Datatron Alternatives

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required.

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs.

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

Quality assurance for machine learning simplified. Snitch removes the noise to surface only the most useful information to improve your models. Track your model’s performance beyond just accuracy with powerful dashboards and analysis. Identify problems in your data pipeline and distribution shifts before they affect your predictions. Stay in production once you’ve deployed and gain visibility on your models & data throughout its cycle. Keep your data secure, cloud, on-prem, private cloud, hybrid, and you decide how to install Snitch. Work within the tools you love and integrate Snitch into your MLops pipeline! Get up and running quickly, we keep installation, learning, and running the product easy as pie. Accuracy can often be misleading. Look into robustness and feature importance to evaluate your models before deploying. Gain actionable insights to improve your models. Compare against historical metrics and your models’ baseline.

AI and non-AI models need risk management and oversight. Fairly provides a continuous monitoring system for advanced model governance and oversight. With Fairly, risk and compliance teams can collaborate with data science and cyber security teams easily to ensure models are reliable and secure. Fairly makes it easy to stay up-to-date with policies and regulations for procurement, validation and audit of non-AI, predictive AI and generative AI models. Fairly simplifies the model validation and auditing process with direct access to the ground truth in a controlled environment for in-house and third-party models, without adding overhead to development and IT teams. Fairly's platform ensures compliant, secure, and ethical models. Fairly helps teams identify, assess, monitor, report and mitigate compliance, operational and model risks according to internal policies and external regulations.

The biggest challenge to scaling AI-powered decision-making is unused data. IBM Cloud Pak® for Data is a unified platform that delivers a data fabric to connect and access siloed data on-premises or across multiple clouds without moving it. Simplify access to data by automatically discovering and curating it to deliver actionable knowledge assets to your users, while automating policy enforcement to safeguard use. Further accelerate insights with an integrated modern cloud data warehouse. Universally safeguard data usage with privacy and usage policy enforcement across all data. Use a modern, high-performance cloud data warehouse to achieve faster insights. Empower data scientists, developers and analysts with an integrated experience to build, deploy and manage trustworthy AI models on any cloud. Supercharge analytics with Netezza, a high-performance data warehouse.

Krista is a nothing-like-code intelligent automation platform that orchestrates your people, apps, and AI so you can optimize business outcomes. Krista builds and integrates machine learning and apps more simply than you can imagine. Krista is purpose-built to automate business outcomes, not just back-office tasks. Optimizing outcomes requires spanning departments of people & apps, deploying AI/ML for autonomous decision-making, leveraging your existing task automation, and enabling constant change. By digitizing complete processes, Krista delivers organization-wide, bottom-line impact.Krista empowers your people to create and modify automations without programming. Democratizing automation increases business speed and keeps you from waiting in the dreaded IT backlog. Krista dramatically reduces TCO compared to your current automation platform.

Get in minutes what used to take years to build. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy, maintain and improve ML in production. Superwise is an open platform that integrates with any ML stack and connects to your choice of communication tools. Want to take it further? Superwise is API-first and everything (and we mean everything) is accessible via our APIs. All from the comfort of the cloud of your choice. When it comes to ML monitoring you have full self-service control over everything. Configure metrics and policies through our APIs and SDK or simply select a monitoring template and set the sensitivity, conditions, and alert channels of your choice. Try Superwise out or contact us to learn more. Easily create alerts with Superwise’s ML monitoring policy templates and builder. Select from dozens of pre-build monitors ranging from data drift to equal opportunity, or customize policies to incorporate your domain expertise.

Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale, build trusted AI solutions, and increase revenue.

Create customized monitors for your machine learning models with our magically-simple monitor builder, and get alerts for issues like concept drift, model performance degradation, bias and more. Aporia integrates seamlessly with any ML infrastructure. Whether it’s a FastAPI server on top of Kubernetes, an open-source deployment tool like MLFlow or a machine learning platform like AWS Sagemaker. Zoom into specific data segments to track model behavior. Identify unexpected bias, underperformance, drifting features and data integrity issues. When there are issues with your ML models in production, you want to have the right tools to get to the root cause as quickly as possible. Go beyond model monitoring with our investigation toolbox to take a deep dive into model performance, data segments, data stats or distribution.

While not all models are created equal, every model needs governance to drive responsible and ethical decision-making throughout the business. IBM® watsonx.governance™ toolkit for AI governance allows you to direct, manage and monitor your organization’s AI activities. It employs software automation to strengthen your ability to mitigate risks, manage regulatory requirements and address ethical concerns for both generative AI and machine learning (ML) models. Access automated and scalable governance, risk and compliance tools that cover operational risk, policy management, compliance, financial management, IT governance and internal or external audits. Proactively detect and mitigate model risks while translating AI regulations into enforceable policies for automatic enforcement.

Standardize your AI governance efforts across diverse stakeholders, ensure regulatory readiness of your governance processes, and measure and manage your AI risks and compliance. Go from fragmented teams and processes to a centralized repository of trusted governance that makes it easy to ensure all of your AI/ML projects are being governed effectively. Stay up-to-date with the latest regulations and standards with AI Policy Packs that meet current and emerging regulations. Credo AI is an intelligence layer that sits on top of your AI infrastructure and translates technical artifacts into actionable risk & compliance insights for product leaders, data scientists, and governance teams. Credo AI is an intelligence layer that sits on top of your technical and business infrastructure and translates technical artifacts into risk and compliance scores.

An AI governance platform designed by lawyers with regulatory expertise, tailored to your use cases and policies. Businesses must learn to navigate and comply with new legislation and guidelines. Organizations risk losing customer trust and a breakdown in product engagement if AI malfunctions. Teams must deal with increasingly complex AI systems, with more use cases than ever. Monitor compliance of your AI systems through our assessments and live model controls. Alert users to mitigate potential issues or risks. Implementing good AI governance practices can be time-consuming. Leverage built-in automation to import model data and artifacts, and review and update documentation. Understand AI compliance across your organization. Provide senior stakeholders with the full picture of their AI compliance to make strategic decisions and share reports for curated audiences. We offer a complete set of policies that ensure legal and regulatory compliance through pre-configured assessments.

Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.

AI Cloud is a new approach built for the demands, challenges and opportunities of AI today. A single system of record, accelerating the delivery of AI to production for every organization. All users collaborate in a unified environment built for continuous optimization across the entire AI lifecycle. The AI Catalog enables seamlessly finding, sharing, tagging, and reusing data, helping to speed time to production and increase collaboration. The catalog provides easy access to the data needed to answer a business problem while ensuring security, compliance, and consistency. If your database is protected by a network policy that only allows connections from specific IP addresses, contact Support for a list of addresses that an administrator must add to your network policy (whitelist).

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

SolasAI is software that detects and removes bias & discrimination from a customer's decisioning models. It works in credit & insurance underwriting, predictive marketing, healthcare, and employment to name a few use cases. We provide trust & transparency into artificial intelligence, machine learning, and standard statistical models. If you are tired of paying expensive experts who can't seem to agree, and then leave the hard part of fixing the problems to your expensive and over worked data scientists, then SolasAI is for you. We follow the latest decisions and signals from courts, regulators, and law makers, as well as the latest and greatest technology trends for AI and fairness as a whole. This is built into SolasAI so you don't have to figure it out yourself.

Take your data-driven enterprise to the next level with augmented intelligence. From algorithms to architecture, BasisAI manages the end to end development of real-time, scalable and responsible AI systems. Complete lifecycle AI solution: from bespoke AI algorithms, to production-grade AI application, and ongoing multi-year management. Rapid time to market: from your data to scalable, containerised deployment of real-time AI engine in weeks. No black boxes. Built-in AI governance, fairness and compliance. Retain control over your enterprise data, whether it is on AWS, GCP or another cloud infrastructure. We provide strategic guidance in building the right structures, frameworks and technologies, giving you a path to long-term scalability. Through exploratory use case sessions and system design workshops, we help you to move beyond experiments and algorithms and have control over your own capability development.

Creating responsible AI is a business problem, not just a tech problem. We solve for the whole problem by bringing teams together onto one platform to mitigate risk, leverage your full potential, and turn intention into action. Uniting every stage of your AI/ML journey with cloud-based governance applications. GovernML is the kickstarter you need to bring good AI/ML systems into the world. We bring user-friendly workflows that document the lifecycle of your AI journey on one platform. That’s good news for your risk mitigation and your bottom line. Monitaur provides cloud-based governance applications that track your AI/ML models from policy to proof. We are SOC 2 Type II-certified to enhance your AI governance and deliver bespoke solutions on a single unifying platform. GovernML brings responsible AI/ML systems into the world. Get scalable, user-friendly workflows that document the lifecycle of your AI journey on one platform.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.

Daria’s advanced automated features allow users to quickly and easily build predictive models, significantly cutting back on days and weeks of iterative work associated with the traditional machine learning process. Remove financial and technological barriers to build AI systems from scratch for enterprises. Streamline and expedite workflows by lifting weeks of iterative work through automated machine learning for data experts. Get hands-on experience in machine learning with an intuitive GUI for data science beginners. Daria provides various data transformation functions to conveniently construct multiple feature sets. Daria automatically explores through millions of possible combinations of algorithms, modeling techniques and hyperparameters to select the best predictive model. Predictive models built with Daria can be deployed straight to production with a single line of code via Daria’s RESTful API.

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

ValidMind is the most efficient solution for organizations to automate testing, documentation, and risk management for AI and statistical models. The ValidMind platform is a suite of tools helping data scientists, businesses, and risk/compliance stakeholders identify and document potential risks in their AI models, and ensure they deliver on expected regulatory outcomes. Our integrated platform makes it easy to review risk areas across all your teams' models and prioritize areas for compliance and risk mitigation. ValidMind enables organizations to break down information silos and reduce the complexity associated with sharing and collaborating on model documentation, validation reports, and risk findings through the model lifecycle.

FairNow equips organizations with all the AI governance tools they need to ensure global compliance and manage AI risk. Loved by CPOs, CAIOs, risk management, and legal professionals, FairNow's features are simplified, centralized, and empowering for the entire team. FairNow’s platform continuously monitors AI models to ensure that every model is fair, compliant, and audit-ready. Top features include: - Intelligent AI Risk Assessments: Conduct real-time assessments of AI models, using their deployment locations to highlight possible reputational, financial, and operational risks. - Hallucination Detection: Proactively detect errors and unexpected answers. - Automated Bias Evaluations: Automate bias evaluations and mitigate algorithmic bias as it happens. Plus: - AI Inventory - Centralized Policy Center - Roles and Controls FairNow’s AI governance platform helps organizations build, buy, and deploy AI with complete confidence.

Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease.

Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.

Harness the power of Qlik® Staige™ to make AI real by delivering a trusted data foundation, automation, actionable predictions, and company-wide impact. AI isn’t just experiments and initiatives — it’s an entire ecosystem of files, scripts, and results. Wherever your investments, we’ve partnered with top sources to bring you integrations that save time, enable management, and validate quality. Automate the delivery of real-time data into AWS data warehouses or data lakes, and make it easily accessible through a governed catalog. Through our new integration with Amazon Bedrock, you can easily connect to foundational large language models (LLMs) including A21 Labs, Amazon Titan, Anthropic, Cohere, and Meta. Seamless integration with Amazon Bedrock makes it easier for AWS customers to leverage large language models with analytics for AI-driven insights.

Do you know how safe your spreadsheets are? Do you know where all your spreadsheets are? Who is using them and if they present risk? If you do know the answers to all these questions, then you are doing well. Why, because most companies do not know! In fact, most don’t even have a plan to find and track the spreadsheets that can get them into trouble. So, how do you stay out of trouble? The answer is very simple. You use Apparity. Apparity is built to find every spreadsheet you use and then tell you what you should do. Should you put it in an inventory? Does it need to be controlled? Or can it be safely ignored? Keeping your company safe from costly mistakes is never easy. But Apparity was built to help you do just that. Apparity is easy to use for both managers and users. From the moment you first build your spreadsheet, to every time it is used and changed, Apparity will let you know when you have a problem. With Apparity your spreadsheets are safe, secure and tracked.

It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).

Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Quickly move between steps of the ML lifecycle to fine-tune your models. Replay training experiments, tune model features and other inputs, and compare results, without leaving SageMaker Studio.

Mind Foundry is an artificial intelligence company operating at the intersection of research, innovation, and usability to empower teams with AI that is built for humans. Founded by world-leading academics, Mind Foundry develops AI solutions that help organisations in the public and private sectors tackle high-stakes problems, focusing on human outcomes and the long-term impact of AI interventions. Our intrinsically collaborative platform powers AI design, testing and deployment and enables stakeholders to manage their AI investment responsibly with key focus on performance, efficiency and ethical impact. Built on a cornerstone of scientific principles and an understanding that you can’t add things like ethics and transparency after the fact. The fusion of experience design and quantitative methods that makes collaboration between humans and AI more intuitive, efficient and powerful.

Kraken is for everyone from analysts to data scientists. Built to be the easiest-to-use, no-code automated machine learning platform. The Kraken no-code automated machine learning (AutoML) platform simplifies and automates data science tasks like data prep, data cleaning, algorithm selection, model training, and model deployment. Kraken was built with analysts and engineers in mind. If you've done data analysis before, you're ready! Kraken's no-code, easy-to-use interface and integrated SONAR© training make it easy to become a citizen data scientist. Advanced features allow data scientists to work faster and more efficiently. Whether you use Excel or flat files for day-to-day reporting or just ad-hoc analysis and exports, drag-and-drop CSV upload and the Amazon S3 connector in Kraken make it easy to start building models with a few clicks. Data Connectors in Kraken allow you to connect to your favorite data warehouse, business intelligence tools, and cloud storage.

Refract is an enterprise AI platform that brings together the best AI frameworks and templates to prepare, build, train, and deploy high-quality Machine Learning (ML) models for users to experience a seamless and personalized “build-to-run” transition on their AI workflows. It reduces up to 70% of the effort for users by accelerating data science, AI, and ML life cycles through no-code automated features that significantly reduce time and effort on pre and post-model development steps (e.g. data provisioning & prep, model deployment, governance, monitoring, etc). Refract enables deep integration with Continuous Integration (CI)/Continuous Deployment (CD), which allows for touchless ML endpoint deployment on any cloud, multi-cloud, on-premises, or hybrid environment. It simplifies model management and is a one-stop-shop for batch, near real-time, and real-time deployment of your ML applications, while it also facilitates pre-built integration of ML apps with your business processes.

The Domino 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. 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 allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.

A data science platform that improves productivity with unparalleled abilities. Build and evaluate higher-quality machine learning (ML) models. Increase business flexibility by putting enterprise-trusted data to work quickly and support data-driven business objectives with easier deployment of ML models. Using cloud-based platforms to discover new business insights. Building a machine learning model is an iterative process. In this ebook, we break down the process and describe how machine learning models are built. Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results. Build high-quality models faster and easier. Automated machine learning capabilities rapidly examine the data and recommend the optimal data features and best algorithms. Additionally, automated machine learning tunes the model and explains the model’s results.

Apache PredictionIO® is an open-source machine learning server built on top of a state-of-the-art open-source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you quickly build and deploy an engine as a web service on production with customizable templates. Respond to dynamic queries in real-time once deployed as a web service, evaluate and tune multiple engine variants systematically, and unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics. Speed up machine learning modeling with systematic processes and pre-built evaluation measures, support machine learning and data processing libraries such as Spark MLLib and OpenNLP. Implement your own machine learning models and seamlessly incorporate them into your engine. Simplify data infrastructure management. Apache PredictionIO® can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Akka HTTP, etc.

Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place. As the leading data science platform for MLOps and model management, cnvrg.io is a pioneer in building cutting-edge machine learning development solutions so you can build high-impact machine learning models in half the time. Bridge science and engineering teams in a clear and collaborative machine learning management environment. Communicate and reproduce results with interactive workspaces, dashboards, dataset organization, experiment tracking and visualization, a model repository and more. Focus less on technical complexity and more on building high impact ML models. Cnvrg.io container-based infrastructure helps simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment.

Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale.

Qwak simplifies the productionization of machine learning models at scale. Qwak’s [ML Engineering Platform] empowers data science and ML engineering teams to enable the continuous productionization of models at scale. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate, and create competitive advantage. 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 c

Proven commercial value in weeks, for high value use cases across all major verticals. ElectrifAi has the largest library of pre-built machine learning models that seamlessly integrate into existing workflows to provide fast and reliable results. Get our domain expertise through pre-trained, pre-structured, or brand-new models. Building machine learning is risky and time-consuming. ElectrifAi delivers superior, fast and reliable results with over 1,000 ready-to-deploy machine learning models that seamlessly integrate into existing workflows. With comprehensive capabilities to deploy proven ML models, we bring you solutions faster. We make the machine learning models, complete the data ingestion and clean up the data. Our domain experts use your existing data to train the selected model that works best for your use case.

The Descartes Labs Platform is designed to answer some of the world’s most complex and pressing geospatial analytics questions. Our customers use the platform to build algorithms and models that transform their businesses quickly, efficiently, and cost-effectively. By giving data scientists and their line-of-business colleagues the best geospatial data and modeling tools in one package, we help turn AI into a core competency. Data science teams can use our scaling infrastructure to design models faster than ever, using our massive data archive or their own. Customers rely on our cloud-based platform to quickly and securely scale computer vision, statistical, and machine learning models to inform business decisions with powerful raster-based analytics. Our extensive API documentation, tutorials, guides and demos provide a deep knowledge base for users allowing them to quickly deploy high-value applications across diverse industries.

Leverage Kepler’s Automated Data Science Workflows and remove the need for coding and machine learning experience. Onboard quickly and generate data-driven insights unique to your organization and your data. Receive continuous updates & additional Workflows built by our world-class AI and ML team via our SaaS-based model. Scale AI and accelerate time-to-value with a platform that grows with your business using the team and skills already present within your organization. Address complex business problems with advanced AI and machine learning capabilities without the need for technical ML experience. Leverage state-of-the-art, end-to-end automation, an extensive library of AI algorithms, and the ability to quickly deploy machine learning models. Organizations are using Kepler to augment and automate critical business processes to improve productivity and agility.

Lentiq is a collaborative data lake as a service environment that’s built to enable small teams to do big things. Quickly run data science, machine learning and data analysis at scale in the cloud of your choice. With Lentiq, your teams can ingest data in real time and then process, clean and share it. From there, Lentiq makes it possible to build, train and share models internally. Simply put, data teams can collaborate with Lentiq and innovate with no restrictions. Data lakes are storage and processing environments, which provide ML, ETL, schema-on-read querying capabilities and so much more. Are you working on some data science magic? You definitely need a data lake. In the Post-Hadoop era, the big, centralized data lake is a thing of the past. With Lentiq, we use data pools, which are multi-cloud, interconnected mini-data lakes. They work together to give you a stable, secure and fast data science environment.

The Peltarion Platform is a low-code deep learning platform that allows you to build commercially viable AI-powered solutions, at speed and at scale. The platform allows you to build, tweak, fine-tune and deploy deep learning models. It is end-to-end, and lets you do everything from uploading data to building models and putting them into production. The Peltarion Platform and its precursor have been used to solve problems for organizations like NASA, Tesla, Dell, and Harvard. Build your own AI models or use our pre-trained ones. Just drag & drop, even the cutting-edge ones! Own the whole development process from building, training, tweaking to deploying AI. All under one hood. Operationalize AI and drive business value, with the help of our platform. Our Faster AI course is created for users who have no prior knowledge of AI. After completing seven short modules, users will be able to design and tweak their own AI models on the Peltarion platform.