Alternatives to Reality AI Tools

SparkCognition's analytics platform, SparkPredict, is changing maintenance. It reduces downtime and saves millions in operating costs. SparkPredict is a turnkey solution which analyzes sensor data and uses machine-learning to provide actionable insights. It flags suboptimal operations and warns of impending failures. Predictive AI analytics can help you protect your assets and keep them online. With insights that help repair, labor efficiency can be increased during downtime. Machine learning that codifies human knowledge helps you retain the knowledge of your workforce. Increase asset failure horizons and predict more machine problems with less effort. Quick, informed repairs with clearly defined failure indicators are possible. Automatic model retraining improves models over time and maintains predictive accuracy.

This field, cloud, and office software suite can be combined with our state of the art instruments and machine control to create and access the right data at the right time. Our field software is easy to use and understand, yet powerful enough to solve even the most difficult positioning challenges. This office companion to our field solutions generates 3D models and manages project data to improve site management. Streamlined earthworks and paving workflow applications. Takeoff, 3D model and survey software. 3D constructible model, survey software. Processing of survey data and delivery to the field. Site layout file preparation. Processing and adjustments to raw survey data. Cloud platform to share and publish 3D reality data. Combine, analyze and process 3D point clouds from multiple sensors. Machine control file type conversion.

Amazon SageMaker Feature Store can be used to store, share and manage features for machine-learning (ML) models. Features are inputs to machine learning models that are used for training and inference. In an example, features might include song ratings, listening time, and listener demographics. Multiple teams may use the same features repeatedly, so it is important to ensure that the feature quality is high-quality. It can be difficult to keep the feature stores synchronized when features are used to train models offline in batches. SageMaker Feature Store is a secure and unified place for feature use throughout the ML lifecycle. To encourage feature reuse across ML applications, you can store, share, and manage ML-model features for training and inference. Any data source, streaming or batch, can be used to import features, such as application logs and service logs, clickstreams and sensors, etc.

Arena empowers businesses from all industries to take high-frequency, critical path decision fully autonomously. Autopilot for high-frequency business decision making. Autonomy OS, which is similar to a physical robot's sensor, brain and arm, is made up of three components: the sensor, the head, and the arm. The sensor measures, while the brain makes decisions and the arm takes actions. The entire system works in real-time and automatically. Autonomy OS encodes heterogeneous data using different latency profiles. This includes streaming real-time and structured data series, as well as unstructured data such images and text. These data are used to train machine learning models. Autonomy OS also adds contextual data from Arena's demand graph, a daily updated index of factors that influence consumer demand and supply. This includes product prices, availability by location, and demand proxies from various social media platforms. Customers' preferences and behavior change, supply routes are unexpectedly disrupted and competitors alter their strategy.

Predictive modeling with Machine Learning and Explainable Ai. FICO®, Analytics Workbench™, is a comprehensive suite of state-of the-art analytic authoring software that empowers companies to make better business decisions throughout the customer lifecycle. Data scientists can use it to build superior decisioning abilities using a variety of predictive data modeling tools, including the most recent machine learning (ML), and explainable AI (xAI) methods. FICO's innovative intellectual property enables us to combine the best of open-source data science and machine learning to provide world-class analytical capabilities to find, combine, and operationalize data predictive signals. Analytics Workbench is built upon the FICO®, leading platform that allows for new predictive models and strategies to easily be put into production.

NeuReality accelerates AI's possibilities by offering a revolutionary AI solution that reduces complexity, cost and power consumption. Other companies develop Deep Learning Accelerators for deployment. However, no company has a software platform that is specifically designed to manage specific hardware infrastructure. NeuReality is a unique company that bridges a gap between infrastructure where AI inference runs, and the MLOps eco-system. NeuReality developed a new architecture to maximize the power of DLAs. This architecture allows inference via hardware using AI-over fabric, an AI hypervisor and AI-pipeline-offload.

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.

Our intelligent automation SaaS creates digital twins for your industrial assets at the push of a single button, saving you up to 90% on integration costs. AI allows you to create a digital replica of your technical assets with just one click. Create virtual replicas in real-time of physical assets, processes and even entire factories. Run simulations in order to practice different scenarios before they are implemented in reality. Make data-driven decisions, optimize production lines, and predict potential issues. Collect and analyze sensor and machinery data to gain valuable insights. Blueprints allow customers to connect machines in a matter of seconds, allowing them to collect and transmit data. This improves decision-making and operational efficiency while optimizing sustainability. The blueprint technology promotes plug-and-produce interoperability and supports plug-and produce integrations between machine manufacturers, and providers of industrial software.

Amazon Elastic Inference allows for low-cost GPU-powered acceleration to Amazon EC2 instances and Sagemaker instances, or Amazon ECS tasks. This can reduce the cost of deep learning inference by up 75%. Amazon Elastic Inference supports TensorFlow and Apache MXNet models. Inference is the process by which a trained model makes predictions. Inference can account for as much as 90% of total operational expenses in deep learning applications for two reasons. First, standalone GPU instances are usually used for model training and not inference. Inference jobs typically process one input at a time and use a smaller amount of GPU compute. Training jobs can process hundreds of data samples simultaneously, but inference jobs only process one input in real-time. This makes standalone GPU-based inference expensive. However, standalone CPU instances aren't specialized for matrix operations and are therefore often too slow to perform deep learning inference.

Agri-SCM is easy to use and does not require any training. Our user-friendly interface allows anyone to use this solution immediately. Agri-SCM offers a variety of ways to collect data, including voice recording, photo and video capture, sensor data collection in real-time, a friendly select box, and more. Sensors were integrated into an IoT system to create a live stream of input data. All data on farming conditions will be sent automatically for analysis and prediction models. We are using Data Science, machine learning and artificial intelligence to provide users with valuable reports about the farm.

ClearPredictions gives organizations great opportunities to gain competitive advantage through their data. ClearPredictions delivers actionable customer behavior predictions using powerful but easy-to-use machine learning technology. Machine learning algorithms that are scientifically tested and proven without the need to be developed by data scientists. Reduce costs and increase growth with minimal implementation and maintenance costs. Predictive insights and lightning fast actionable predictions are available within days. Predictive capabilities don't need to be expensive. Our goal is to help businesses harness the power of machine-learning and improve decision-making. Our software and services allow you to quickly build predictive models using very little data science knowledge.

Censius, an innovative startup in machine learning and AI, is a pioneering company. We provide AI observability for enterprise ML teams. With the extensive use machine learning models, it is essential to ensure that ML models perform well. Censius, an AI Observability platform, helps organizations of all sizes to make their machine-learning models in production. The company's flagship AI observability platform, Censius, was launched to help bring accountability and explanation to data science projects. Comprehensive ML monitoring solutions can be used to monitor all ML pipelines and detect and fix ML problems such as drift, skew and data integrity. After integrating Censius you will be able to: 1. Keep track of the model vitals and log them 2. By detecting problems accurately, you can reduce the time it takes to recover. 3. Stakeholders should be able to understand the issues and recovery strategies. 4. Explain model decisions 5. Reduce downtime for end-users 6. Building customer trust

Skylight provides high-performance network monitoring and user experience monitoring for any application, cloud, or network. Skylight is a virtualized performance platform that provides end-to-end visibility into network, application and service performance, from the user edge to core network and cloud. Skylight helps to ensure that networks and cloud applications meet increasing performance requirements, optimize network capacities, and meet customer expectations regarding quality of experience. Skylight uses its high-quality performance data and machine learning to predict, predict, and prevent customer-impacting problems. Skylight sensors allow you to collect precise network, service, and application data. Skylight sensors are available in both software and hardware. This allows you to place Skylight sensors capabilities anywhere in your network, from remote locations to cloud-based locations and everywhere in between.

hIOTron's machine-monitoring product allows you to monitor and analyze industrial machines in real time. This product allows you to track machine performance, detect anomalies and predict failures and schedule maintenance activities to increase uptime and decrease downtime. The machine monitoring product is made up of a variety of sensors and devices that collect data about your machines. This includes temperature, pressure, vibration, as well as other operational parameters. The data is then sent to a central hub, where it is analyzed with advanced analytics and machine learning algorithms to find patterns and trends. The hIOTron platform offers a user-friendly dashboard which displays real-time machine data and key performance indicators. It also offers analytics and reports to help you understand the machine performance and identify areas that need improvement.

It's easy to build advanced predictive models with no math or coding in just a few clicks. The Complete Experience We provide everything you need, from cleaning data to building models to forecasting, and everything in between. Trustworthy. Learn the "why" behind AI decisions to drive business with actionable insight. Data Analytics in minutes without having to write code. In one click, you can complete the entire process of building ML algorithms, explaining results and predicting future outcomes. Machine Learning in 3 Steps You can go from raw data to actionable insights without writing a single line code. Upload your data. Ask questions in plain English Find the best model for your data. Share your results. Increase customer productivity We assist companies to use no code Machine Learning to improve their Customer Experience.

LidarView, an open-source platform by Kitware, is designed for the real-time recording, visualization, and processing 3D LiDAR data. It is built on ParaView and renders large point cloud data efficiently. It also offers features like 3D visualization of timestamp-stamped LiDAR return, a spreadsheet inspector to inspect attributes such as azimuth and timestamp, and the capability to display multiple dataframes simultaneously. Users can input data directly from live sensor streams, recorded.pcaps or manage subsets in laser data. LidarView supports a variety of sensors, including models by Velodyne Hesai Robosense Livox and Leishen. It allows for the visualization of live streams as well as replaying recorded data. The platform integrates advanced algorithms to facilitate accurate environmental reconstruction and sensor location. It also incorporates AI capabilities and machine learning for scene classification.

QuantaVerse's Financial Crime Investigation Platform utilizes RPA, AI and machine learning to automate data collection, identify financial crime, and document findings. Our solutions have been proven to improve AML case adjudication, reporting efficiency, and overall effectiveness. Validated analytics and comprehensive data gathering deliver the consistent findings you and regulators need. Your investigation team will be more efficient if false positives are eliminated before they are generated. Additionally, automation of data collection and analysis can cut down on investigation time by up to 70%. Innovative solutions that provide the regulators with clear, transparent and fully-explainable results will ensure consistency in your AML/BSA program. Find out the risks in your current system and drive terrorists and criminals out of your institution.

Your engineers can use digital technology to predict and prevent asset failures. Increase machine availability and asset reliability. Facilitate proactive maintenance and PaaS offerings. Reduce repair and break-fix costs. Optimize resource allocation. Enhance customer service and quality. Your customer's success is dependent on machine availability. Traditional break-fix service models can't keep up with asset downtime that can cause millions of dollars in lost output. It is essential to invest in technology that can not only collect IoT sensor data, but also run analytics on it to detect anomalies or prognose failures. This solution will enable you to increase equipment availability and provide service excellence through intelligent repairs.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

Advanced embedded machine learning applications can be built without a PhD. To create custom datasets, collect sensor, audio, and camera data directly from devices, files or cloud integrations. Automated labeling tools, from object detection to audio segmentation, are available. Our cloud infrastructure allows you to set up and execute reusable scripted tasks that transform large amounts of input data. Integrate custom data sources, CI/CD tool, and deployment pipelines using open APIs. With ready-to-use DSPs and ML algorithms, you can accelerate the development of custom ML pipelines. Every step of the process, hardware decisions are made based on flash/RAM and device performance. Keras APIs allow you to customize DSP feature extraction algorithms. You can also create custom machine learning models. Visualized insights on model performance, memory, and datasets can fine-tune your production model. Find the right balance between DSP configurations and model architecture. All this is budgeted against memory constraints and latency constraints.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

It allows you to design and test circuits in a flexible way, allowing you to try all "what if" scenarios. You don't have to worry about faulty components or bad connections. CircuitLogix supports mixed-signal, analog, and digital circuits. Its proven SPICE simulation provides accurate results that you can trust. 3DLab, which is included in both versions of CircuitLogix, is a virtual reality lab environment that is designed to closely mimic the appearance and functionality actual devices and instruments. 3DLab includes approximately 30 different devices, including switches, meters and lamps, as well as resistors, capacitors, inductors and fuses.

Infor Internet of Things, (IoT), provides secure sensor data ingestion at scale from anywhere. Infor IoT synchronizes sensor data with Infor CloudSuite™, EAM information, providing deeper context for asset health monitoring. This contextual information is used to improve exception detection, workflows and reporting, as well as machine learning and data analytics. The Infor Data Lake stores the sensor readings of Infor IoT, which allows for richer IoT data consumption. You can also augment device data with metadata and attributes that allow advanced search and device management capabilities. The rule engine allows you to monitor telemetry in real-time, create device templates to simplify device configuration, and leverage a flexible condition editor and action rule editor.

Polar Signals cloud provides continuous profiling that is always on, zero-instrumentation, and helps to improve performance, reduce infrastructure costs, and understand incidents. You can optimize performance and save costs in your infrastructure with just one command. You can go back in time and pinpoint incidents or issues. Profiling data can provide unique insights and depth about how a process has performed over time. Use the profiling data gathered over time to confidently identify hot paths that can be optimized. Many organizations waste 20-30% of their resources on code paths that are easily optimized. Polar Signals Cloud is a unique blend of technologies that was designed to provide the profiling toolset necessary for today's changing infrastructure and applications. Deploy immediately with zero instrumentation and benefit from actionable observability information.

PyRasgo, an open-source Python library, allows you to install Rasgo into your Python environment. Or, use our powerful, beautifully designed UI to get the Rasgo experience. You can create intuitive and detailed feature profiles in your panda's Rasgo UI or in its dataframe. Analyze key data statistics, quality issues, data drift, value distribution, and other data. Select features can be pruned to create a final set for modeling. Our extensive library of feature transformation functions can transform your raw data into useful features. Before you spend time training your model, visualize critical insights such as feature importance, explainability, and correlation. Collaborate with colleagues to create feature collection or duplicate existing feature collection to tailor for your model.

Zegami makes it easier to deliver explainable imaging AI more quickly and accurately. Zegami's full-stack service allows researchers, data scientists, and medical professionals to deliver explainable AI with greater efficiency. Our team and tools are your data science plug-in to create, validate, and enhance machine learning models in healthcare, life science, and manufacturing to propel your business or project forward.

Hawk AI combines AI and traditional rule-based approaches to monitor financial transactions. This ensures financial institutions are in compliance with anti-money laundering regulations. The solution includes classic rule-based models. These are enhanced with auto-closing features that are based on machine-learning models that learn from investigators' decisions through our case manager. Hawk AI uses Anomaly Detection as a machine learning model that is unsupervised. This allows Hawk AI to identify new patterns in crime using insights from the platform's overarching nature which spans multiple financial institutions. The platform gives full transparency to machine decisions in order to provide the necessary clarity for regulators who require "explainable" AI. It also instills trust in the machine's actions. Hawk AI uses Artificial Intelligence to maximize automation and delivers significant cost savings through a 70% reduction in the required resources.

Research-backed and validated equity signals and factors are delivered as an automated data-feed to quantitative and systematic investment professionals that is timely and cost efficient. A derived equity rating score of 0 to 9 with high K Scores indicates higher likelihood of out-performance. Quantitative buyside companies overlay K Score with investment models as buy/sell signal. We used machine learning algorithms and ranking algorithms to rank over 200 factors and signals, including fundamental, price/volume, and alternative data. You can access the most recent researched signals, anomalies, or factors in minutes, rather than weeks. You can save time and money by conducting your own research and validating factors. You will receive an automated data-feed, long-term history, and wide coverage all at your fingertips. An ETF that offers more control. Kavout can help you design index-enhancing portfolios.

Automate product discovery to learn customer intent. Machine learning can optimize results across the board. Personalize search, autosuggests, browse, and recommendation results for each user. Computational approaches that infer intent from a user's search query. Continuously updated search results rankings using automated learning from behavioral data. Data-driven, integrated effort to provide unique results for every person and query. Advanced tools to complement automated results with merchant expertise. Automate product discovery to learn customer intent. Machine learning can optimize results across the board. You can personalize search, autosuggest and browse results for each user. Natural language processing interprets the query and provides conversion-worthy results as users type. Constructor Search learns from behavioral data.

ClaimBuster is a project umbrella that includes all projects related to fact-checking for the IDIR Lab. It began as an attempt to create an AI model capable of automatically detecting claims that were worth checking. Since then, it has made steady progress towards the holy-grail of automated truth-checking. ClaimBuster has a wide range of users, including journalists. However, anyone who is interested in combating misinformation can use it. Our API allows easy access to our models. You can register for a free API Key and gain access. ClaimBuster was made possible by the contributions of human data-labeling. Sign up for an Account and start labeling to help improve our models. We've also opened-sourced the code for our machine learning models, so if your are an AI expert feel free to contribute there too. Our claim-spotting algorithm re-tweets any tweets that it believes may need to be fact-checked.

FIELD (FANUC Intelligent Edge Link & Driver) is a FANUC-developed Industrial IoT (Internet of Things). It allows users to connect production equipment from different generations and all manufacturers - including FANUC equipment – in a system. This allows for comprehensive data collection and analysis throughout the entire process chain. It reduces equipment downtime and improves operational efficiency and product quality. FIELD is an open platform that allows manufacturers to connect devices from different manufacturers (robots and PLCs) and create and implement FIELD Apps that meet their specific needs. FIELD Apps can be purchased by device manufacturers (robots and machines, PLCs and sensors). To provide advanced functionality for their equipment, users can also purchase FIELD Apps created by third-party software developers with the contextual experience and resources necessary to deliver game-changing innovative solutions.

Sensorita has developed a sensor that can withstand extreme environments such as construction sites. We have developed a new sensor technology that measures the fill level in large waste containers. The technology is based upon research at the Norwegian University of Life Sciences, and is the only sensor specifically designed for large containers. Senorita's unique fill-level measurement approach provides highly accurate and reliable results. We can measure the fill level of any container, even those that do not have a lid, by combining the robustness and accuracy of radar signals with cutting-edge machine-learning algorithms. Sensorita will let you know when the container is due for pickup based on AI. What if your customers never had to worry about container capacity? Sensorita’s solution will allow you to know when to collect waste.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

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

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

ACE is easy to learn because of its compact, clear, and semi-modular architecture. Simple, but not simplistic. Compact but not limited. Clear but not underpowered. ACE is a powerful, versatile synthesizer that can be used by beginners and seasoned users alike. The modular synthesizer's "boxes-and-cables" philosophy allows you to create your own custom instrument. The vast potential of modular synthesizers becomes apparent as you begin to connect the 16 modules in ACE. As ACE does no distinguish between audio and control signals (modulation), the full-range LFOs are also able to be used to create audio frequencies. ACE's oscillators have been modeled after analog circuits. This includes instabilities and non-linear characteristics.

Drone technology is rapidly evolving and only a software-centric solution will keep up. Dedrone's DroneTracker software is hosted on-premise or in the cloud. It uses our DroneDNA database for recognition and classification of RF, WiFi and non-WiFi drones. DroneTracker can also be integrated with third-party sensors and triggers alarms and countermeasures. Drone intrusions are logged by dedrone sensors and external video cameras. The DroneTracker software automatically captures forensic data, including drone manufacturer, model and time and length of drone activity. It also records video verification. Summary reports are automatically generated and made available for immediate analysis of the most important airspace security data. DroneTracker software incorporates Dedrone's machine-learning algorithms, which allow it to detect drones from other moving objects in the airspace.

The Easy Way to Remove Duplicate Salesforce Records DataGroomr uses Machine Learning to automatically detect duplicate Salesforce records. Duplicate Salesforce records are automatically loaded into a queue so users can compare them side-by-side and decide which values to keep, add new values, or merge. DataGroomr provides everything you need to locate, merge, and get rid off dupes. DataGroomr's Machine Learning algorithms take care of the rest. You can merge duplicate records in one click or en masse from within the app. You can select field values to create a master record, or you can use inline editing for new values. You don't want to see duplicates across the entire organization. You can define your own data by industry, region, or any Salesforce field. The import wizard allows you to merge, deduplicate and append records while importing Salesforce. Automated duplication reports and mass merging tasks can be set up at a time that suits your schedule.

Machine learning (ML), models that are specific to your equipment can be created using data from existing sensors. Automatic equipment monitoring pinpoints anomalous sensors and allows you to respond quickly and precisely. Automated equipment monitoring detects anomalies and immediately notifies you. This will speed up the resolution of issues. By incorporating feedback and anomaly trends, you can improve model performance and accuracy. Amazon Lookout for Equipment, a ML industrial equipment monitoring service, detects abnormal equipment behavior and alerts you to take action to avoid unplanned downtime. Automatically detecting abnormal equipment behavior will help you avoid unplanned downtime. Lookout for Equipment analyzes the sensor data from your industrial equipment to detect abnormal behavior. This allows you to quickly detect anomalies in equipment, diagnose them quickly, and prevent unplanned downtime.

The Cadence Clarity 3D Solver software is a 3D electromagnetic simulator that can be used to design critical interconnects on PCBs, IC Packages, and Systems on IC Designs. Clarity 3D Solver is a 3D electromagnetic simulation software tool that helps designers solve the most complex electromagnetic challenges in designing systems for 5G and automotive applications, high-performance computing and machine learning. Clarity 3D Solver's distributed multiprocessing, an industry-leading technology, provides the 10X speed and virtually unlimited capacity required to address larger and complex structures. It creates highly accurate S parameter models for use in high speed signal integrity, power integration, high frequency RF/microwave and electromagnetic compliance analyses.

Overcome challenges when acquiring data to train AI and machine learning systems. Rendered.ai, a PaaS, is designed for data scientists and engineers. Create synthetic datasets to train and validate ML/AI. Experiment with scene content, sensor models, and post-processing. Catalogue and characterize real and synthetic datasets. Download or move data into your own cloud repositories to be processed and trained. Synthetic data can be used to boost innovation and productivity. Create custom pipelines for modeling diverse sensors and computer-vision inputs. Python sample code is available for free and can be customized to model SAR, RGB Satellite imagery, and other sensor types. Flexible licensing allows for almost unlimited content creation. Create labeled, high-performance computing content quickly in a hosted environment. No-code configuration allows data scientists and engineers to collaborate.

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.

Your industry's strongest digital twin. A comprehensive data model is created based on your asbuilts and other sources, including an interactive map. Machine learning is enabling us to provide insights that are based on our industry-leading data modeling. These insights improve over time. Everything from commissioning to warranty claims and financial due diligence can be strengthened. A secure and centrally located platform for storing inspection reports, data and documents, CAD files and technical specifications, performance models, warranty documentation and shipping receipts, as well as photographs and field notes. Easily accessible and maintained with live geospatial equipment profiles. The market leader in aerial thermography offers unlimited inspection reports and analytics. The inputs for Raptor inspections can be any drone, plane, satellite, or sensor. Ours or yours. Get industry-leading training to ensure that your data collection meets specifications.

Qint, a cloud-based data processing platform, allows users to extract meaningful insights from unstructured texts. It allows you to combine the results of text-based and unstructured analytics to create a searchable index that can be used for data mining or predictive analytics. Qint uses machine learning algorithms to recognize correlations. You can train new algorithm models to identify custom entities or word usage specific for your domain. Qint's dashboard presents information via intuitive graphs and charts. You can drill down to specifics and filter reports as required. Qint allows you to export crawled and analyzed documents, as well as search results, in multiple file formats including CSV or XML. The platform can collect structured and unstructured data from documents, emails and databases, websites, as well as other data repositories. The platform can be manually fed data or at set intervals with cron jobs.

Fluke Connect™, software stores, displays and makes machine data viewable from more than 80 Fluke sensors and tools. The cloud allows teams to access their data from any device or computer, and store measurements. No matter if your maintenance and reliability team collects data from portable test tools or condition monitoring sensors, they have all the information they need to make crucial decisions and complete jobs. Remote sensors that upload data to cloud storage can reduce the time-consuming routine rounds. No more clipboards or data silos, just easy-accessible asset information on your smart phone. Fluke Connect Condition Monitoring™, software allows teams to view asset trends on graphs using connected condition monitoring devices. Data analysis can be done by staff, giving teams insights that they can use to predict asset condition or avoid downtime.

SensorCloud is a unique platform for remote management, storage, and visualization of sensor data. It leverages powerful Cloud computing technologies to offer exceptional data scalability, rapid visualization, user-programmable analysis, and data scalability. SensorCloud's core features are FastGraph, MathEngine®, LiveConnect and the OpenData API. SensorCloud makes it easy to create dashboards that display all your data. You can create dashboards with as little as a Timeseries Graph widget or more complex features such as Radial Gauges and Text Charts, Linear Gauges and FFTs, Statistics, and so on. SensorCloud allows you upload as much data you like, and LORD's sensors can sample very fast, so it was important that you could quickly visualize large amounts of data. We were unable to find an application that could handle even a few gigabytes, so we created our own algorithm.

You can create 3D models using simple photos and/or point cloud data. Reality modeling is the process that captures the physical reality of infrastructure assets, creates a representation of them, and maintains it through continuous surveys. ContextCapture, Bentley's reality modeling software provides real-world digital context in form of a 3D reality map. A reality mesh is a 3D model that represents real-world conditions and contains large quantities of image data and triangles. Each digital component can automatically be recognized and/or geospatially referred to, giving you an intuitive and immersive way of navigating, finding, viewing, querying, and querying your asset information. Reality meshes can be used in many engineering, maintenance, and GIS workflows to provide a precise digital context for design, construction, or operations decisions. Laser scans are used to supplement the ground-level imagery and drone photos that overlap.

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

CentML speeds up Machine Learning workloads by optimising models to use hardware accelerators like GPUs and TPUs more efficiently without affecting model accuracy. Our technology increases training and inference speed, lowers computation costs, increases product margins using AI-powered products, and boosts the productivity of your engineering team. Software is only as good as the team that built it. Our team includes world-class machine learning, system researchers, and engineers. Our technology will ensure that your AI products are optimized for performance and cost-effectiveness.