Alternatives to Aim

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.

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.

Gartner's Magic Quadrant includes Centralpoint as a Digital Experience Platform. It is used by more than 350 clients around the world, and it goes beyond Enterprise Content Management. It securely authenticates (AD/SAML/OpenID, oAuth), all users for self-service interaction. Centralpoint automatically aggregates information from different sources and applies rich metadata against your rules to produce true Knowledge Management. This allows you to search for and relate disparate data sets from anywhere. Centralpoint's Module Gallery is the most robust and can be installed either on-premise or in the cloud. Check out our solutions for Automating Metadata and Automating Retention Policy Management. We also offer solutions to simplify the mashup of disparate data to benefit from AI (Artificial Intelligence). Centralpoint is often used to provide easy migration tools and an intelligent alternative to Sharepoint. It can be used to secure portal solutions for public sites, intranets, members, or extranets.

Neptune.ai, a platform for machine learning operations, is designed to streamline tracking, organizing and sharing of experiments, and model-building. It provides a comprehensive platform for data scientists and machine-learning engineers to log, visualise, and compare model training run, datasets and hyperparameters in real-time. Neptune.ai integrates seamlessly with popular machine-learning libraries, allowing teams to efficiently manage research and production workflows. Neptune.ai's features, which include collaboration, versioning and reproducibility of experiments, enhance productivity and help ensure that machine-learning projects are transparent and well documented throughout their lifecycle.

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.

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.

AI engineering does not have to be a mystery. You can get full visibility using tools for tracing and evaluation, prompt management and more. HoneyHive is a platform for AI observability, evaluation and team collaboration that helps teams build reliable generative AI applications. It provides tools for evaluating and testing AI models and monitoring them, allowing engineers, product managers and domain experts to work together effectively. Measure the quality of large test suites in order to identify improvements and regressions at each iteration. Track usage, feedback and quality at a large scale to identify issues and drive continuous improvements. HoneyHive offers flexibility and scalability for diverse organizational needs. It supports integration with different model providers and frameworks. It is ideal for teams who want to ensure the performance and quality of their AI agents. It provides a unified platform that allows for evaluation, monitoring and prompt management.

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.

With confidence, collaborate on prompts, monitor and evaluate LLM apps. Agenta is an integrated platform that allows teams to build robust LLM applications quickly. Create a playground where your team can experiment together. Comparing different prompts, embeddings, and models in a systematic way before going into production is key. Share a link with the rest of your team to get human feedback. Agenta is compatible with all frameworks, including Langchain, Lama Index and others. Model providers (OpenAI, Cohere, Huggingface, self-hosted, etc.). You can see the costs, latency and chain of calls for your LLM app. You can create simple LLM applications directly from the UI. If you want to create customized applications, then you will need to use Python to write the code. Agenta is model-agnostic, and works with any model provider or framework. Our SDK is currently only available in Python.

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.

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.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

DataHub is a free and open-source metadata platform that streamlines data discovery, observability and governance across diverse data ecologies. It allows organizations to discover trustworthy data with experiences tailored to each user and eliminates breaking updates with detailed cross-platform, column-level lineage. DataHub gives you a complete view of your data, including its business, operational and technical context. The platform provides automated data quality checks, AI-driven anomaly identification and alerts teams when problems arise. It also centralizes incident tracking. DataHub's detailed ownership, documentation, and lineage information allows for quick issue resolution. It automates governance by classifying assets in real-time, reducing manual work with GenAI documentation, AI classification, and smart propagation. DataHub’s extensible architecture supports more than 70 native integrations.

Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.

Literal AI is an open-source platform that helps engineering and product teams develop production-grade Large Language Model applications. It provides a suite for observability and evaluation, as well as analytics. This allows for efficient tracking, optimization and integration of prompt version. The key features are multimodal logging encompassing audio, video, and vision, prompt management, with versioning and testing capabilities, as well as a prompt playground to test multiple LLM providers. Literal AI integrates seamlessly into various LLM frameworks and AI providers, including OpenAI, LangChain and LlamaIndex. It also provides SDKs for Python and TypeScript to instrument code. The platform supports the creation and execution of experiments against datasets to facilitate continuous improvement in LLM applications.

BigHand Metadata Management allows law firms at any stage to clean Word, Excel PowerPoint, PDF and media files. Users can automate metadata deletion, configure different levels of metadata management and protect their staff from unwittingly causing business impact and confidentiality breaches. All users can view all metadata from one panel. Global settings are set and distributed to all users. Sending documents outside your organization? Get alerts about clean documents. Users will be prompted to select the level required for metadata removal. Create different settings for each staff member, department or individual depending on the requirements. Integrate with popular document management and email programs.

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.

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.

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.

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.

Although your organization understands the value of data, and how it must be accessible to business users for maximum impact, enterprise data governance can make it difficult to trust, understand, or find that data. Data360 Govern, an enterprise data governance, metadata, and catalog management solution, gives you confidence in your data's quality, value, and trustworthiness. It automates governance tasks and stewardship tasks, helping you to answer critical questions about your data's origin, use, meaning, ownership and quality. Data360 Govern allows you to make faster decisions about data usage and management, foster collaboration across your organization, and give users the ability to get the answers they require - whenever they need them. Transparency into your company's data landscape allows you to track the most critical data that aligns with your business goals.

IBM InfoSphere®, Information Governance Catalog is a web-based application that allows you to search, understand, and analyze information. You can create, manage, share, and even enact policies and guidelines, as well as track data lineage. Combine with IBM Watson®, Knowledge Catalog to make use of existing curated data sets, and extend your investment in the Information Governance Catalog on-premises to the cloud. A knowledge catalog lets you put your collected metadata in the hands of knowledge workers, so that data science and analytics communities have easy access to the best assets for their purposes while still adhering with enterprise governance requirements. It provides a common language and vocabulary that allows for a deeper understanding of all data assets, whether they are structured, semi-structured, or unstructured. Documents governance policies and enacts rules that help you determine how information should be stored, transformed, and moved.

PromptHub allows you to test, collaborate, version and deploy prompts from a single location. Use variables to simplify prompt creation and stop copying and pasting. Say goodbye to spreadsheets and compare outputs easily when tweaking prompts. Batch testing allows you to test your datasets, and prompts, at scale. Test different models, parameters, and variables to ensure consistency. Test different models, system messaging, or chat templates. Commit prompts, branch out, and collaborate seamlessly. We detect prompts changes so you can concentrate on outputs. Review changes in a team setting, approve new versions and keep everyone on track. Monitor requests, costs and latencies easily. PromptHub allows you to easily test, collaborate, and version prompts. With our GitHub-style collaboration and versioning, it's easy to iterate and store your prompts in one place.

CometLLM allows you to visualize and log your LLM chains and prompts. CometLLM can be used to identify effective prompting strategies, streamline troubleshooting and ensure reproducible workflows. Log your prompts, responses, variables, timestamps, duration, and metadata. Visualize your responses and prompts in the UI. Log your chain execution to the level you require. Visualize your chain in the UI. OpenAI chat models automatically track your prompts. Track and analyze feedback from users. Compare your prompts in the UI. Comet LLM Projects are designed to help you perform smart analysis of logged prompt engineering workflows. Each column header corresponds with a metadata attribute that was logged in the LLM Project, so the exact list can vary between projects.

All in one place, simplify prompt edits, SDK integration, and version control. No more waiting for deployments or scattered tools. Explore features designed to streamline your workflow and elevate prompting engineering. Manage your projects and prompts in a structured manner with tools that keep everything organized. Adapt your prompts dynamically to the context of your app, improving user experience through tailored interactions. Our user-friendly SDK is designed to minimize disruption and maximize efficiency. Utilize detailed analytics to better understand prompt performance, user interaction, and areas for improvements, based on concrete data. Invite team members to work together in a shared workspace where everyone can review, refine, and contribute prompts. Control access and permissions to ensure that your team members can work efficiently.

The xAI PromptIDE integrates development for prompt engineering, interpretability research and other related tasks. It accelerates prompting engineering through an SDK which allows complex prompting techniques to be implemented and rich analytics to visualize the network's results. We use it heavily for our Grok continuous development. We developed PromptIDE in order to provide engineers and researchers with transparent access to Grok-1 - the model that powers Grok - to the community. The IDE was designed to empower users, and allow them to explore the capabilities of large language models at their own pace. The IDE's core is a Python editor that, when combined with a new SDK, allows for complex prompting techniques. Users can see useful analytics while executing prompts within the IDE. These include the precise tokenization of the prompt, sampling probabilities and alternative tokens. The IDE offers a number of features that enhance the quality of life. It automatically saves prompts.

Prompt hunt's advanced AI model, called Chroma, along with a library of styles and templates that have been verified, makes creating art simple and accessible. Prompt Hunt gives you the tools to unleash your creativity and create stunning art and assets in minutes, whether you're an experienced artist or a novice. We know how important privacy is, so we provide this feature to our users. Templates in Prompt hunt are pre-designed structures or frameworks that simplify the process of creating artwork without the need for complex prompt engineers. The template will handle the work behind the scenes and generate the desired output by simply entering the subject and clicking "create". Anyone can create their own templates with Prompt Hunt. You can choose to share or keep your designs private.

The first platform designed for prompt engineers. Log OpenAI requests, track usage history, visual manage prompt templates, and track performance. Manage Never forget one good prompt. GPT in Prod, done right. Trusted by more than 1,000 engineers to monitor API usage and version prompts. Your prompts can be used in production. Click "log in" to create an account on PromptLayer. Once you have logged in, click on the button to create an API Key and save it in a secure place. After you have made your first few requests, the API key should be visible in the PromptLayer dashboard. LangChain can be used with PromptLayer. LangChain is a popular Python library that assists in the development and maintenance of LLM applications. It offers many useful features such as memory, agents, chains, and agents. Our Python wrapper library, which can be installed with pip, is the best way to access PromptLayer at this time.

Iterate quickly, and ship confidently. Ship confidently by evaluating prompts using a suite evals such as context recall, llm rubric (LLM is a judge), latencies, and more. We can handle complex implementations and intelligent caching to save you money and time. Iterate quickly on your prompts using a collaborative playground. This includes all major providers, variables, versioning and more. You can easily build datasets using real data by using Logs. You can also upload your own CSV or collaborate to build and edit them within your Adaline workspace. Our APIs allow you to track usage, latency and other metrics in order to monitor the performance of your LLMs. Our APIs allow you to continuously evaluate your completions on production, see the way your users use your prompts, create datasets, and send logs. The platform allows you to iterate and monitor LLMs. You can easily rollback if you see a decline in your production and see how the team iterated on the prompt.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Your metadata can be discovered, documented and managed. Dataedo has multiple automated metadata scanners. These scanners connect to different database technologies, extract data structures, and then load them into the metadata repository. In just a few clicks you can create a catalog of all your data and then describe each element. With business-friendly aliases, decrypt column and table names and provide meaning and purpose to data assets with descriptions and custom fields. To find out what data is stored in your data asset, you can use sample data. Make sure you have a better understanding of the data before you use it. Data profiling can help ensure high quality data. Data profiling allows everyone to have access to data knowledge. A lightweight, on-premises data catalogue can help you build data literacy, democratize data, and empower your employees to make better data use. A data catalog can help you increase data literacy.

Automated voice testing, monitoring and more. Test your AI voice agent with 1000s of simulated users within minutes. It's hard to get AI voice agents right. LLM outputs can be affected by a small change in the prompts, function calls or model providers. We are the only platform that can support you from development through to production. Hamming allows you to store, manage, update and sync your prompts with voice infra provider. This is 1000x faster than testing voice agents manually. Use our prompt playground for testing LLM outputs against a dataset of inputs. Our LLM judges quality of generated outputs. Save 80% on manual prompt engineering. Monitor your app in more than one way. We actively track, score and flag cases where you need to pay attention. Convert calls and traces to test cases, and add them to the golden dataset.

DataGalaxy’s all-in one data catalog provides out-of-the box actionability, with fully-customizable features, visualization tools, AI integration, to give business teams a way to document, link and track their metadata assets. The Data Catalog 360deg platform's user-centric approach is dedicated to metadata management, knowledge sharing, and mapping. This helps your organization manage data in the way that you want. A data catalog allows employees from different teams to collaborate by using homogeneous, centralized data sets. Our data catalog provides clarity for data definitions, synonyms and essential business attributes. It also includes a semantic layer to help all users understand and leverage data. If you are looking for answers on a specific metadata, the data catalog will identify 360deg data experts and owners. This will empower your team by facilitating collaboration.

DataTools for Salesforce is a metrics and meta-data management tool that brings together Salesforce and data cloud professionals. DataTools for Salesforce provides context data and metadata to help you implement a modern customer data cloud. Our Salesforce dictionary utility was designed specifically for robust reporting and insightful analyses. You can categorize and pinpoint key fields while highlighting essential metrics in order to maximize the utility your meta-data. Color schemes make it easy to see the connections between Salesforce objects. Structure and interact with your Salesforce entity relation diagram. Create your Salesforce entity relationship diagram by selecting the relevant objects in your dictionary. Our Salesforce data migration tool was designed to streamline project and process management.

To have complete control over your data, harness the power of data discovery, data lineage and a data catalogue. It can quickly navigate through complex data landscapes. Access the most comprehensive automated data lineage and discovery system. This gives you unprecedented visibility and trust in the most complex data environments. Octopai extracts metadata from all data environments. Octopai can instantly analyze metadata in a fast, secure, and easy process. Octopai gives you access to data lineage, data discovery, and a data catalogue, all from one central platform. In seconds, trace any data from end to end through your entire data landscape. Find the data you need automatically from any place in your data landscape. A self-creating, self updating data catalog will help you create consistency across your company.

Mirascope is a powerful, flexible and user-friendly library that simplifies the process of working with LLMs through a unified interface. It works across various supported providers including OpenAI, Anthropic Mistral Gemini Groq Cohere LiteLLM Azure AI Vertex AI and Bedrock. Mirascope is a flexible, powerful and user-friendly LLM library that simplifies working with LLMs. It has a unified interface and works across multiple supported providers including OpenAI, Anthropic Mistral, Gemini Groq Cohere LiteLLM Azure AI Vertex AI and Bedrock. Mirascope is a powerful and flexible library that allows you to create robust, powerful applications. Mirascope's response models allow you to structure the output of LLMs and validate it. This feature is especially useful when you want to make sure that the LLM response follows a certain format or contains specific fields.

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.

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.

Talend Data Catalog provides your organization with a single point of control for all your data. Data Catalog provides robust tools for search, discovery, and connectors that allow you to extract metadata from almost any data source. It makes it easy to manage your data pipelines, protect your data, and accelerate your ETL process. Data Catalog automatically crawls, profiles and links all your metadata. Data Catalog automatically documents up to 80% of the data associated with it. Smart relationships and machine learning keep the data current and up-to-date, ensuring that the user has the most recent data. Data governance can be made a team sport by providing a single point of control that allows you to collaborate to improve data accessibility and accuracy. With intelligent data lineage tracking and compliance tracking, you can support data privacy and regulatory compliance.

You can scan and index metadata, discover and profile information, and provide detailed lineage across tens to millions of data sets. To maximize data reuse and data value, classify and organize data assets in any environment. Automate scanning across multiple cloud platforms, BI tools and ETL tools; and data types. AI-powered domain discovery, data similarities, business term associations, recommendations, and recommendation making are possible. You can track data movement from high-level system views through to fine column-level lineage and get detailed impact analysis. The Data Asset Analytics dashboard allows you to see asset usage, enrichment, collaboration. You can view data quality rules, scorecards and metric groups in context. You can tap into shared data knowledge through certifications, ratings, reviews, a Q&A forum, and change notifications. Informatica stands out from the rest with its extensive range of enterprise-grade data management products.

SOLIXCloud CDP delivers cloud data management as-a-service for modern data-driven enterprises. SOLIXCloud CDP is a cloud-native opensource platform that helps companies manage all their structured, semi-structured, and unstructured data. This allows for advanced analytics, compliance, infrastructure optimization, and data security. SOLIXCloud CDP features include Solix Connect for data input, Solix Data Governance and Solix Metadata Management. This cloud-based data management framework allows you to create and run data-driven apps such as SQL data warehouse, machine intelligence, and artifitial insight. It also meets the ever-growing data management requirements such as complex data retention, consumer data privacy, and data regulation.

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.

This is the essential tool for metadata management. In just 5 minutes, automatically populate your entire catalogue! Data Discovery. Data Discovery. Find the data you need from any part of your data ecosystem, starting with the database and ending with the specific values for each field. Automated documentation of your data from existing data storage. First-class support for unstructured and tabular data. Automated data governance actions. Data Lineage. Data Lineage. Explore your data lineage to understand where your data is coming from and where it is headed. View the impact analysis of changes. See all up- and downstream impacts. Visualize connections and relationships. API AccessNew. Tree Schema API allows you to manage your data lineage in code and keep your catalog current. Integrate Data Lineage in CICD pipelines Capture values & description within your code Analyze the impact of breaking changes. Data Dictionary. Know the key terms and lingo which drive your business. Define the context and scope of keywords

Inquiring is the most popular platform for strategic enterprise planning. It allows large organizations to do more with their data and make it even better. The IQ platform provides total integration, unparalleled insight, and maximum impact for modern enterprises. It includes a powerful suite of strategic planning tools as well as industry-leading metadata management. InQuisient's patented platform offers unparalleled data management capabilities, and it delivers it all through your existing knowledge workers. You can either create a completely customized database solution from scratch or use a powerful suite out-of-the box tools. Or, you can do both. An intuitive web browser interface allows you to quickly and securely deliver the power of your data into your organization. You can plan, change, transform, and manage smarter and more efficiently with InQuisient's web browser interface. InQuisient's powerful PPM tools and platform will show you why IQ is the trusted solution for enterprises.

SAS Data Management allows you to access your data regardless of where it is stored. You can create data management rules once and then reuse them. This gives you a consistent, repeatable way to improve and integrate data without extra cost. It's easy to get involved in tasks that are not your normal duties as an IT expert. SAS Data Management allows your business users to update data and tweak processes, as well as analyze results. This frees you up to work on other projects. A built-in business glossary, along with SAS and third-party metadata management, and lineage visualization capabilities, keeps everyone on the same page. SAS Data Management technology is fully integrated. This means that you don't have to use a solution that has been thrown together. All components of SAS Data Management technology, from data quality to federation technology, are part and parcel of the same architecture.

The new world of data means that you can spend more time looking at data than you are analyzing it. Azure Data Catalog is an enterprise-wide data catalog that makes data asset discovery easy. It is a fully managed service that allows you, from analyst to data scientist, to use it to register, enrich, discover and understand data sources. You can work with data using the tool you prefer. Data Catalog allows you to find the data you need, and you can use it in any tool you choose. Data Catalog ensures that your data is always available. It makes it easy to find and use the data you need, and provides an intuitive user interface. Increase adoption and continuous value generation across your data ecosystem. Data Catalog provides tips, tricks, as well as unwritten rules that can be used to create an environment where everyone can benefit. Data Catalog makes it possible for everyone to contribute. Democratize data asset discovery.

MetaCenter allows business and technology teams to classify and catalog an organization's information assets. Self-service questions can be asked about data assets, how data flows through the company, and how it should use. This allows organizations to reduce costs, improve agility, and reduce operational risks. Cross-referencing models can be automated by a search-based semantic layer. Faceted views of data assets can be published to specific roles. Comparable solutions offer lower costs of ownership and better ROI. Simple GUI driven customization enables rapid application customization. No programming or professional services required

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

The modern data workspace. All your data assets, from data tables to reports, will be instantly discoverable. The combination of powerful search algorithms and easy browsing makes it easy to find the right asset. Atlan automatically generates data quality profiles that make it easy to detect bad data. We have you covered, from automatic variable type detection and frequency distribution to missing values or outlier detection. Atlan takes the hassle out of managing and governing your data ecosystem. Atlan's bots analyze SQL query history to automatically construct data lineage. They also auto-detect PII information. This allows you to create dynamic access policies and best-in-class governance. Our Excel-like query builder allows anyone to query multiple data lakes, warehouses, and DBs. Native integrations with tools such as Tableau and Jupyter make data collaboration possible.