Seldon Alternatives

The training data platform for AI teams. A machine learning model is only as good as its training data. Labelbox is an end-to-end platform to create and manage high-quality training data all in one place, while supporting your production pipeline with powerful APIs. Powerful image labeling tool for image classification, object detection and segmentation. When every pixel matters, you need accurate and intuitive image segmentation tools. Customize the tools to support your specific use case, including instances, custom attributes and much more. Performant video labeling editor for cutting-edge computer vision. Label directly on the video up to 30 FPS with frame level. Additionally, Labelbox provides per frame label feature analytics enabling you to create better models faster. Creating training data for natural language intelligence has never been easier. Label text strings, conversations, paragraphs, and documents with fast & customizable classification.

Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.

Datatron offers tools and features built from scratch, specifically to make machine learning in production work for you. Most teams discover that there’s more to just deploying models, which is already a very manual and time-consuming task. Datatron offers single model governance and management platform for all of your ML, AI, and Data Science models in production. We help you automate, optimize, and accelerate your ML models to ensure that they are running smoothly and efficiently in production. Data Scientists use a variety of frameworks to build the best models. We support anything you’d build a model with ( e.g. TensorFlow, H2O, Scikit-Learn, and SAS ). Explore models built and uploaded by your data science team, all from one centralized repository. Create a scalable model deployment in just a few clicks. Deploy models built using any language or framework. Make better decisions based on your model performance.

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.

Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.

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.

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.

Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.

Easily deploy, manage, monitor, and secure AI models in production. Modzy is the Enterprise AI platform designed to make it easy to scale trustworthy AI to your enterprise. Use Modzy to accelerate your deployment, management, and governance of trusted AI through the power of: Enterprise-grade platform features including security, APIs, and SDKs with unlimited model deployment, management, governance and monitoring at scale. Deployment options—your hardware, private, or public cloud. Includes AirGap deployments and tactical edge. Governance and auditing for centralized AI management, so you'll always have insight into AI models running in production in real-time. World's fastest Explainability (beta) solution for deep neural networks, creating audit logs to understand model predictions. Cutting-edge security features to block data poisoning and full-suite of patented Adversarial Defense to secure models running in production.

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.

Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.

Use our platform to build AI products, train, fine tune and deploy models on the cloud without worrying about infrastructure, cost management, scaling, and other technical headaches. Train, fine tune and deploy models with prebuilt, fully customizable, modular components. Focus on the science and not the engineering. A Lightning component organizes code to run on the cloud, manage its own infrastructure, cloud costs, and more. 50+ optimizations to lower cloud costs and deliver AI in weeks not months. Get enterprise-grade control with consumer-level simplicity to optimize performance, reduce cost, and lower risk. Go beyond a demo. Launch the next GPT startup, diffusion startup, or cloud SaaS ML service in days not months.

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.

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.

Powerful, self-serve machine learning platform where you can turn models into scalable APIs in just a few clicks. Sign up for Deep Infra account using GitHub or log in using GitHub. Choose among hundreds of the most popular ML models. Use a simple rest API to call your model. Deploy models to production faster and cheaper with our serverless GPUs than developing the infrastructure yourself. We have different pricing models depending on the model used. Some of our language models offer per-token pricing. Most other models are billed for inference execution time. With this pricing model, you only pay for what you use. There are no long-term contracts or upfront costs, and you can easily scale up and down as your business needs change. All models run on A100 GPUs, optimized for inference performance and low latency. Our system will automatically scale the model based on your needs.

The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications.

Censius is an innovative startup in the machine learning and AI space. We bring AI observability to enterprise ML teams. Ensuring that ML models' performance is in check is imperative with the extensive use of machine learning models. Censius is an AI Observability Platform that helps organizations of all scales confidently make their machine-learning models work in production. The company launched its flagship AI observability platform that helps bring accountability and explainability to data science projects. A comprehensive ML monitoring solution helps proactively monitor entire ML pipelines to detect and fix ML issues such as drift, skew, data integrity, and data quality issues. Upon integrating Censius, you can: 1. Monitor and log the necessary model vitals 2. Reduce time-to-recover by detecting issues precisely 3. Explain issues and recovery strategies to stakeholders 4. Explain model decisions 5. Reduce downtime for end-users 6. Build customer trust

Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.

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.

Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.

Alegion is the data labeling solution for enterprise-grade Machine Learning. We lead the industry in streaming, high-resolution, high-density video annotation, delivering accurately-annotated, model-ready data to train and validate ML models. Alegion provides both the platform and workforce to operate with quality at scale, processing structured and unstructured data including video, image, audio, and text. Our ML powered platform speeds up task completion by as much as 70%, including classless object tracking and single click smart polygon generation. Segmentation options include Keypoint, Bounding Box, Polyline, & Polygon segmentation, for image and video. Semantic Segmentation tools deliver seamless entity boundaries with pixel perfect accuracy. NLP and NER capabilities support text and audio classification and sentiment analysis. The platform is highly configurable to support hybrid use cases. Available via SaaS (Alegion Control), Managed Platform, and Managed Labeling Services.

You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.

Training-ready platform with NVIDIA® H100 Tensor Core GPUs. Competitive pricing. Dedicated support. Built for large-scale ML workloads: Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tb/s per host. Best value for money: Save at least 50% on your GPU compute compared to major public cloud providers*. Save even more with reserves and volumes of GPUs. Onboarding assistance: We guarantee a dedicated engineer support to ensure seamless platform adoption. Get your infrastructure optimized and k8s deployed. Fully managed Kubernetes: Simplify the deployment, scaling and management of ML frameworks on Kubernetes and use Managed Kubernetes for multi-node GPU training. Marketplace with ML frameworks: Explore our Marketplace with its ML-focused libraries, applications, frameworks and tools to streamline your model training. Easy to use. We provide all our new users with a 1-month trial period.

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.

It is the artificial intelligence solution to analyze complex data, empower decision making and transform human and industrial productivity. It is the automated machine solution that has the potential to reduce the burden on already overwhelmed teams by automating the main bottlenecks in the data science process. It breaks data silos with the intuitive creation of data pipelines that feed live data into deployed models and then dynamically creates model execution pipelines to obtain real-time predictions on incoming data. It monitors the health of deployed models based on the confidence of predictions to inform model maintenance.

Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.

ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.

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.

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.

Neuton, a no-code AutoML solution, makes Machine Learning available to everyone. Explore data insights and make predictions leveraging Automated Artificial Intelligence. • NO coding • NO need for technical skills • NO need for data science knowledge Neuton provides comprehensive Explainability Office©, a unique set of tools that allow users to evaluate model quality at every stage, identify the logic behind the model analysis, understand why certain predictions have been made. • Exploratory Data Analysis • Feature Importance Matrix with class granularity • Model Interpreter • Feature Influence Matrix • Model-to-Data Relevance Indicators historical and for every prediction • Model Quality Index • Confidence Interval • Extensive list of supported metrics with Radar Diagram Neuton enables users to implement ML in days instead of months.

Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure.

Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.

Segmind provides simplified access to large computing. You can use it to run your high-performance workloads such as Deep learning training or other complex processing jobs. Segmind offers zero-setup environments within minutes and lets your share access with your team members. Segmind's MLOps platform can also be used to manage deep learning projects end-to-end with integrated data storage and experiment tracking. ML engineers are not cloud engineers and cloud infrastructure management is a pain. So, we abstracted away all of it so that your ML team can focus on what they do best, and build models better and faster. Training ML/DL models take time and can get expensive quickly. But with Segmind, you can scale up your compute seamlessly while also reducing your costs by up to 70%, with our managed spot instances. ML managers today don't have a bird's eye view of ML development activities and cost.

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.

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.

At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine.

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).

Build, deploy, and operate Enterprise AI applications. The C3 AI® Suite uses a unique model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise AI applications. The C3 AI model-driven architecture provides an “abstraction layer,” that allows developers to build enterprise AI applications by using conceptual models of all the elements an application requires, instead of writing lengthy code. This provides significant benefits: Use AI applications and models that optimize processes for every product, asset, customer, or transaction across all regions and businesses. Deploy AI applications and see results in 1-2 quarters – rapidly roll out additional applications and new capabilities. Unlock sustained value – hundreds of millions to billions of dollars per year – from reduced costs, increased revenue, and higher margins. Ensure systematic, enterprise-wide governance of AI with C3.ai’s unified platform that offers data lineage and governance.

HumanFirst provides radically efficient infrastructure and workflows to explore, curate and scale your AI training data. We productize the capabilities to scale NLU & NLP. HumanFirst accelerates conversational AI. Real voice-of-the-customer data, always in the loop. HumanFirst Studio makes working with voice and text data to train and maintain natural language understanding NLU models a breeze. Replace costly, and ad-hoc data ingestion and labeling processes with a seamless experience that allows you to build and improve the performance of your AI with real data, immediately. Import your Search and Help Center requests, emails, live chat or voice call logs to continuously train AI that understands your customers. Discover intents and improve their accuracy. Guessing what intents you need to train, and coming up with training phrases manually is painful - and often leads to low coverage and accuracy of intents.

HPE Ezmeral ML Ops provides pre-packaged tools to operationalize machine learning workflows at every stage of the ML lifecycle, from pilot to production, giving you DevOps-like speed and agility. Quickly spin-up environments with your preferred data science tools to explore a variety of enterprise data sources and simultaneously experiment with multiple machine learning or deep learning frameworks to pick the best fit model for the business problems you need to address. Self-service, on-demand environments for development and test or production workloads. Highly performant training environments—with separation of compute and storage—that securely access shared enterprise data sources in on-premises or cloud-based storage. HPE Ezmeral ML Ops enables source control with out of the box integration tools such as GitHub. Store multiple models (multiple versions with metadata) for various runtime engines in the model registry.

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.

Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale. Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. And its custom high-speed network offers over 100 petaflops of performance in a single pod, enough computational power to transform your business or create the next research breakthrough. Training machine learning models is like compiling code: you need to update often, and you want to do so as efficiently as possible. ML models need to be trained over and over as apps are built, deployed, and refined.

Superb AI provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows, saving time and money. Majority of ML teams spend more than 50% of their time managing training datasets Superb AI can help. On average, our customers have reduced the time it takes to start training models by 80%. Fully managed workforce, powerful labeling tools, training data quality control, pre-trained model predictions, advanced auto-labeling, filter and search your datasets, data source integration, robust developer tools, ML workflow integrations, and much more. Training data management just got easier with Superb AI. Superb AI offers enterprise-level features for every layer in an ML organization.

Innovation is our nucleus. Cogito shoulders AI enterprises and business initiatives by deploying a proficient workforce for data annotation, content moderation and any other data processing services. Our data enrichment services provide one-stop solutions for all your data-related needs. Our scalable, immensely experienced, brilliant minds unite their knowledge to meet your requirements swiftly with precise accuracy while maintaining full data security and confidentiality. We specializes in Human Empowered Automation. Our mission is to help our customers innovate and scale by solving their day-to-day data needs. Using our skilled on-demand workforce, we partner with Artificial Intelligence, Technology and eCommerce clients to develop high-quality data sets used to build and enhance various cutting-edge business applications. Delivering cost-effective, highly accurate, completely scalable, and secure data enrichment solutions for Businesses and AI Enterprises.

Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industry’s first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster. Hopsworks can ingest data from the datasources you use. Whether they are in the cloud, on‑premise, IoT networks, or from your Industry 4.0-solution. Deploy on‑premises on your own hardware or at your preferred cloud provider. Hopsworks will provide the same user experience in the cloud or in the most secure of air‑gapped deployments. Learn how to set up customized alerts in Hopsworks for different events that are triggered as part of the ingestion pipeline.

PredictSense is an end-to-end Machine Learning platform powered by AutoML to create AI-powered analytical solutions. Fuel the new technological revolution of tomorrow by accelerating machine intelligence. AI is key to unlocking value from enterprise data investments. PredictSense enables businesses to monetize critical data infrastructure and technology investments by creating AI driven advanced analytical solutions rapidly. Empower data science and business teams with advanced capabilities to quickly build and deploy robust technology solutions at scale. Easily integrate AI into the current product ecosystem and fast track GTM for new AI solutions. Incur huge savings in cost, time and effort by building complex ML models in AutoML. PredictSense democratizes AI for every individual in the organization and creates a simple, user-friendly collaboration platform to seamlessly manage critical ML deployments.

Seamless machine learning model management, deployment, and monitoring for supercharging MLOps for any organization on the best AI platform. Use navio to perform various machine learning operations across an organization's entire artificial intelligence landscape. Take your experiments out of the lab and into production, and integrate machine learning into your workflow for a real, measurable business impact. navio provides various Machine Learning operations (MLOps) to support you during the model development process all the way to running your model in production. Automatically create REST endpoints and keep track of the machines or clients that are interacting with your model. Focus on exploration and training your models to obtain the best possible result and stop wasting time and resources on setting up infrastructure and other peripheral features. Let navio handle all aspects of the product ionization process to go live quickly with your machine learning models.

Semantria is a natural language processing (NLP) API from Lexalytics, leaders in enterprise sentiment analysis and text analytics since 2004. Semantria offers multi-layered sentiment analysis, categorization, entity recognition, theme analysis, intention detection and summarization in an easy-to-integrate RESTful API package. Semantria is totally customizable through graphical configuration tools, supports 24 languages, and can be deployed across private, public and hybrid clouds. Semantria scales effortlessly from single servers to entire data centers and back again to meet your on-demand processing needs. Integrate Semantria to add powerful, flexible text analytics and natural language processing capabilities to your cloud-based data analytics products or enterprise business intelligence infrastructure. Or add Lexalytics storage and visualization tools to create a complete business intelligence platform for storing, managing, analyzing and visualizing text documents.

Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

End-to-end project, model and hosting management platform, which allows companies to convert data and algorithms into holistic, execution-ready AI-strategies. Build, train and manage models securely with ease. Create products that consume AI models from anywhere, any time. Minimize risks of AI investments, while increasing strategic flexibility. Completely automated model testing, evaluation deployment, scaling and hardware load balancing. From real-time, low-latency, high-throughput inference to batch, long-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. Intuitive interface that acts as a unified hub for managing projects, creating and visualizing datasets, and training models via collaborative and reproducible workflows.