Alternatives to Weka

The training data platform for AI teams. A machine learning model can only be as good as the training data it uses. Labelbox is an integrated platform that allows you to create and manage high quality training data in one place. It also supports your production pipeline with powerful APIs. A powerful image labeling tool for segmentation, object detection, and image classification. You need precise and intuitive image segmentation tools when every pixel is important. You can customize the tools to suit your particular use case, including custom attributes and more. The performant video labeling editor is for cutting-edge computer visual. Label directly on the video at 30 FPS, with frame level. Labelbox also provides per-frame analytics that allow you to create faster models. It's never been easier to create training data for natural language intelligence. You can quickly and easily label text strings, conversations, paragraphs, or documents with fast and customizable classification.

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

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.

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.

Apache Mahout is an incredibly powerful, scalable and versatile machine-learning library that was designed for distributed data processing. It provides a set of algorithms that can be used for a variety of tasks, such as classification, clustering and recommendation. Mahout is built on top of Apache Hadoop and uses MapReduce and Spark for data processing. Apache Mahout(TM), a distributed linear-algebra framework, is a mathematically expressive Scala DSL that allows mathematicians to quickly implement their algorithms. Apache Spark is recommended as the default distributed back-end, but can be extended to work with other distributed backends. Matrix computations play a key role in many scientific and engineering applications such as machine learning, data analysis, and computer vision. Apache Mahout is designed for large-scale data processing, leveraging Hadoop and Spark.

A platform that offers a variety of machine learning algorithms to meet data mining and analysis needs. Machine Learning Platform for AI offers end-to-end machine-learning services, including data processing and feature engineering, model prediction, model training, model evaluation, and model prediction. Machine learning platform for AI integrates all these services to make AI easier than ever. Machine Learning Platform for AI offers a visual web interface that allows you to create experiments by dragging components onto the canvas. Machine learning modeling is a step-by-step process that improves efficiency and reduces costs when creating experiments. Machine Learning Platform for AI offers more than 100 algorithm components. These include text analysis, finance, classification, clustering and time series.

Open source machine learning and data visualization. With a wide range of tools, you can create data analysis workflows visually. Simple data analysis can be done with data visualization. Explore statistical distributions, box and scatter plots. Or dive deeper with decision trees and hierarchical clustering, heatmaps and MDS. Smart attribute ranking and selections can make multidimensional data more sensible in 2D. Interactive data exploration allows for qualitative analysis in a quick and efficient manner. The graphic user interface allows you focus on exploratory data analysis and not coding. Smart defaults make prototyping a data analysis workflow very easy. Connect widgets to the canvas, place them on the screen, and then load your data. We like to show data mining rather than just explain it. Orange excels at this.

Paradise employs robust, unsupervised machine-learning and supervised deep learning technologies in order to increase interpretation and gain greater insight from the data. Generate attributes to extract valuable geological information and for input into machine learning analysis. Identify the attributes that have the greatest variance and contribution to a given set of attributes in a particular geologic setting. Display the neural classes (topology), and the associated colors resulting Stratigraphic analysis. These indicate the distribution of facies. Deep learning and machine learning can automatically detect faults. Compare machine learning classification results with other seismic attributes to traditional logs. In fraction of the time it takes to generate spectral and geometric decomposition attributes on a cluster compute nodes, you can do this in fraction of the time with a single machine.

Connect your data to the Engine by uploading a file, or connecting to a database. You can then analyze your data with visualizations or prepare it for machine learning modeling using the data wrangling recipes. Build machine learning models using algorithms such as clustering, classification, or regression. All without writing any code. Discover insights into your data using the feature importance tools, prediction explanations, and what-ifs. Our connectors allow you to make predictions and integrate them into your existing systems.

Data scientists need innovative and efficient turn-key solutions. For quantum engineers, powerful circuit building blocks. Turn-key implementations of algorithms for data scientists, financial analysts, engineers. Explore problems in binary optimization and machine learning on simulators and real hardware. You don't need to have any prior experience in quantum computing. To load classical data into quantum states, use NISQ data loader devices. Circuit building blocks are available for linear algebra with distance estimation or matrix multiplication circuits. You can create your own algorithms using our circuit building blocks. You can get a significant performance boost with D-Wave hardware. Also, the latest gate-based improvements will help you. Quantum data loaders and algorithms offer guaranteed speed-ups in clustering, classification, regression.

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.

AIxBlock is an end-to-end blockchain-based platform for AI that harnesses unused computing resources of BTC miners, as well as all global consumer GPUs. Our platform's training method is a hybrid machine learning approach that allows simultaneous training on multiple nodes. We use the DeepSpeed-TED method, a three-dimensional hybrid parallel algorithm which integrates data, tensor and expert parallelism. This allows for the training of Mixture of Experts models (MoE) on base models that are 4 to 8x larger than the current state of the art. The platform will identify and add compatible computing resources from the computing marketplace to the existing cluster of training nodes, and distribute the ML model for unlimited computations. This process unfolds dynamically and automatically, culminating in decentralized supercomputers which facilitate AI success.

The Salford Predictive Modeler® (SPM), software suite, is highly accurate and extremely fast for developing predictive, descriptive, or analytical models. Salford Predictive Modeler®, which includes the CART®, TreeNet®, Random Forests® engines, and powerful new automation capabilities and modeling capabilities that are not available elsewhere, is a software suite that includes the MARS®, CART®, TreeNet[r], and TreeNet®. The SPM software suite's data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation. SPM algorithms are essential in advanced data science circles. Automation of model building is made easier by the SPM software suite. It automates significant portions of the model exploration, refinement, and refinement process for analysts. We combine all results from different modeling strategies into one package for easy review.

Machine Learning made simple for everyone The leading Machine Learning platform will take your business to the next level. Get data-driven decisions now! No more cumbersome or expensive solutions. Machine Learning that works. BigML offers a variety of Machine Learning algorithms that are robustly engineered and can be applied across your company to solve real-world problems. You can avoid dependencies on multiple libraries that will increase complexity, maintenance costs, or technical debt in your projects. BigML allows unlimited predictive applications in all industries, including aerospace, automotive and energy, entertainment, financial, financial services, food and healthcare, IoT pharmaceutical, transportation, telecommunications and many more. Supervised Learning: Classification and regression (trees and ensembles, logistic regressions and deepnets), as well as time series forecasting.

MLBox is a powerful Automated machine learning python Library. It provides the following features fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization in high-dimensional space, state-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM), and prediction with models interpretation. MLBox's main package includes 3 sub-packages, namely preprocessing and prediction. Each of them is aimed at reading data and preprocessing it, testing or optimising learners of different levels and predicting the target using a test dataset.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.

AutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool.

Accelerate compute jobs such as machine learning and HPC. There are many GPUs available to suit different price points and performance levels. Flexible pricing and machine customizations are available to optimize your workload. High-performance GPUs available on Google Cloud for machine intelligence, scientific computing, 3D visualization, and machine learning. NVIDIA K80 and P100 GPUs, T4, V100 and A100 GPUs offer a variety of compute options to meet your workload's cost and performance requirements. You can optimize the processor, memory and high-performance disk for your specific workload by using up to 8 GPUs per instance. All this with per-second billing so that you only pay for what you use. You can run GPU workloads on Google Cloud Platform, which offers industry-leading storage, networking and data analytics technologies. Compute Engine offers GPUs that can be added to virtual machine instances. Learn more about GPUs and the types of hardware available.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

HPE Ezmeral ML Ops offers pre-packaged tools that enable you to operate machine learning workflows at any stage of the ML lifecycle. This will give you DevOps-like speed, agility, and speed. You can quickly set up environments using your preferred data science tools. This allows you to explore multiple enterprise data sources, and simultaneously experiment with multiple deep learning frameworks or machine learning models to find the best model for the business problems. On-demand, self-service environments that can be used for testing and development as well as production workloads. Highly performant training environments with separation of compute/storage that securely access shared enterprise data sources in cloud-based or on-premises storage.

Machine teaching that is highly efficient An annotation tool powered with active learning. Prodigy is a scriptable tool that allows data scientists to do annotations themselves. This allows for a new level in rapid iteration. Transfer learning technologies allow you to train production-quality models using very few examples. Prodigy allows you to take full advantage modern machine learning by using a more agile approach for data collection. You'll be more productive, more independent, and deliver more successful projects. Prodigy combines state-of-the art insights from machine learning with user experience. You are only required to annotate examples that the model doesn't already know. The web application is flexible, powerful, and follows modern UX principles. It's simple to understand: it's designed for you to focus on one decision at the time and keep you clicking, much like Tinder for data.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.

Kubeflow is a project that makes machine learning (ML), workflows on Kubernetes portable, scalable, and easy to deploy. Our goal is not create new services, but to make it easy to deploy the best-of-breed open source systems for ML to different infrastructures. Kubeflow can be run anywhere Kubernetes is running. Kubeflow offers a custom TensorFlow job operator that can be used to train your ML model. Kubeflow's job manager can handle distributed TensorFlow training jobs. You can configure the training controller to use GPUs or CPUs, and to adapt to different cluster sizes. Kubeflow provides services to create and manage interactive Jupyter Notebooks. You can adjust your notebook deployment and compute resources to meet your data science requirements. You can experiment with your workflows locally and then move them to the cloud when you are ready.

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

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.

The machine learning platform that unifies data and orchestrates intelligent behavior among revenue-generating teams. It also delivers out-of the-box attribution and actionable analytics. QFlow.ai processes the gigabytes worth of data that Salesforce.com's activity table stores. To help you win more deals and generate more opportunities, we normalize, trend, or analyze your sales efforts. QFlow.ai uses data engineering for outbound activity reporting. It focuses on one crucial factor: whether they were productive. It also automatically displays critical metrics such as average days between first activity and opp creation, and average days between opp création to close. To understand trends in sales activity and productivity over time, Sales Effort data can either be filtered by a team or an individual.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

Machine Learning and Predictive Analytics Platform. Advanced artificial intelligence is a pre-built platform for machine learning and big-data analytics that can enhance data-driven decision making. OpenText Magellan makes predictive analytics easy to use and provides flexible data visualizations that maximize business intelligence. Artificial intelligence software reduces the need to manually process large amounts of data. It presents valuable business insights in a manner that is easily accessible and relevant to the organization's most important objectives. Organizations can enhance business processes by using a curated combination of capabilities such as predictive modeling, data discovery tools and data mining techniques. IoT data analytics is another way to use data to improve decision-making based on real business intelligence.

CORE is a high performance computing platform that can be used for a variety of applications. CORE is easy to use with its point-and-click interface. You can run the most complex applications. CORE provides unlimited computing power on-demand. Cloud computing is available without the high-cost. CORE for teams offers powerful tools that allow you to sort, filter, create, connect, and create users, machines, networks, and machines. With an intuitive and simple GUI, it's easier than ever to see all of your infrastructure from one place. It is easy to add Active Directory integration or VPN through our simple but powerful management console. It's now possible to do things that used to take days, or even weeks. Even complex network configurations can be managed with just a few clicks.

Automation, machine learning and data mining are all key components of design thinking. These are not things companies do anymore, they are how they do what they do. Vector is a digital delivery platform that accelerates digital transformation and propels innovation. It ensures that all your activities are digitally enabled. You don't need to be focused on "going digital", you can focus on being digital. The days of a standalone digital project are gone. Digital powers almost every company move. Analytics informs every high-stakes decision. Companies that are able to identify emerging technologies early will have a significant advantage. Vector brings together all of these capabilities, allowing us to integrate a variety of digital capabilities into every project we work on. Bain has experts in digital marketing, smart automation, prototyping and enterprise technology. This allows us to adopt a digital-first approach for every engagement.

Our machine learning technology is highly innovative and can accurately predict financial time series even when only a few training data points are available. Advance AI is computationally demanding. YottamineAI uses the cloud to reduce the time and cost of managing hardware. This helps to get a much higher ROI. Trade secrets are protected by strong encryption and key protection. We use strong encryption to protect your data and follow best practices in AWS. We help you make informed decisions by evaluating how your data, both past and future, can be used to generate predictive analytics. Yottamine Consulting Services offers project-based predictive analytics to meet your data-mining requirements.

It's fast and easy to create machine learning models for websites, apps, and other applications. Teachable Machine is flexible. You can use files or capture live examples. It respects your work. You can even use it entirely on-device without having to leave any microphone or webcam data. Teachable Machine, a web-based tool, makes it easy to create machine learning models. Artists, educators, students, innovators, and makers of all types - anyone with an idea to explore. There is no need to have any prior machine learning knowledge. Without writing any machine learning code, you can train a computer how to recognize your images, sounds, poses, and sounds. You can then use your model in your own sites, apps, and other projects.

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.

SparkCognition, an industrial AI company, has created a natural language processing solution that automates the workflows of unstructured data within companies so that humans can concentrate on high-value business decisions. DeepNLP uses machine learning to automate the retrieval, classification, and analysis of information. DeepNLP integrates with existing workflows to allow organizations to respond more quickly to changes in their businesses and get quick answers to specific queries.

Folio3 has a dedicated team of Data Scientists and Consultants who have completed end-to-end projects in machine learning, natural language processing and computer vision. Companies can now use highly customized solutions with advanced Machine Learning capabilities thanks to Artificial Intelligence and Machine Learning algorithms. Computer vision technology has revolutionized the way companies use visual content. It has also made it easier to analyze visual data and introduced new functions that are image-based. Folio3's predictive analytics solutions produce fast and effective results that allow you to identify anomalies and opportunities in your business processes.

Machine learning (ML) allows you to detect machine problems before they happen and take immediate action. Easy installation and secure analysis via the Amazon Monitron end to end system allow you to quickly monitor your equipment. Amazon Monitron continuously improves system accuracy by analyzing technician feedback through the web and mobile apps. Amazon Monitron is an end to end system that uses machine-learning to detect abnormal conditions in industrial equipment. This allows for predictive maintenance. Easy-to-install hardware combined with the power of machine learning allows you to save money and prevent equipment from going down. Predictive maintenance and machine learning can reduce unplanned equipment downtime. Amazon Monitron uses machine-learning to analyze temperature and vibration data. Amazon Monitron helps you predict equipment failures before they happen. Compare the cost of getting started with the savings you could make.

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.

Scikit-learn offers simple and efficient tools to analyze predictive data. Scikit-learn, an open source machine learning toolkit for Python, is designed to provide efficient and simple tools for data modeling and analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, built on popular scientific libraries such as NumPy SciPy and Matplotlib. It offers a range of supervised learning algorithms and unsupervised learning methods, making it a valuable toolkit for researchers, data scientists and machine learning engineers. The library is organized in a consistent, flexible framework where different components can be combined to meet specific needs. This modularity allows users to easily build complex pipelines, automate tedious tasks, and integrate Scikit-learn in larger machine-learning workflows. The library's focus on interoperability also ensures that it integrates seamlessly with other Python libraries to facilitate smooth data processing.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

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.

Easily extract, transform, manage & process CAD data, FE data, CFD and test results. Create automatic data pipelines to support machine learning, deep learning, and ROM. Data science barriers can be removed without coding. Key Ward's platform, the first engineering no-code end-to-end solution, redefines how engineers work with their data. Our software allows engineers to handle multi-source data with ease, extract direct value using our built-in advanced analytical tools, and build custom machine and deep learning model with just a few clicks. Automatically centralize, update and extract your multi-source data, then sort, clean and prepare it for analysis, machine and/or deep learning. Use our advanced analytics tools to correlate, identify patterns, and find dependencies in your experimental & simulator data.

The platform is easy to use and quick to implement machine learning and computer vision workflows. Sense makes it easy to create and deploy AI IoT solutions on any cloud, edge or on-premise. Learn how Sense makes it easy for AI/ML teams to create and deploy AI IoT solutions to any cloud, the edge or on-premise. It is powerful enough for ML engineers but simple enough for subject matter experts. Sense Data Annotation maximizes the success of your machine-learning models by making it the easiest and fastest way to label image and video data for high-quality training datasets. The Sense platform provides one-touch labeling integration to enable continuous machine learning at edge for simplified management.

Machine learning has led to business and research breakthroughs in everything from network security to medical diagnosis. To make similar breakthroughs possible, we created the Tensor Processing unit (TPU). Cloud TPU is a custom-designed machine learning ASIC which powers Google products such as Translate, Photos and Search, Assistant, Assistant, and Gmail. Here are some ways you can use the TPU and machine-learning to accelerate your company's success, especially when it comes to scale. Cloud TPU is designed for cutting-edge machine learning models and AI services on Google Cloud. Its custom high-speed network provides over 100 petaflops performance in a single pod. This is enough computational power to transform any business or create the next breakthrough in research. It is similar to compiling code to train machine learning models. You need to update frequently and you want to do it as efficiently as possible. As apps are built, deployed, and improved, ML models must be trained repeatedly.

Baidu AI Cloud Machine Learning is an end-toend machine learning platform for enterprises and AI developers. It can perform data preprocessing, model evaluation and training, as well as service deployments. The Baidu AI Cloud AI Development Platform BML is a platform for AI development and deployment. BML allows users to perform data pre-processing and model training, evaluation, service deployment and other tasks. The platform offers a high-performance training environment for clusters, a massive algorithm framework and model cases as well as easy to use prediction service tools. It allows users to concentrate on the algorithm and model, and achieve excellent model and predictions results. The interactive programming environment is fully hosted and allows for data processing and code debugging. The CPU instance allows users to customize the environment and install third-party software libraries.

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.

A simple-to-use interactive tool that allows data exploration, analysis, visualization and machine learning. Cloud Datalab is an interactive tool that allows you to analyze, transform, visualize, and create machine learning models on Google Cloud Platform. It runs on Compute Engine. It connects to multiple cloud services quickly so you can concentrate on data science tasks. Cloud Datalab is built using Jupyter (formerly IPython), a platform that boasts a rich ecosystem of modules and a solid knowledge base. Cloud Datalab allows you to analyze your data on BigQuery and AI Platform, Compute Engine and Cloud Storage using Python and SQL. JavaScript is also available (for BigQuery user defined functions). Cloud Datalab can handle megabytes and terabytes of data. Cloud Datalab allows you to query terabytes and run local analysis on samples of data, as well as run training jobs on terabytes in AI Platform.

Amazon EC2 capacity blocks for ML allow you to reserve accelerated compute instance in Amazon EC2 UltraClusters that are dedicated to machine learning workloads. This service supports Amazon EC2 P5en instances powered by NVIDIA Tensor Core GPUs H200, H100 and A100, as well Trn2 and TRn1 instances powered AWS Trainium. You can reserve these instances up to six months ahead of time in cluster sizes from one to sixty instances (512 GPUs, or 1,024 Trainium chip), providing flexibility for ML workloads. Reservations can be placed up to 8 weeks in advance. Capacity Blocks can be co-located in Amazon EC2 UltraClusters to provide low-latency and high-throughput connectivity for efficient distributed training. This setup provides predictable access to high performance computing resources. It allows you to plan ML application development confidently, run tests, build prototypes and accommodate future surges of demand for ML applications.

Declarative machine-learning systems offer the best combination of flexibility and simplicity, allowing for the fastest way to implement state-of-the art models. The system works by asking users to specify the "what" and then the system will figure out the "how". Start with smart defaults and iterate down to the code level on parameters. With Ludwig at Uber, and Overton from Apple, our team pioneered declarative machine-learning systems in industry. You can choose from our pre-built data connectors to support your databases, data warehouses and lakehouses as well as object storage. You can train state-of the-art deep learning models without having to manage infrastructure. Automated Machine Learning achieves the right balance between flexibility and control in a declarative manner. You can train and deploy models quickly using a declarative approach.

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