipping through pictures or scrubbing videos. You can refine your taxonomy and search your content library by using a phrase or word. Coactive can help you keep up with the constant evolution of your data. Use our Python SDKs and APIs to monitor and understand your data as it comes in. Coactive prioritizes integrity along with sales in a manner that will ultimately benefit the company and its customers. Coactive AI, a machine learning platform of industry-leading quality, allows businesses of all sizes analyze their unstructured images in minutes. Our platform is lightning fast, with a clean, intuitive and user-friendly interface.

The Alex Platform is your company's single source for data and business truth. Alex is the foundation of our customers' data-driven success. Alex is designed to reduce complexity and create value from the first day of implementation. Alex Augmented Data Catalog uses the best machine learning in the industry to quickly provide an enterprise-wide, unified data platform. Alex Data Lineage can help you understand and map your data flows, no matter how complex or complicated your technical landscape. Global teams require global coordination. The Alex Intelligent Business Glossary's beautiful interface and rich functionality are ideal for global collaboration. Unifying all definitions, policies and metrics to reduce complexity in multi-cloud environments and global enterprises will help you to manage the complexity. Global data governance programs can be powered.

Intelligent cataloging enables you to activate business-ready data for AI/analytics. It is supported by active metadata management and policy management. IBM Watson®, Knowledge Catalog is a data catalogue tool that powers intelligent, self service discovery of data, models, and more. The cloud-based enterprise metadata repository activates data for AI, machine learning (ML), and deep learning. Access, categorize, categorize, and share knowledge assets, data, and their relationships wherever they are located. To provide the right context and drive value across requirements such as regulatory compliance and data monetization, organize, define, and manage enterprise data. Active policy management and dynamic masking allow clients to trust you. This will help protect data and manage compliance and audit readiness. With intuitive dashboards and flows, you can consume and transform data at the speed and convenience of business. These flows can be shared with colleagues or analytics tools.

Smartlogic's Semantic AI platform Semaphore uncovers qualified contextual data using an integrated platform that identifies enterprise information. It has the capabilities to create and manage active metadata, semantic metadata, and information extraction. Semaphore is a platform for cognitive applications, such as intelligent contextual search, enriched processing automation, relevant recommendation engines and customer experience. It also supports regulatory compliance, contract management, information security, and contract lifecycle management. Boost customer experience, operational efficiency, risk management, and marketing effectiveness. To streamline support and provide the right information at the right moment, connect your organization's language with your customer's. Reduce costs, close the compliance gap and enforce best practices in governance.

DQLabs has over a decade of data-related experience for Fortune 100 clients. This includes data integration, data governance and data visualization. The platform comes with all the features necessary to enable autonomous execution without any configuration or manual intervention. This AI-powered tool can automate every aspect of scaling, governance, and automation. It can also be integrated and compatible with other tools within the data ecosystem. Machine Learning and AI make it possible to make decisions in all aspects data management. No more ETL, workflows and rules. The platform automatically learns and reconfigures rules as the business strategy changes and requires new data patterns and trends.

Kepler's Automated Data Science workflows make it easy to eliminate the need for programming and machine learning. You can quickly join and get data-driven insights that are unique to your company and your data. Our SaaS-based model allows you to receive continuous updates and additional Workflows from our AI and ML teams. With a platform that grows with you business, scale AI and accelerate time to value using the skills and team already within your company. Advanced AI and machine learning capabilities can solve complex business problems without the need to have any technical ML knowledge. You can leverage state-of the-art, end to end automation, a large library of AI algorithms, as well as the ability to quickly deploy machine-learning models. Organizations use Kepler to automate and augment critical business processes in order to increase productivity and agility.

Jump to our product page for more information about our data integration software. This includes solutions for Active Directory and SharePoint. Our data integration solutions offer developers the opportunity to use the flexibility and power offered by the SSIS ETL engine to connect almost any application or data source. Data integration is possible without writing any code. This means that your development can be completed in minutes. Our integration solutions are the most flexible on the market. Our software has intuitive user interfaces that make it easy and flexible to use. Our solution is easy to use and offers the best return on your investment. Our software has many features that will help you achieve the highest performance without consuming too much of your budget.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

The 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.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

AI teams are faced with challenges that require new technologies. These technologies are built by us. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. AI and software development go hand in hand. Built with data scientists, ML experts, and data engineers at heart. Don't reinvent your wheel! Production is fast and cost-effective. All your data is stored by you. Your machines are used to train your models. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. New technologies are required for AI teams. These technologies are built by us. Studio is an extension to BitBucket, GitLab, and GitHub. Register for the online SaaS version, or contact us to start an on-premise installation

Dataiku provides a powerful AI platform that helps organizations harness the potential of data science and machine learning by enabling teams to collaborate on AI-driven projects. With a flexible user interface that supports both no-code and code-based workflows, Dataiku allows users to streamline data preparation, build models using AutoML, and deploy solutions across diverse environments. The platform supports advanced capabilities like generative AI and AI governance, making it suitable for enterprises seeking scalable and secure AI solutions across various business functions.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.

This machine learning monitoring tool allows you to easily monitor and troubleshoot large model volumes. Data scientists can avoid false alarms and dead ends by using an unrivaled explainability accuracy and unique analyses that aren't available anywhere else. This allows them to quickly and effectively address critical problems. So that your business runs at its best, machine learning models are optimized. TruEra's explainability engine is the result of years of dedicated research and development. It is significantly more accurate that current tools. TruEra's enterprise-class AI explainability tech is unrivalled. The core diagnostic engine is built on six years of research by Carnegie Mellon University. It outperforms all competitors. The platform performs sophisticated sensitivity analyses quickly, allowing data scientists, business users, risk and compliance teams to understand how and why a model makes predictions.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

Scikit-learn offers simple and efficient tools to analyze predictive data. Scikit-learn, an open source machine learning toolkit for Python, is designed to provide efficient and simple tools for data modeling and analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, built on popular scientific libraries such as NumPy SciPy and Matplotlib. It offers a range of supervised learning algorithms and unsupervised learning methods, making it a valuable toolkit for researchers, data scientists and machine learning engineers. The library is organized in a consistent, flexible framework where different components can be combined to meet specific needs. This modularity allows users to easily build complex pipelines, automate tedious tasks, and integrate Scikit-learn in larger machine-learning workflows. The library's focus on interoperability also ensures that it integrates seamlessly with other Python libraries to facilitate smooth data processing.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

Artificio, an innovative automation tool created by Artificio Products Inc., is designed to revolutionize the data processing process and eliminate manual data input. This cutting-edge tool uses state-of-the art AI and machine learning models for extracting, segregating, validating, and integrating unstructured data from different sources including texts, images, and PDFs. Artificio enables businesses to unlock digital intelligence's full potential by converting unstructured data into structured data.

Machine learning can be applied to distributed data to provide faster insights and better results without the long lead times, high concentration risk, or privacy concerns associated with centralizing data. Access to diverse, high-quality data sources is often a limitation of machine learning algorithms' effectiveness. You can gain more insight by unlocking more data and making it transparent about the impact of each dataset model. It takes time to get approvals, centralize data, and build out infrastructure. You can train models faster by using data right where it is while parallelizing and federating the training process. Devron allows you to access data in situ without the need to mask or anonymize. This greatly reduces the overhead of data extraction, transformation, loading, and storage.

Simplified quality assurance for machine learning. Snitch eliminates all noise so you can find the most relevant information to improve your models. With powerful dashboards and analysis, you can track your model's performance beyond accuracy. Identify potential problems in your data pipeline or distribution shifts and fix them before they impact your predictions. Once you've deployed, stay in production and have visibility to your models and data throughout the entire cycle. You can keep your data safe, whether it's cloud, on-prem or private cloud. Use the tools you love to integrate Snitch into your MLops process! We make it easy to get up and running quickly. Sometimes accuracy can be misleading. Before you deploy your models, make sure to assess their robustness and importance. Get actionable insights that will help you improve your models. Compare your models against historical metrics.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.