Alternatives to Peltarion

Fraud.net is the world's leading infrastructure for fraud management. It is powered by a sophisticated collective Intelligence network, world-class AI, and a modern cloud-based platform that assists you: * Combine fraud data from all sources with one connection * Detect fraudulent activity in real-time for transactions exceeding 99.5% * Uncover hidden insights in Terabytes of data to optimize fraud management Fraud.net was recognized in Gartner's market guide for online fraud detection. It is a real-time enterprise-strength, enterprise-strength, fraud prevention and analytics solution that is tailored to the needs of its business customers. It acts as a single point-of-command, combining data from different sources and systems, tracking digital identities and behaviors, then deploying the most recent tools and technologies to eradicate fraudulent activity and allow transactions to go through. Get a free trial by contacting us today

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

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.

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

Build and deploy deep-learning models for custom use scenarios. Our automated machine-learning process allows customers create powerful AI solutions based on our best-in class models and tailored to their specific challenges. Digital platforms can quickly create custom models that fit their guidelines and requirements. Build large language models to support specialized use cases, such as bots for customer and technical service. Create image classification models for better understanding image libraries, including search, organization and more.

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

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.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

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.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

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

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

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.

Produvia is a serverless machine-learning development service. Partner with Produvia for machine model development and deployment using serverless cloud infrastructure. Produvia partners with Fortune 500 companies and Global 500 businesses to develop and deploy machine-learning models using modern cloud infrastructure. Produvia uses state-of-the art methods in machine learning and deep-learning technologies to solve business problems. Overspending on infrastructure costs can lead to organizations. Modern organizations employ serverless architectures to lower server costs. Complex servers and legacy code can hold back organizations. Machine learning technologies are used by modern organizations to rewrite technology stacks. Software developers are hired by companies to write code. Machine learning is used to create software that codes in modern companies.

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

Exafunction optimizes deep learning inference workloads, up to a 10% improvement in resource utilization and cost. Instead of worrying about cluster management and fine-tuning performance, focus on building your deep-learning application. Poor utilization of GPU hardware is a common problem in deep learning applications. Exafunction allows any GPU code to be moved to remote resources. This includes spot instances. Your core logic is still an inexpensive CPU instance. Exafunction has been proven to be effective in large-scale autonomous vehicle simulation. These workloads require complex custom models, high numerical reproducibility, and thousands of GPUs simultaneously. Exafunction supports models of major deep learning frameworks. Versioning models and dependencies, such as custom operators, allows you to be certain you are getting the correct results.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

Neuralhub is an AI system that simplifies the creation, experimentation, and innovation of neural networks. It helps AI enthusiasts, researchers, engineers, and other AI professionals. Our mission goes beyond just providing tools. We're creating a community where people can share and collaborate. We want to simplify deep learning by bringing together all the tools, models, and research into a collaborative space. This will make AI research, development, and learning more accessible. Create a neural network by starting from scratch, or use our library to experiment and create something new. Construct your neural networks with just one click. Visualize and interact with each component of the network. Tune hyperparameters like epochs and features, labels, and more.

The Intel®, Deep Learning SDK is a collection of tools that allows data scientists and software developers alike to create, train, and then deploy deep learning solutions. The SDK includes a training tool as well as a deployment tool. These tools can be used together or separately to create a complete deep-learning workflow. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and conduct experiments. It is easy to install and use popular deep learning frameworks that are optimized for Intel®. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and prepare training data. It makes it easier to install and use popular deep learning frameworks that are optimized for Intel®. The web interface features an easy-to-use wizard for creating deep learning models. There are also tooltips to help you navigate the process.

Metacoder makes data processing faster and more efficient. Metacoder provides data analysts with the flexibility and tools they need to make data analysis easier. Metacoder automates data preparation steps like cleaning, reducing the time it takes to inspect your data before you can get up and running. It is a good company when compared to other companies. Metacoder is cheaper than similar companies and our management is actively developing based upon our valued customers' feedback. Metacoder is primarily used to support predictive analytics professionals in their work. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We make it easy to manage the machine learning pipeline and help organizations share their work. Soon, we will offer code-free solutions for image, audio and video as well as biomedical data.

Interplay Platform is a patented low-code platform with 475 pre-built Enterprises, AI, IoT drag-and-drop components. Interplay helps large organizations innovate faster. It's used as middleware and as a rapid app building platform by big companies like Circle K, Ulta Beauty, and many others. As middleware, it operates Pay-by-Plate (frictionless payments at the gas pump) in Europe, Weapons Detection (to predict robberies), AI-based Chat, online personalization tools, low price guarantee tools, computer vision applications such as damage estimation, and much more.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

RZT aiOS provides all the benefits of a unified AI platform, and more. It's not just a platform, it's an Operating System that connects, manages, and unifies all your AI initiatives. AI developers can now do what used to take months in days thanks to aiOS process management which dramatically increases their productivity. This Operating System provides an intuitive environment for AI development. It allows you to visually build models, explore data and create processing pipelines. You can also run experiments and view analytics. It's easy to do all of this without any advanced software engineering skills.

A class-agnostic, pixel-perfect automated annotation platform. Built for teams that have a lot of data and strict quality requirements but little time. Ground truth creation can be scaled up 10x. Collaborate with unlimited team members, annotators and seamlessly integrate into your deep learning pipeline. Create ground truth 10x faster with pixel-perfect annotations. Use V7's intuitive tools for labeling data and automating your ML pipelines. The ultimate image and Video Annotation Solution

Deci's deep learning platform powered by Neural architecture Search allows you to quickly build, optimize, deploy, and deploy accurate models. You can instantly achieve accuracy and runtime performance that is superior to SoTA models in any use case or inference hardware. Automated tools make it easier to reach production. No more endless iterations or dozens of libraries. Allow new use cases for resource-constrained devices and cut down on your cloud computing costs by up to 80% Deci's NAS-based AutoNAC engine automatically finds the most appropriate architectures for your application, hardware, and performance goals. Automately compile and quantify your models using the best of breed compilers. Also, quickly evaluate different production settings.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

Our MIND platform will help you solve your problem. We then train your staff to maintain the solution, and re-initialize the underlying models if necessary. Our products and services are used by businesses in the industrial, medical, consumer service, and consumer service industries to automate processes that were previously only possible with human intervention. Quality assurance in the food industry. Counting and classifying cells in biomedicine. Analyzing gaming performance. Measuring geometrical characteristics (position, size, profile, distance, angle. Tracking objects in agriculture. Time series analysis in sport and healthcare. Our MInD platform allows you to build AI solutions for your business. It provides all the tools you need to develop deep learning solutions in each of the five stages.

We work closely with companies to identify the issues that prevent them from realising their potential. We create AI platforms that allow corporations to take full control and empowerment of their data. Adopting AI platforms at a reasonable cost will help you to protect your data and ensure that your privacy is protected. Enhance efficiency in businesses and increase the quality of the work done by humans. AI is used to automate repetitive or dangerous tasks. It also bypasses human intervention. This allows for faster tasks that are creative and compassionate. Machine Learning allows applications to easily provide predictive capabilities. It can create regression and classification models. It can also visualize and do clustering. It supports multiple ML libraries, including Scikit-Learn and Tensorflow. It contains around 22 algorithms for building classifications, regressions and clustering models.

SynapseAI, like our accelerator hardware, is designed to optimize deep learning performance and efficiency, but most importantly, for developers, it is also easy to use. SynapseAI's goal is to make it easier and faster for developers by supporting popular frameworks and model. SynapseAI, with its tools and support, is designed to meet deep-learning developers where they are -- allowing them to develop what and in the way they want. Habana-based processors for deep learning preserve software investments and make it simple to build new models. This is true both for training and deployment.

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

ConvNetJS is a Javascript library that allows you to train deep learning models (neural network) in your browser. You can train by simply opening a tab. No software requirements, no compilers, no installations, no GPUs, no sweat. The library was originally created by @karpathy and allows you to create and solve neural networks using Javascript. The library has been greatly expanded by the community, and new contributions are welcome. If you don't want to develop, this link to convnet.min.js will allow you to download the library as a plug-and play. You can also download the latest version of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create an index.html file with no content and copy build/convnet.min.js to that folder.

Amazon EC2 instances P4d deliver high performance in cloud computing for machine learning applications and high-performance computing. They offer 400 Gbps networking and are powered by NVIDIA Tensor Core GPUs. P4d instances offer up to 60% less cost for training ML models. They also provide 2.5x better performance compared to the previous generation P3 and P3dn instance. P4d instances are deployed in Amazon EC2 UltraClusters which combine high-performance computing with networking and storage. Users can scale from a few NVIDIA GPUs to thousands, depending on their project requirements. Researchers, data scientists and developers can use P4d instances to build ML models to be used in a variety of applications, including natural language processing, object classification and detection, recommendation engines, and HPC applications.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

Amazon Elastic Compute Cloud's (Amazon EC2) instances P5 powered by NVIDIA Tensor core GPUs and P5e or P5en instances powered NVIDIA Tensor core GPUs provide the best performance in Amazon EC2 when it comes to deep learning and high-performance applications. They can help you accelerate the time to solution up to four times compared to older GPU-based EC2 instance generation, and reduce costs to train ML models up to forty percent. These instances allow you to iterate faster on your solutions and get them to market quicker. You can use P5,P5e,and P5en instances to train and deploy increasingly complex large language and diffusion models that power the most demanding generative artificial intelligent applications. These applications include speech recognition, video and image creation, code generation and question answering. These instances can be used to deploy HPC applications for pharmaceutical discovery.

Cauliflower can process feedback and comments for any type of service or product. Cauliflower uses Artificial Intelligence (AI) to identify the most important topics, evaluate them, and establish relationships. Machine learning models in-house developed for extracting content and evaluating sentiment. Intuitive dashboards that offer filter options and drill-downs. You can use included variables to indicate language, weight, ID and time. In the dropdown, you can define your own filter variables. Cauliflower can translate the results into a common language if necessary. Instead of reading customer feedback sporadically and quoting individual opinions, define a company-wide language.

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

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

FARO Sphere XG, a cloud-based digital platform for digital reality, provides users with a centralized collaborative experience across all of the company's 3D modeling and reality capture applications. Sphere XG, when paired with Stream, enables faster 3D data collection, processing, and project management anywhere in the world. Sphere XG is a systematized tool that allows users to organize 3D scans, 360-degree photos and 3D models. It also allows them to manage data from different teams around the globe. Sphere XG allows you to view and share 3D point clouds, 360-degree photo documentation, and floorplans all in one place. You can also track the progress of your project over time. Ideal for 4D progress management, where the ability of comparing elements over time is crucial, project managers and VDC manager can better democratize the data and eliminate the necessity to use two platforms.

H2O.ai, the open-source leader in AI and machinelearning, has a mission to democratize AI. Our enterprise-ready platforms, which are industry-leading, are used by thousands of data scientists from over 20,000 organizations worldwide. Every company can become an AI company in financial, insurance, healthcare and retail. We also empower them to deliver real value and transform businesses.