Alternatives to FeedStock Cortex

Fully managed service that provides product recommendations tailored to the needs of your customers. BytePlus recommend draws on our machine learning expertise to provide dynamic and targeted recommendations. Our industry-leading team has a track history of delivering recommendations on some of the most popular platforms in the world. To engage users better and make personalized suggestions based upon customer behavior, you can use the data from your users. BytePlus recommend is easy to use, leveraging your existing infrastructure and automating the machine-learning workflow. BytePlus recommend leverages our research on machine learning to deliver personalized recommendations that are tailored to your audience's preferences. Our algorithm team is highly skilled and can develop customized strategies to meet changing business goals and needs. Pricing is determined based on A/B testing results. Based on your business needs, optimization goals are set.

PrecisionOCR is an easy-to-use, secure and HIPAA-compliant cloud-based optical character recognition (OCR) platform that organizations and providers can user to extract medical meaning from unstructured health care documents. Our OCR tooling leverages machine learning (ML) and natural language processing (NLP) to power semi-automatic and automated transformations of source material, such as pdfs and images, into structured data records. These records integrate seamlessly with EMR data using the HL7s FHIR standards to make the data searchable and centralized alongside other patient health information. Our health OCR technology can be accessed directly in a simple web-UI or the tooling can be used via integrations with API and CLI support on our open healthcare platform. We partner directly with PrecisionOCR customers to build and maintain custom OCR report extractors, which intelligently look for the most critical health data points in your health documents to cut through the noise that comes with pages of health information. PrecisionOCR is also the only self-service capable health OCR tool, allowing teams to easily test the technology for their task workflows.

Manage unstructured data to develop AI solutions in record time. Enterprise-grade data platform with vision AI. Dataloop offers a single-stop-shop for building and deploying powerful data pipelines for computer vision, data labeling, automation of data operations, customizing production pipelines, and weaving in the human for data validation. Our vision is to make machine-learning-based systems affordable, scalable and accessible for everyone. Explore and analyze large quantities of unstructured information from diverse sources. Use automated preprocessing to find similar data and identify the data you require. Curate, version, cleanse, and route data to where it's required to create exceptional AI apps.

Your contact center is your greatest untapped resource. It's where customers can continuously coach your business. PrediCX, an AI platform that unlocks this value through predictive insight and automation across every channel to optimize customer experience and customer service, is called PrediCX. AI can be used to extract predictive insights from every customer interaction, regardless of the channel. Recommendations to improve profitability and customer service. Receive early warnings about issues and automatic coaching. You can quickly track any urgent complaints or enquiries and point your customers to the best resource or channel. AI uses concepts and not keywords to classify incoming comments so that analysis is accurate, insightful, and not predetermined. Customer feedback is not overlooked. Automated triage of queries from digital channels, automatically classify agents and assist them, and enhance chatbots.

Invert provides a complete solution for collecting, cleaning and contextualizing data. This ensures that every analysis and insight are based on reliable and organized data. Invert collects, standardizes, and models all your bioprocessing data. It has powerful built-in tools for analysis, machine-learning, and modeling. Data that is clean, standardized and pristine is only the beginning. Explore our suite of tools for data management, analysis and modeling. Replace manual workflows with spreadsheets or statistical software. Calculate anything with powerful statistical features. Automatically generate reports using recent runs. Add interactive plots and calculations and share them with collaborators. Streamline the planning, coordination and execution of experiments. Find the data you want and dive deep into any analysis. Find all the tools to manage your data, from integration to analysis and modeling.

Building Machine Learning Pipelines internally can be costly and take longer than expected. Gartner's statistics show that more than 80% will fail in AI Projects. Cortex helps teams set up machine learning faster than other alternatives and puts data to work for business results. Every team can create their own AI Predictions. You no longer need to wait for a team to be hired and costly infrastructure to be built. Cortex allows you to make predictions using the data you already own, all via a simple web interface. Everyone can now be a Data Scientist! Cortex automates the process for turning raw data into Machine Learning Pipelines. This eliminates the most difficult and time-consuming aspects of AI. These predictions are accurate and always up-to-date because Cortex continuously ingests new data and updates the underlying model automatically, with no human intervention.

Easy management, deployment and monitoring of machine learning models for supercharging MLOps. Available for all organizations on the best AI platform. You can use navio for various machine learning operations across your entire artificial intelligence landscape. Machine learning can be integrated into your business workflow to make a tangible, measurable impact on your business. navio offers various Machine Learning Operations (MLOps), which can be used to support you from the initial model development phase to the production run of your model. Automatically create REST endspoints and keep track the clients or machines that interact with your model. To get the best results, you should focus on exploring and training your models. You can also stop wasting time and resources setting up infrastructure. Let navio manage all aspects of product ionization so you can go live quickly with your machine-learning models.

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.

Cloud AutoML is a set of machine learning products that allows developers with limited machine-learning expertise to create high-quality models tailored to their business needs. It uses Google's state of the art neural architecture and transfer learning search technology. Cloud AutoML uses more than 10 years' of Google Research technology to help machine learning models achieve faster performance, better predictions, and more accurate predictions. Cloud AutoML's graphical user interface makes it easy to build, evaluate, improve, deploy, and test models based upon your data. Only a few clicks away is your custom machine learning model. Google's human-labeling service can assign a team to clean and annotate your labels. This will ensure that your models are trained with high-quality data.

Our clients can get the best collection software on the market. We use machine learning to maximize collections, minimize costs, optimize customer relationships, and offer the best collections software. Trusted by top lenders and agencies for over 14 years, the only digital-native, full-suite, omnichannel collections software on the market. Our cloud-based platform is secure, compliant and easy to use. It delivers the right message, in a right medium, to the right borrower quickly and cheaply, maximising collections and customer experience. Our strategy engine integrates powerful workflow capabilities and decision tree capabilities into one platform. This allows you to create unique customer experiences that improve collections. Our award-winning technology learns from experience to provide a seamless and efficient end-to-end experience for customers and agents, maximizing dollars collected.

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.

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.

It can take weeks, months or even years to build a model. Reproducing model results, maintaining version control and auditing past work can be complex. Model building is an iterative task. It is important to record each step and how you got there. A model should not be a file that is hidden somewhere. It should be a tangible object that can be tracked and analyzed by all parties. Prevision.io allows users to track each experiment as they train it. You can also view its characteristics, automated analyses, versions, and version history as your project progresses, regardless of whether you used our AutoML or other tools. To build highly performant models, you can automatically experiment with dozens upon dozens of feature engineering strategies. The engine automatically tests different feature engineering strategies for each type of data in a single command. Tabular, text, and images are all options to maximize the information in your data.

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.

Pachyderm's Data Versioning provides teams with an automated and efficient way to track all data changes. File-based versioning allows for a complete audit trail of all data and artifacts across the pipeline stages, including intermediate results. Versioning can be automated and guaranteed because they are native objects, not metadata pointers. Without writing additional code, autoscale data processing by parallel. Incremental processing reduces computation by only processing the differences and automatically skipping duplicates. Pachyderm's Global IDs allow teams to track any result back to its raw input. This includes all analysis, parameters, codes, and intermediate results. The Pachyderm Console allows you to see your DAG (directed-acyclic graph) and helps with reproducibility using Global IDs.

The Accern No-Code NLP Platform empowers citizen data scientists to extract insights from unstructured data, minimize time to value and maximize ROI with pre-built AI/ML/NLP solutions. Recognized as the first No-Code NLP platform and industry leader with the highest accuracy scores, Accern also enables data scientists to customize end-to-end workflows that enhance existing models and enrich BI dashboards.

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.

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.

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

Forrester research shows that between 60% and 73% (or more) of enterprise data is not used for analytics. Find out how DataStories can help you maximize the value of your data. DataStories made machine learning easy for business people by making advanced machine learning understandable. DataStories Platform offers A.I. DataStories Platform is an A.I. tool that explains in an intuitive way and takes less than 30 minutes how to predict, understand and guide your business targets based upon the context and business data. DataStories aims to empower everyone to make data-driven decisions. Business experts are often left behind by complex tools and lack of access to analytics. We offer a self service analytics platform. Now you can run analytics on your own and share the results as explainable and interactive data stories. These can also be exported into PowerPoint.

Scalable Python framework designed to build real-time intelligent applications, data pipelines, and integrate AI/ML models

A powerful labeling platform for all stages and types of ML development. We leverage a suite of industry-leading computer vision algorithms to automatically detect and classify the content of your images and videos. Creating detailed segmentation information is a time-consuming process. Machine assistance speeds up task completion by as much as 70%, saving you both time and money. We leverage ML to propose labels that accelerate human labeling. This includes computer vision models to automatically detect, localize, and classify entities in your images and videos before handing off the task to our workforce. Automatic labelling reduces workforce costs and allows annotators to spend their time on the more complicated steps of the annotation process. Our video annotation tool is built to handle 4K resolution and long-running videos natively and provides innovative features like interpolation, object proposal, and entity resolution.

The list is not exhaustive. Our solution engineers will work with your team to customize Kolena to your workflows and business metrics. The aggregate metrics do not tell the whole story. Unexpected model behavior is the norm. The current testing processes are manual and error-prone. They also cannot be repeated. Models are evaluated based on arbitrary statistics that do not align with product objectives. It is difficult to track model improvement as data evolves. Techniques that are adequate for research environments do not meet the needs of production.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

Ginger Software is a productivity-focused company with an award winning record. It helps you write faster and more effectively thanks to grammar checker and punctuation tools. These tools automatically detect and correct grammar mistakes and misused words. Ginger, an AI-powered writing assistant, can correct your texts, improve style, and increase your creativity. Ginger does more than just spellcheck and grammar. Ginger can suggest context-based corrections by taking into account complete sentences. This greatly speeds up writing, especially when you are working on lengthy emails or documents. Ginger's AI will suggest other ways to convey your message. It is especially useful for simplifying long sentences. To find the perfect match, double-click any word on any website.

Data engineering workflows that are powerful, but without the headaches of infrastructure. Simple, reusable Python is used to define complex streaming, scheduling and data backfill pipelines. Fetch all your data in real time, no matter how complicated. Deep learning and LLMs can be used to make decisions along with structured business data. Don't pay vendors for data that you won't use. Instead, query data right before online predictions. Experiment with Jupyter and then deploy into production. Create new data workflows and prevent train-serve skew in milliseconds. Instantly monitor your data workflows and track usage and data quality. You can see everything you have computed, and the data will replay any information. Integrate with your existing tools and deploy it to your own infrastructure. Custom hold times and withdrawal limits can be set.

Time-series analysis is crucial for many companies' day-to-day operations. The most popular uses include analyzing foot traffic and conversions for retailers, detecting data abnormalities, identifying correlations over sensor data, and generating high-quality suggestions. Cloud Inference API Alpha allows you to gather insights from your time-series data in real-time. You can get all the information you need to understand your API query results, including the groups of events examined, the number and background probabilities of each event returned. You can stream data in real time, which makes it possible to calculate correlations for real events. Rely on Google Cloud's entire infrastructure and defense-in depth approach to security, which has been innovating for over 15 years via consumer apps. Cloud Inference API integrates seamlessly with other Google Cloud Storage services.

The Descartes Labs Platform was created to address some of the most pressing geospatial analysis questions in the world. The platform allows customers to quickly and efficiently build models and algorithms that transform their businesses. We help AI become a core competency by providing data scientists and their line of business colleagues with the best geospatial and modeling tools in one package. Our massive data archive and their own data can be used by data science teams to create models faster than ever before. Our cloud-based platform allows customers to rapidly and securely scale machine learning, statistical, or computer vision models to inform business decisions using powerful raster-based analytics. Our extensive API documentation, tutorials and guides, as well as demos, provide users with a rich knowledge base that allows them to quickly deploy high-value apps across a variety of industries.

Mona is a flexible and intelligent monitoring platform for AI / ML. Data science teams leverage Mona’s powerful analytical engine to gain granular insights about the behavior of their data and models, and detect issues within specific segments of data, in order to reduce business risk and pinpoint areas that need improvements. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. In 2018, we ventured on a mission to empower data teams to make AI more impactful and reliable, and to raise the collective confidence of business and technology leaders in their ability to make the most out of AI. We have built the leading intelligent monitoring platform to provide data and AI teams with continuous insights to help them reduce risks, optimize their operations, and ultimately build more valuable AI systems. Enterprises in a variety of industries leverage Mona for NLP/NLU, speech, computer vision, and machine learning use cases. Mona was founded by experienced product leaders from Google and McKinsey&Co, is backed by top VCs, and is HQ in Atlanta, Georgia. In 2021, Mona was recognized by Gartner as a Cool Vendor in AI Operationalization and Engineering.

Experience a completely new way to train machine learning models on Mac. Create ML simplifies model training and produces powerful Core ML Core models. Train multiple models with different datasets in one project. Preview the performance of your model using Continuity on your Mac with your iPhone's camera and microphone, or by dropping in sample data. Pause, save, resume and extend your training. Learn interactively how your model performs using test data from your evaluation dataset. Explore key metrics in relation to specific examples, to identify difficult use cases, additional investments in data collection and opportunities to improve model quality. You can improve the performance of model training by using an external graphics processor with your Mac. You can train models on your Mac at lightning speed by utilizing the CPU and GPU. Create ML offers a wide range of model types.

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.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

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.

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

Abacus.AI is the first global end-to-end autonomous AI platform. It enables real-time deep-learning at scale for common enterprise use cases. Our innovative neural architecture search methods allow you to create custom deep learning models and then deploy them on our end-to-end DLOps platform. Our AI engine will increase user engagement by at least 30% through personalized recommendations. Our recommendations are tailored to each user's preferences, which leads to more interaction and conversions. Don't waste your time dealing with data issues. We will automatically set up your data pipelines and retrain the models. To generate recommendations, we use generative modeling. This means that even if you have very little information about a user/item, you won't have a cold start.

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.

MyDataModels' best-in-class predictive analytics model TADA allows professionals to use their Small Data to improve their business. It is a simple-to-use tool that is easy to set up. TADA is a predictive modeling tool that delivers fast and useful results. With our 40% faster automated data preparation, you can transform your time from days to just a few hours to create ad-hoc effective models. You can get results from your data without any programming or machine learning skills. Make your time more efficient with easy-to-understand models that are clear and understandable. You can quickly turn your data into insights on any platform and create automated models that are effective. TADA automates the process of creating predictive models. Our web-based pre-processing capabilities allow you to create and run machine learning models from any device or platform.

Ludwig is a low code framework for building custom AI networks like LLMs or other deep neural network models. Create custom models easily: a declarative YAML file is all that you need to train a modern LLM using your data. Support for multitasking and multimodality learning. Comprehensive configuration validation detects invalid parameters and prevents runtime errors. Optimized for efficiency and scale: automatic batch size selection (DDP, QLoRA), distributed training (DDP), parameter-efficient fine-tuning, 4-bit quantization, and larger-than memory datasets. Expert level control: Retain full control over your models, down to the activation function. Support for hyperparameter optimizing, explainability and rich metric visualisations. Modular and extensible - experiment with different models, tasks, features and modalities by changing just a few parameters in the configuration. Think of building blocks for deep-learning.

Butler is a platform that allows developers to turn AI into simple APIs. In minutes, you can create, train, and deploy AI Models. No AI experience is required. Butler's user interface is easy to use and allows you to create a complete labeled data set. You can forget about the tedious labeling. Butler automatically selects and trains the right ML model for you. There is no need to spend hours researching which models are the most effective. Butler offers a wide range of customization options that allow you to tailor your model to meet your needs. Don't waste time constructing custom models from scratch or modifying pre-defined models. Any image or document that is not structured can be parsed to extract key data fields and tables. With lightning fast document parsing APIs, you can free your users from the tedious task of manually entering data. Information can be extracted from text, including names, terms, and places. Your product should be able to understand your users as well as you.

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.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

Datoin removes barriers to entry into Machine Learning by using Graphical Interface and No Code approach. It's designed to quickly turn your vision into reality. Re-using blocks over and over is the best way to reduce costs. The Datoin's Block Superstore has a wide range of blocks, including enterprise software connectors and ETL tools, machine-learning libraries, NLP libraries, cloud service integration, SaaS APIs, and machine learning libraries. The best thing about Datoin is that the blocks are constantly being added to the store as we cover more use cases. Pre-built machine learning models make it easy to get started quickly and eliminate the need for training. We have created and built blocks that solve common problems across all industries and functional areas. Edit existing apps to quickly test them if you are unsure of specific functionality or efficacy.

Segmind simplifies access to large compute. It can be used to run high-performance workloads like Deep learning training and other complex processing jobs. Segmind allows you to create zero-setup environments in minutes and lets you share the access with other members of your team. Segmind's MLOps platform is also able to manage deep learning projects from start to finish with integrated data storage, experiment tracking, and data 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.

Even if you have the best developers, every test makes them slower. 80% of your software testing is pointless. The problem is that you don't know which 20%. We use your data to find the right 20% so you can ship faster. We offer shrink-wrapped predictive testing selection. This machine learning-based method is used by companies like Facebook and can be used by all companies. We support multiple languages, test runners and CI systems. Bring Git. Launchable uses machine-learning to analyze your source code and test failures. It doesn't rely solely on code syntax analysis. Launchable can easily add support for any file-based programming language. This allows us to scale across projects and teams with different languages and tools. We currently support Python, Ruby and Java, JavaScript and Go, as well as C++ and C++. We regularly add new languages to our support.

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.

Reach strong and transparent audiences or lookalikes to your ads. You can sell more with the most valuable Enhencer audiences via Facebook, Instagram, and Google campaigns. In minutes, integrate your ecommerce website with Shopify Connect or Google Tag Manager. Enhencer will automatically collect all the data you need from your website visitors across all ecommerce platforms. Enhencer's AI-Engine, which is self-learning, will automatically determine and extract the most valuable visitors from your website based on their behavior. To boost your Facebook and Google Ads campaigns, you can obtain the most relevant lookalike and real-time retargeting audiences for your brand and products. Enhencer clearly presents the audience segments. This allows you to understand how website behavior leads to a sale. Real-time Enhencer audiences allow you to keep up with the changing trends and fast-paced e-commerce industry.

You can assign tasks to your deliverymen and track their progress in real time. Distributors, retailers, and consumers can easily choose products, quantity, and place orders. The Mobile App allows users to track deliverymen and status in real time, just like the Web App. The Consumer Mobile App is available as a white-label application for your business. The process is transparent and fraud-free if each stage can be confirmed and tracked. Flexible algorithm takes into account 30+ constraints such as multimodal deliveries, weight volumes capacities, time windows... to create dynamically the best transportation plan. Assign orders to vehicles, optimize length, width and height, and visualize the shipment in 3D. Inventory routing reduces stockouts and saves distribution costs. Automatically assign orders that have temperature restrictions to refrigerated vehicles. Abivin vRoute integrates with telematics devices to keep track of temperature levels.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

Machine learning applications can be deployed to production in minutes instead of months. Automate the transformation of raw data and generate training data sets. Also, you can serve features for online inference at large scale. Replace bespoke data pipelines by robust pipelines that can be created, orchestrated, and maintained automatically. You can increase your team's efficiency and standardize your machine learning data workflows by sharing features throughout the organization. You can serve features in production at large scale with confidence that the systems will always be available. Tecton adheres to strict security and compliance standards. Tecton is neither a database nor a processing engine. It can be integrated into your existing storage and processing infrastructure and orchestrates it.