Alternatives to Prevision

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

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.

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.

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.

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.

You can make changes to your models quickly and easily by turning on the black box, pip installing 3LC. Iterate quickly and remove the guesswork in your model training. Visualize per-sample metrics in your browser. Analyze and fix issues in your dataset by analyzing your training. Interactive data debugging, guided by models. Find out which samples are important or inefficient. Understanding what samples work well and where your model struggles. Improve your model in different ways by weighting your data. Make sparse and non-destructive changes to individual samples or a batch. Keep track of all changes, and restore previous revisions. Data tracking and metrics per-sample, per-epoch will allow you to go deeper than standard experiment trackers. To uncover hidden trends, aggregate metrics by sample features rather than epoch. Each training run should be tied to a specific revision of the dataset for reproducibility.

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.

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.

Sensiml analytics toolkit. Create smart iot sensor devices rapidly reduce data science complexity. Compact algorithms can be created that run on small IoT devices and not in the cloud. Collect precise, traceable, and version-controlled datasets. Advanced AutoML code-gen is used to quickly create autonomous working device code. You can choose your interface and level of AI expertise. All aspects of your algorithm will remain accessible to you. Edge tuning models can be built that adapt to the data they receive. SensiML Analytics Toolkit suite automates every step of the process to create optimized AI IoT sensor recognition codes. The workflow employs a growing number of advanced ML algorithms and AI algorithms to generate code that can learn new data, either in the development phase or once it is deployed. The key tools for healthcare decision support are non-invasive, rapid screening applications that use intelligent classification of one or several bio-sensing inputs.

Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code.

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.

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.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

A platform that manages model, project, and hosting. This platform allows companies to transform data and algorithms into comprehensive, execution-ready AI strategies. Securely build, train, and manage models. You can create products that use AI models from anywhere and at any time. Reduce the risks associated with AI investments while increasing strategic flexibility. Fully automated model testing, evaluation deployment and scaling. From real-time, low latency, high-throughput, inference to batch-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. A user-friendly interface that serves as a central hub for managing projects, visualizing data, and training models through collaborative and reproducible workflows.

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

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.

Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.

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.

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.

Amazon SageMaker Data Wrangler cuts down the time it takes for data preparation and aggregation for machine learning (ML). This reduces the time taken from weeks to minutes. SageMaker Data Wrangler makes it easy to simplify the process of data preparation. It also allows you to complete every step of the data preparation workflow (including data exploration, cleansing, visualization, and scaling) using a single visual interface. SQL can be used to quickly select the data you need from a variety of data sources. The Data Quality and Insights Report can be used to automatically check data quality and detect anomalies such as duplicate rows or target leakage. SageMaker Data Wrangler has over 300 built-in data transforms that allow you to quickly transform data without having to write any code. After you've completed your data preparation workflow you can scale it up to your full datasets with SageMaker data processing jobs. You can also train, tune and deploy models using SageMaker data processing jobs.

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.

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.

KitOps, a packaging, versioning and sharing system, is designed for AI/ML project. It uses open standards, so it can be used with your existing AI/ML, DevOps, and development tools. It can also be stored in the enterprise container registry. It is the preferred solution of AI/ML platform engineers for packaging and versioning assets. KitOps creates an AI/ML ModelKit that includes everything you need to replicate it locally or deploy it in production. You can unpack a ModelKit selectively so that different team members can save storage space and time by only taking what they need to complete a task. ModelKits are easy to track, control and audit because they're immutable, signed and reside in your existing container registry.

Unstructured data is the future for AI. This future is now possible. Build an ML/AI pipeline to scale all your unstructured data from one place. Graviti allows you to use better data to create better models. Learn about Graviti, the data platform that allows AI developers to manage, query and version control unstructured data. Quality data is no longer an expensive dream. All your metadata, annotations, and predictions can be managed in one place. You can customize filters and see the results of filtering to find the data that meets your needs. Use a Git-like system to manage data versions and collaborate. Role-based access control allows for safe and flexible team collaboration. Graviti's built in marketplace and workflow creator makes it easy to automate your data pipeline. No more grinding, you can quickly scale up to rapid model iterations.

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.

Kraken is suitable for all data scientists and analysts. It is designed to be easy-to-use and no-code automated machine-learning platform. The Kraken no code automated machine learning platform (AutoML), simplifies and automates data science tasks such as data prep, data cleaning and algorithm selection. It also allows for model training and deployment. Kraken was designed with engineers and analysts in mind. If you've done data analysis before, you're ready! Kraken's intuitive interface and integrated SONAR(c), training make it easy for citizens to become data scientists. Data scientists can work more efficiently and faster with advanced features. You can use Excel or flat files for daily reporting, or just ad-hoc analysis. With Kraken's drag-and-drop CSV upload feature and the Amazon S3 connector, you can quickly start building models. Kraken's Data Connectors allow you to connect with your favorite data warehouse, business intelligence tool, or cloud storage.

SAS technologies combine to provide powerful tools for visual information. You can access, manipulate, analyze, and present information in visual formats. SAS Visual Machine Learning allows you to expand your analytics by using machine learning and deep learning capabilities. This makes it easier to visualize and report better. Visualize and discover relationships in your data. You can create and share interactive dashboards and reports, and use self service analytics to quickly assess possible outcomes to make data-driven, smarter decisions. This solution runs in SAS®, Viya®. It allows you to explore data and create or adjust predictive analytical models. Analysts, statisticians, data scientists, and analysts can work together to refine and refine models for each group or segment, allowing them to make informed decisions. A comprehensive visual interface allows you to solve complex analytical problems. It handles all aspects of the analytics lifecycle.

Openlayer will accept your data and models. Work with the team to align performance and quality expectations. You can quickly identify the reasons behind failed goals and find a solution. You have all the information you need to diagnose problems. Retrain the model by generating more data that looks similar to the subpopulation. Test new commits in relation to your goals, so that you can ensure a systematic progress without regressions. Compare versions side by side to make informed decisions. Ship with confidence. Save time on engineering by quickly determining what drives model performance. Find the quickest ways to improve your model. Focus on cultivating high quality and representative datasets and knowing the exact data required to boost model performance.

The Salford Predictive Modeler® (SPM), software suite, is highly accurate and extremely fast for developing predictive, descriptive, or analytical models. Salford Predictive Modeler®, which includes the CART®, TreeNet®, Random Forests® engines, and powerful new automation capabilities and modeling capabilities that are not available elsewhere, is a software suite that includes the MARS®, CART®, TreeNet[r], and TreeNet®. The SPM software suite's data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation. SPM algorithms are essential in advanced data science circles. Automation of model building is made easier by the SPM software suite. It automates significant portions of the model exploration, refinement, and refinement process for analysts. We combine all results from different modeling strategies into one package for easy review.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

Aquarium's embedding technologies surface the biggest problems with your model and find the right data to fix them. You can unlock the power of neural networks embeddings, without having to worry about infrastructure maintenance or debugging embeddings. Find the most critical patterns in your dataset. Understanding the long tail of edge case issues and deciding which issues to tackle first. Search through large datasets without labels to find edge cases. With few-shot learning, you can quickly create new classes by using a few examples. We offer more value the more data you provide. Aquarium scales reliably to datasets with hundreds of millions of points of data. Aquarium offers customer success syncs and user training as well as solutions engineering resources to help customers maximize their value. We offer an anonymous mode to organizations who wish to use Aquarium without exposing sensitive data.

Scale Data Engine helps ML teams build better datasets. Bring together data, ground truth and model predictions for a quick fix to model failures and issues with data quality. Scale Data Engine can optimize your labeling costs by identifying errors, class imbalances, and edge cases within your data. Improve model performance by identifying and fixing model failures. Curate unlabeled data using active learning and edge case analysis to find and label high-value information. Curate the best datasets with ML engineers and labelers on the same platform. Visualize and explore your data easily to quickly identify edge cases that require labeling. Check the performance of your models and ship only the best. Our powerful UI allows you to view your data, aggregate statistics, and metadata with rich overlays. Scale Data Engine allows visualization of images, lidar scenes and videos. All associated labels, predictions and metadata are displayed overlaid.

Amazon SageMaker Pipelines allows you to create ML workflows using a simple Python SDK. Then visualize and manage your workflow with Amazon SageMaker Studio. SageMaker Pipelines allows you to be more efficient and scale faster. You can store and reuse the workflow steps that you create. Built-in templates make it easy to quickly get started in CI/CD in your machine learning environment. Many customers have hundreds upon hundreds of workflows that each use a different version. SageMaker Pipelines model registry allows you to track all versions of the model in one central repository. This makes it easy to choose the right model to deploy based on your business needs. SageMaker Studio can be used to browse and discover models. Or, you can access them via the SageMaker Python SDK.

Towhee can automatically optimize your pipeline for production-ready environments by using our Python API. Towhee supports data conversion for almost 20 unstructured data types, including images, text, and 3D molecular structure. Our services include pipeline optimizations that cover everything from data decoding/encoding to model inference. This makes your pipeline execution 10x more efficient. Towhee integrates with your favorite libraries and tools, making it easy to develop. Towhee also includes a Python method-chaining API that allows you to describe custom data processing pipelines. Schemas are also supported, making it as simple as handling tabular data to process unstructured data.

Select the configurations and resources required for specific code segments within your project. It only takes seconds to save and apply changes in a training scenario. Select the right configuration of computing resources to launch training models in a matter of seconds. All will be created automatically, without the need to manage infrastructure. Select a serverless or dedicated operating mode. All in one interface, manage project data, save to datasets and connect to databases, object storage or other repositories. Create a ML model with colleagues from around the world, share the project and set budgets across your organization. Launch your ML within minutes, without developers' help. Try out experiments with different models being published simultaneously.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.

Models can be opaque about what data they failed to perform well on and why. Galileo offers a variety of tools that allow ML teams to quickly inspect and find ML errors up to 10x faster. Galileo automatically analyzes your unlabeled data and identifies data gaps in your model. We get it - ML experimentation can be messy. It requires a lot data and model changes across many runs. You can track and compare your runs from one place. You can also quickly share reports with your entire team. Galileo is designed to integrate with your ML ecosystem. To retrain, send a fixed dataset to the data store, label mislabeled data to your labels, share a collaboration report, and much more, Galileo was designed for ML teams, enabling them to create better quality models faster.

Fully managed infrastructure, tools and workflows allow you to build, train and deploy models faster. Scale inference and deploy custom AI & LLMs in seconds on any infrastructure. Schedule batch jobs to handle your most demanding tasks, and only pay per second. Optimize costs by utilizing GPUs, spot instances, and automatic failover. YAML simplifies complex infrastructure setups by allowing you to train with a single command. Automate the scaling up of workers during periods of high traffic, and scaling down to zero when inactive. Deploy cutting edge models with persistent endpoints within a serverless environment to optimize resource usage. Monitor system and inference metrics, including worker counts, GPU utilization, throughput, and latency in real-time. Split traffic between multiple models to evaluate.

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.

Superb AI offers a new generation of machine learning data platform to AI team members so they can create better AI in a shorter time. The Superb AI Suite, an enterprise SaaS platform, was created to aid ML engineers, product teams and data annotators in creating efficient training data workflows that save time and money. Superb AI can help ML teams save more than 50% on managing training data. Our customers have averaged a 80% reduction in the time it takes for models to be trained. Fully managed workforce, powerful labeling and training data quality control tools, pre-trained models predictions, advanced auto-labeling and filtering your datasets, data source and integration, robust developer tools, ML work flow integrations and many other benefits. Superb AI makes it easier to manage your training data. Superb AI provides enterprise-level features to every layer of an ML organization.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.

Easily extract, transform, manage & process CAD data, FE data, CFD and test results. Create automatic data pipelines to support machine learning, deep learning, and ROM. Data science barriers can be removed without coding. Key Ward's platform, the first engineering no-code end-to-end solution, redefines how engineers work with their data. Our software allows engineers to handle multi-source data with ease, extract direct value using our built-in advanced analytical tools, and build custom machine and deep learning model with just a few clicks. Automatically centralize, update and extract your multi-source data, then sort, clean and prepare it for analysis, machine and/or deep learning. Use our advanced analytics tools to correlate, identify patterns, and find dependencies in your experimental & simulator data.

Noogata's AI Blocks are designed for professionals who need to quickly turn their data into actionable insight to drive business growth, profit, and opportunities. AI and ML tools designed for business users, not data scientist. Your business intelligence and analytics tools can be turbocharged, including your spreadsheets. To turn your data into insights, you can use dozens of pre-built AI models. To tackle your most pressing business problems, connect and customize the blocks. Connect your data sources and data platforms, including Google Sheets and Excel. Get actionable insights, best practices, and recommendations. We understand that business users have different data requirements and objectives. Therefore, the Noogata AI blocks and libraries were created to meet your specific needs. Find out the competitive landscape to improve your online sales performance. To gain insight into pricing, content strategy, advertising recommendations, and more, run analyses in minutes.

The platform is easy to use and quick to implement machine learning and computer vision workflows. Sense makes it easy to create and deploy AI IoT solutions on any cloud, edge or on-premise. Learn how Sense makes it easy for AI/ML teams to create and deploy AI IoT solutions to any cloud, the edge or on-premise. It is powerful enough for ML engineers but simple enough for subject matter experts. Sense Data Annotation maximizes the success of your machine-learning models by making it the easiest and fastest way to label image and video data for high-quality training datasets. The Sense platform provides one-touch labeling integration to enable continuous machine learning at edge for simplified management.

A platform for ML engineers that provides synthetic data to facilitate the development of better AI models. Simple APIs allow for the instant generation of photorealistic, well-labeled images. Cloud-based generation platform that is highly scalable delivers millions of perfectly labeled photos. On-demand data allows for new data-centric approaches to create more efficient models. A broader range of pixel-perfect labels, including segmentation maps and dense 2D/3D landmarks as well as depth maps, surface norms, depth maps, and more, is now available. You can quickly design, test, refine, and revise your products before you build hardware. To optimize your system, prototype different imaging modalities, camera placements, lens types, and other lens types. Your models will be less biased if there are misbalanced data sets. Privacy will still be protected. Equal representation across identities, facial attributes and poses, as well as lighting and camera settings. We have worked with top-notch customers in many different use cases.

SANCARE is a start up that specializes in Machine Learning applied to hospital data. We work with some of the most respected scientists in the field. SANCARE offers Medical Information Departments an intuitive and ergonomic interface that promotes rapid adoption. All documents that make up the computerized patient record are available to the user. Each step of the coding process can be traced to external checks. Machine learning allows you to create powerful predictive models using large amounts of data. It also allows you to consider the notion of context which is not possible with rule engines or semantic analysis engines. It is possible to automate complex decision making processes and to detect weak signals that are often ignored by humans. The SANCARE software machine-learning engine is based upon a probabilistic approach. It uses a large number of examples to predict the correct codes without any indication.

Create & Share Delightful Apps for Machine Learning. Gradio allows you to quickly and easily demo your machine-learning model. It has a friendly interface that anyone can use, anywhere. Installing Gradio is easy with pip. It only takes a few lines of code to create a Gradio Interface. You can choose between a variety interface types to interface with your function. Gradio is available as a webpage or embedded into Python notebooks. Gradio can generate a link that you can share publicly with colleagues to allow them to interact with your model remotely using their own devices. Once you have created an interface, it can be permanently hosted on Hugging Face. Hugging Face Spaces hosts the interface on their servers and provides you with a shareable link.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.