Alternatives to SAIBRE

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.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

It is a frustratingly slow process that requires development resources and know-how. Most models will never see the light of day. In minutes, you can ship full-stack applications. You can deploy models immediately, automatically generate API endpoints and quickly create UI using drag-and-drop components. To put models into production, you don't have to be a DevOps Engineer. Baseten allows you to instantly manage, monitor, and serve models using just a few lines Python. You can build business logic around your model, and sync data sources without any infrastructure headaches. Start with sensible defaults and scale infinitely with fine-grained controls as needed. You can read and write to your existing data sources or our built-in Postgres databases. Use headings, callouts and dividers to create engaging interfaces for business users.

You can choose from a wide range of assets to jumpstart your AI project. The AI hub offers a wealth of artifacts to help you get started with your AI project. These include datasets, notebooks and industry AI starter kits. Access the best resources created by your company or from external sources. Prepare and manage your data to support model training. With a simple drag-and-drop interface, collect, organize, tag or select features and prepare datasets for model training. To tag large datasets, collaborate with multiple team members. To ensure quality data, you should implement a quality control system. Use the model wizards to create models in a few clicks. No data science knowledge is required. The system automatically selects the most suitable models for the problem and optimizes their training parameters. Advanced users can adjust the hyper-parameters and select models for optimal performance. One-click deployment to produce inference environments.

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.

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.

It is an artificial intelligence solution that analyzes complex data and empowers decision making. This can transform industrial productivity and human life. It automates the data science process, which can reduce the workload on already overburdened teams. It breaks down data silos by intuitively creating data pipelines that feed live data into deployed model and then dynamically creating model execution pipelines to make real-time predictions based on incoming data. It monitors the health and maintenance of deployed models using the confidence of predicted results.

No matter what industry you work in the GroveStreams Data Analytics Platform provides you with the tools and building blocks to create solutions that meet your customers' needs. For more examples, visit our Developers page. For multiple locations, model time-of-use block rates. For apartment-level energy billing, monitor energy usage over thousands of meters/submeters. When energy usage exceeds predetermined levels, alert and take steps to reduce it. To check for leakages or excessive flow, monitor water flow in apartments and other difficult to reach locations. Monitor propane tank levels in rural areas and route shipments accordingly. The Grove Streams Data Analytics Platform, a cutting-edge cloud-based platform that provides decision-making capabilities to many users and devices from multiple sources, is the Grove Streams Data Analytics Platform.

Public datasets can help you improve the accuracy of your machine-learning models. Use curated datasets to save time on data preparation and discovery. These datasets are ready for use in machine-learning workflows and can be accessed from Azure services. Consider real-world factors which can have an impact on business outcomes. By incorporating features of curated datasets in your machine learning model, you can improve the accuracy and reduce the time required for data preparation. Share datasets with the growing community of data analysts and developers. Azure Open Datasets can be used to deliver insights at hyperscale with Azure's machine-learning and data analytics solutions. Open Datasets are free to use. Open Datasets are free to use, but you'll only be charged for the Azure services you consume, such as virtual machines, storage, networking resources and machine learning. Open data that has been curated and made available on Azure.

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.

With extremely high accuracy standards, review your documents and extract key clauses and obligations. GrayBox is a customizable solution that creates deep learning models in fractions of the time it takes to use traditional data science methods. Cognitiv+ allows you to quickly and easily integrate your system. Cognitiv+ machine-learning technology automatically analyzes and extracts text and creates new services. GrayBox automates Natural Language Processing & Deep Learning infrastructures and workflows. It accelerates the deployment and deployment of production-ready solutions. You can speed up contract review by as much as 80%. This allows you to act quickly and with a time-savings that is contractually compliant. This tool allows you to monitor your contracts without having to read them every day.

Simplismart’s fastest inference engine allows you to fine-tune and deploy AI model with ease. Integrate with AWS/Azure/GCP, and many other cloud providers, for simple, scalable and cost-effective deployment. Import open-source models from popular online repositories, or deploy your custom model. Simplismart can host your model or you can use your own cloud resources. Simplismart allows you to go beyond AI model deployment. You can train, deploy and observe any ML models and achieve increased inference speed at lower costs. Import any dataset to fine-tune custom or open-source models quickly. Run multiple training experiments efficiently in parallel to speed up your workflow. Deploy any model to our endpoints, or your own VPC/premises and enjoy greater performance at lower cost. Now, streamlined and intuitive deployments are a reality. Monitor GPU utilization, and all of your node clusters on one dashboard. On the move, detect any resource constraints or model inefficiencies.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

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.

From development to production, and from traditional data engineering to AI. Secure your development environments, pipelines and tools, as well as open source components, which make up the data and AI supply chains. Discover, prevent and fix AI compliance and security risks continuously before they reach production. Monitor your AI applications during runtime to detect and block adversarial AI threats and enforce app-specific safeguards. Noma integrates seamlessly across your data and AI supply chains and AI applications. It maps all your data pipelines and notebooks, MLOps Tools, open-source AI components and first- and third party models and datasets. This automatically generates a comprehensive AI/ML BOM. Noma continuously identifies security risks, such as misconfigurations and AI vulnerabilities, throughout your data and AI chain. It then provides actionable remedies to mitigate these risks.

IBM Safer Payments allows you to create custom, user friendly decision models, so you can adapt faster to emerging threats and detect fraud more quickly, without relying on vendors or data scientists. IBM Safer Payments accelerates the modeling optimization process by providing the analytics tools and simulations needed to continuously monitor and adapt to evolving and modified fraud patterns. After deploying our solution, clients report high detection rates with ultra-low false positive rates. Machine-learning models can be built, tested, validated, and deployed in days, not months, without the need for vendors. Monitor thousands of payments every second. Enterprise-grade solution with high throughput and 99.999% availability. Open platform allows you to import detection models, model parts, and IP, while using a rich user interface to create new models. You can use any data science technique, including machine learning and artificial intelligence.

ZinkML is an open-source data science platform that does not require any coding. It was designed to help organizations leverage data more effectively. Its visual and intuitive interface eliminates the need for extensive programming expertise, making data sciences accessible to a wider range of users. ZinkML streamlines data science from data ingestion, model building, deployment and monitoring. Users can drag and drop components to create complex pipelines, explore the data visually, or build predictive models, all without writing a line of code. The platform offers automated model selection, feature engineering and hyperparameter optimization, which accelerates the model development process. ZinkML also offers robust collaboration features that allow teams to work seamlessly together on data science projects. By democratizing the data science, we empower businesses to get maximum value out of their data and make better decisions.

Snowflake®, a cloud data platform and enterprise-grade architecture, allows you to securely store and use your data in the cloud. Drag-and-drop visual workflow design and built-in ETL to connect, clean and transform data from 250+ sources. You can generate actionable insights and insights from your data using the latest Search and AI-driven technology. Our AI/ML environments can be used to build, tune, and deploy models for predictive analytics or forecasting. Our AI/ML environments are available to help you take your data to new heights. The PurpleCube Data Science module allows you to create, train, tune, and deploy AI models for forecasting and predictive analysis. PurpleCube Analytics allows you to create BI visualizations, search your data with natural language and use AI-driven insights and smart recommendations to provide answers to questions that you didn't know to ask.

The first AI platform for business that does not require any code allows you to train and deploy GPT models in any use case. Our experts are ready to assist you. Our cost-effective tools can be used to fine-tune your open source models using your own data. You can deploy your open source model securely across multiple clouds, without having to rely on a third-party vendor for your valuable data. Our team of experts will create the perfect app for your needs. Our tool allows you to easily monitor your usage, and reduce costs. Let our team of experts help you solve your problems. Automate your customer service and efficiently classify your calls. Our advanced solution allows you to streamline customer interaction and improve service delivery. Build a robust system to detect fraud and anomalies based on previously flagged information.

AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators.

Many customers of Amazon Web Services (AWS), require data storage and analytics solutions that are more flexible and agile than traditional data management systems. Data lakes are a popular way to store and analyze data. They allow companies to manage multiple data types, from many sources, and store these data in a central repository. AWS Cloud offers many building blocks to enable customers to create a secure, flexible, cost-effective data lake. These services include AWS managed services that allow you to ingest, store and find structured and unstructured data. AWS offers the data solution to support customers in building data lakes. This is an automated reference implementation that deploys an efficient, cost-effective, high-availability data lake architecture on AWS Cloud. It also includes a user-friendly console for searching for and requesting data.

Node-RED is a programming language that allows you to wire together hardware devices, online services, and APIs in new and exciting ways. It offers a browser-based editor that allows you to easily wire together flows using the large number of nodes available in the palette. The editor can be deployed to the runtime with just one click. Node-RED is a browser-based flow editor. It makes it easy to create flows by using the large number of nodes available in the palette. Flows can then be deployed to the runtime with a single click. The editor supports JavaScript functions. The library is able to save useful functions, flows, and templates for future reference. Node.js is used to build the lightweight runtime, which takes full advantage of its event-driven and non-blocking model. This makes it possible to run at the edge on low-cost hardware like the Raspberry Pi, as well as in cloud.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

Data scientists and developers can collaborate on ML projects by empowering them. Before publishing to end-users, build, load, optimize, and test models and their integrations. Data science workload can be reduced and decision-making improved by sharing and storing ML models throughout the organization. Publish updates to automatically push any changes to production models. ML-powered insights can be instantly provided within any web-based business app to increase efficiency and boost productivity. Our browser extension allows analysts and business users to seamlessly integrate models into any web application using drag-and-drop.

HPE Ezmeral ML Ops offers pre-packaged tools that enable you to operate machine learning workflows at any stage of the ML lifecycle. This will give you DevOps-like speed, agility, and speed. You can quickly set up environments using your preferred data science tools. This allows you to explore multiple enterprise data sources, and simultaneously experiment with multiple deep learning frameworks or machine learning models to find the best model for the business problems. On-demand, self-service environments that can be used for testing and development as well as production workloads. Highly performant training environments with separation of compute/storage that securely access shared enterprise data sources in cloud-based or on-premises storage.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

High-value use cases across all major verticals, with proven commercial value in just weeks ElectrifAi's largest library of pre-built machine intelligence models seamlessly integrates into existing workflows to deliver reliable and fast results. Our domain expertise is available through pre-trained, prestructured, or new models. Building machine learning is risky. ElectrifAi delivers superior results that are fast, reliable and accurate. We have over 1,000 machine learning models ready to deploy. They seamlessly integrate into existing workflows. We have the ability to quickly deploy proven ML models and provide solutions. We create the machine learning models, clean up the data, and insinuate the data. Our domain experts use your data to train the model that is most appropriate for your case.

Curiosity Modeler generates: Clear, complete specifications that reduce the creation of costly bugs. Test cases optimized to catch more defects the first time. Test data that is compliant for each test and available to testers when they need it. Test frameworks, whether open source, commercial or homegrown, can be used to execute rigorous automated tests. Rapidly create flowcharts with a variety of importers and accelerations and automatically generate comprehensive automated tests and complete test data. Automation engineers maintain coded frames, exporting objects and actions to Curiosity Modeller. Anyone can automate with a drag-and drop approach.

Connect your enterprise for single-source clarity Automate processes. Data transformation can be automated. This is not a job for menial tasks. We created a platform that does what technology should do and allows you to concentrate on the things you love. Make an impact, not a headache. Spend your time on what matters most. Give numbers meaning by adding context. Shared datasets should be always up-to-date. Do not create another rogue spreadsheet. Instead, create reusable assets for your company. Collaboration is not for data sources. Combine data from all sources. Create reusable datasets. You should always have the right answers at your disposal. Because you don't need to. Our platform automates manual tasks like gathering data, updating narratives and numbers, keeping up with changes, managing authorizations, and much more. Is it magic or not? It could be.

A system that can scale to millions of users, without requiring an engineering team. Create, customize and deploy your chat-based AI. Finance chatbot. xFinance is a 13-billion-parameter model fine-tuned using LoRA. Our goal was show that impressive results can be achieved in financial NLP without breaking the bank. Your own AI assistant to chat with documents. Single or multiple documents. Simple or complex questions. Easy-to-use deep learning platform, hardware efficient algorithms that speed up inference and lower costs. Real-time monitoring and logging of resource usage and cloud costs for deployed models. xTuring, an open-source AI software for personalization, is a powerful tool. xTuring provides a simple interface for personalizing LLMs based on your data and application.

Predictive analytics that doesn't require any programming. You only need a spreadsheet. Our machine learning algorithms will show you which factors are most influential on your business. Consider what if scenarios for your business metrics. You will be amazed at how the promo code affects your bottom line and how much you can save long-term by upgrading to a more expensive model. You can create a "recipe for data manipulation using building blocks and apply it to multiple datasets via API. It speeds up data manipulation/feature engineering by at least 11x. Learn how other businesses have transformed themselves using our powerful, easy-to-use machine learning algorithms.

Amazon SageMaker Autopilot takes out the tedious work of building ML models. SageMaker Autopilot simply needs a tabular data set and the target column to predict. It will then automatically search for the best model by using different solutions. The model can then be directly deployed to production in one click. You can also iterate on the suggested solutions to further improve its quality. Even if you don't have the correct data, Amazon SageMaker Autopilot can still be used. SageMaker Autopilot fills in missing data, provides statistical insights on columns in your dataset, extracts information from non-numeric column, such as date/time information from timestamps, and automatically fills in any gaps.

The fastest way to add analytics to your application It's easy to integrate Easy To White-label Affordable Connect and Model You can extract data from many industry-leading data sources to create configurable models that correspond to your business semantics. Visualize and query Combine multiple models to create powerful analytics. Visualize data and insights in a variety of forms, including powerful dashboards. Scale and deploy You can operate in any infrastructure model, on-premise, hybrid, or cloud. This will ensure enterprise-grade security and scale without affecting performance. Software makers Transform your application data into new revenue streams and insightful analytics Business Users Without any IT support, create operational reports and data visualizations Data Explorers Create configurable data models and mash-able analytics visualizations

Advanced embedded machine learning applications can be built without a PhD. To create custom datasets, collect sensor, audio, and camera data directly from devices, files or cloud integrations. Automated labeling tools, from object detection to audio segmentation, are available. Our cloud infrastructure allows you to set up and execute reusable scripted tasks that transform large amounts of input data. Integrate custom data sources, CI/CD tool, and deployment pipelines using open APIs. With ready-to-use DSPs and ML algorithms, you can accelerate the development of custom ML pipelines. Every step of the process, hardware decisions are made based on flash/RAM and device performance. Keras APIs allow you to customize DSP feature extraction algorithms. You can also create custom machine learning models. Visualized insights on model performance, memory, and datasets can fine-tune your production model. Find the right balance between DSP configurations and model architecture. All this is budgeted against memory constraints and latency constraints.

Exspanse simplifies the path between development and business value. Build, train and rapidly deploy powerful machine-learning models using a single interface that scales with your business. Exspanse Notebook allows you to train, tune and prototype models with the help from high-powered GPUs, processors & AI code assistant. Rapid deploy allows you to go beyond training and modeling by deploying models as APIs directly from an Exspanse Notebook. Clone and publish unique AI project to DeepSpace AI Marketplace to advance the AI Community. Power, efficiency and collaboration all in one platform. You can maximize your impact as a data scientist working alone. Our integrated platform allows you to manage and accelerate the AI development process. Turn your innovative ideas quickly and efficiently into working models. You can seamlessly transition from building AI solutions to deploying them, without needing extensive DevOps expertise.

Our platforms are a solid foundation for custom machine learning and artificial Intelligence solutions. Build next-best-action applications that learn, adapt, and optimize using reinforcement-learning based algorithms. Custom, continuously-improving deep learning vision models to solve your unique challenges. Forecasts that are up-to-date will help you predict the future. Cloud-based tools that monitor and analyze cloud data will help you make better decisions for your company. Experienced data scientists and engineers face a challenge in transforming a machine learning application from research and ad hoc code to a robust, scalable platform. With a comprehensive suite of tools to manage and deploy your machine learning applications, Strong ML makes this easier.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Amazon SageMaker Studio (IDE) is an integrated development environment that allows you to access purpose-built tools to execute all steps of machine learning (ML). This includes preparing data, building, training and deploying your models. It can improve data science team productivity up to 10x. Quickly upload data, create notebooks, tune models, adjust experiments, collaborate within your organization, and then deploy models to production without leaving SageMaker Studio. All ML development tasks can be performed in one web-based interface, including preparing raw data and monitoring ML models. You can quickly move between the various stages of the ML development lifecycle to fine-tune models. SageMaker Studio allows you to replay training experiments, tune model features, and other inputs, and then compare the results.

Voxel51, the company behind FiftyOne is responsible for the open-source software that allows you to create better computer vision workflows through improving the quality of datasets and delivering insights into your models. Explore, search and slice your datasets. Find samples and labels quickly that match your criteria. FiftyOne offers tight integrations to public datasets such as COCO, Open Images and ActivityNet. You can also create your own datasets. Data quality is one of the most important factors that affect model performance. FiftyOne can help you identify, visualize and correct the failure modes of your model. Annotation errors lead to bad models. But finding mistakes manually is not scalable. FiftyOne automatically finds and corrects label mistakes, so you can curate better-quality datasets. Manual debugging and aggregate performance metrics don't scale. Use the FiftyOne Brain for edge cases, new samples to train on, and more.

All-in-one credentialing software that will help you keep your facility healthy and compliant. Reduce time-consuming processes such as OIG monitoring and primary source verification. Silversheet prevents mistakes and missing documents from falling through the cracks, preparing for inspections. No more fumbling through multiple paper folders. You can store credentials in the cloud, and access them from any device at any time. With automatic reminders, we will let you know which credential documents are missing and/or outdated. One-step verifications of medical licenses and certifications. Online reference request and completion with customizable questions. You can set up OIG monitoring and NPDB query logging. Logging and managing credentials online can eliminate the need for paper. Log in to manage and log out providers' appointments and reappointments. Complete profiles with summary and contact information.

With getpaid, you can replace the outdated and clunky AR solutions provided by ERPs with a fully-automated platform without changing your setup. Reduce manual workflows, and spend less time on limited functionality. Automate your AR team's daily processes in a streamlined, centralized environment. Your team members will have all of the necessary data and tools available at their fingertips. Avoid expensive lock-in effects, and user-based pricing from ERP modules. You can start with full functionality and add unlimited users. Manage cash flow by integrating data into a single source of truth, and using accurate predictions of payment dates and operational KPIs. Automate time-consuming and tedious tasks when collecting invoices. Use tools that will help your business get paid faster. Manage automatic discounts when paying early. Schedule automated reminders or follow-ups. Switch between communication channels without changing applications.

DotData allows your business to concentrate on the results of your AI/ML applications and not the hassles of the data science process. Automate full-cycle AI & ML pipeline deployment in minutes. Continuous deployment allows you to update your data in real-time. Feature engineering automation reduces the time it takes to complete data science projects. Data science automation automates the discovery of unknowns in your business. Data science automation is a labor-intensive and cumbersome process that uses data science to create and deploy machine learning and AI models. Automate repetitive and time-consuming tasks that are the banes of data science work. This will reduce the development times for AI from months to days.

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

With our intuitive interface, you can create an AI-powered application in minutes. Connect GPT-3 with the internet using a Web Search Block, pull data in with an HTTP Request Block, or chain multiple Large Language Model blocks. Launch your app with a UI or bring the power to language into your community by deploying your app as a discord bot.

Azure Managed Grafana provides a fully managed solution for monitoring and analytics. Grafana Enterprise provides extensible data visualisations. Azure security allows you to deploy Grafana dashboards quickly and easily with high availability. Grafana Enterprise supports a variety of data sources. Connect to your data stores, whether they are in Azure or elsewhere. Combine charts, alerts, and logs to get a holistic view of your infrastructure and application. Correlate information across multiple datasets. Share Grafana dashboards within and outside your organization. Allow others to participate in solution monitoring and problem solving.

Mazaal, a platform for AI without code, allows anyone, regardless of their technical background, the ability to easily build and deploy AI model. Our platform simplifies AI model development by providing a user friendly interface and pre-built template. This eliminates the need to hire expensive data scientist or spend significant resources and time on development. Our platform includes powerful features such as automatic preprocessing of data, model optimization and implementation, and real-time monitoring. Mazaal makes AI available to a wider audience, empowering businesses to fully leverage AI's power to drive growth and innovate. Our platform allows organizations to stay up-to-date with rapidly changing market needs and customer expectations. Our user-friendly platform, which does not require any coding, allows businesses to easily integrate AI.

Mule, the runtime engine for Anypoint Platform, allows developers to quickly and easily connect applications to each other, allowing them to exchange data. It allows for easy integration of existing systems regardless of which technologies they use, such as JMS, Web Services and JDBC, HTTP, etc. The ESB is portable and can be deployed anywhere. It can also integrate and orchestrate events in batch or real-time and has universal connectivity. An ESB allows multiple applications to communicate with one another by acting as a transit network for data transport between applications within your organization or across the Internet. The ESB can be used as a lightweight container to host reusable services. Protect services from protocol and message formats, separate business logic and messaging, and allow location-independent service call.

Microsoft has worked hard on making.NET and SQL Server a strong combination, and VistaDB brings this capability into a small and easy-to-deploy packaged. VistaDB is a small, managed assembly that you can deploy with your app. Each database is a separate file. Using Xamarin you can fit a complete RDBMS onto your phone. VistaDB is a compliant ADO.NET provider of data with support for Entity Frame, ADO.NET and Typed Datasets, as well as its direct data access API to perform efficient cursor-based operations. VistaDB is compatible with a variety of third-party ORMs and reporting systems thanks to ADO.NET. VistaDB fully supports ADO.NET's provider factory model, allowing you to create a single codebase that targets VistaDB or SQL Server. Imagine all the things that are built on ADO.NET. Not just your application, but also reporting systems, ORMs and other data-driven libraries.

Daria's advanced automated features enable users to quickly and easily create predictive models. This significantly reduces the time and effort required to build them. Eliminate technological and financial barriers to building AI systems from scratch for businesses. Automated machine learning for data professionals can streamline and speed up workflows, reducing the amount of iterative work required. An intuitive GUI for data science beginners gives you hands-on experience with machine learning. Daria offers various data transformation functions that allow you to quickly create multiple feature sets. Daria automatically searches through millions of combinations of algorithms, modeling techniques, and hyperparameters in order to find the best predictive model. Daria's RESTful API allows you to deploy predictive models directly into production.

FinetuneFast allows you to fine-tune AI models, deploy them quickly and start making money online. Here are some of the features that make FinetuneFast unique: - Fine tune your ML models within days, not weeks - The ultimate ML boilerplate, including text-to-images, LLMs and more - Build your AI app to start earning online quickly - Pre-configured scripts for efficient training of models - Efficient data load pipelines for streamlined processing Hyperparameter optimization tools to improve model performance - Multi-GPU Support out of the Box for enhanced processing power - No-Code AI Model fine-tuning for simple customization - Model deployment with one-click for quick and hassle free deployment - Auto-scaling Infrastructure for seamless scaling of your models as they grow - API endpoint creation for easy integration with other system - Monitoring and logging for real-time performance monitoring