Best Machine Learning Software for Linux of 2024

OpenCV (Open Source Computer Vision Library), is an open-source machine learning and computer vision software library. OpenCV was created to provide a common infrastructure to support computer vision applications and accelerate machine perception in commercial products. OpenCV is a BSD-licensed product that makes it easy to modify and use the code by businesses. The library contains more than 2500 optimized algorithms. This includes a comprehensive set both of classic and modern computer vision and machine-learning algorithms. These algorithms can be used for recognizing faces, identifying objects, tracking camera movements, classifying human actions in videos and producing 3D point clouds from stereo-cameras. They can also be used to stitch images together to create a high resolution image of the entire scene, find similar images from a database, remove red eyes from images taken with flash, recognize scenery, and follow eye movements.

Apache PredictionIO®, an open-source machine-learning server, is built on top a state of the art open-source stack that allows data scientists and developers to create predictive engines for any type of machine learning task. It allows you to quickly create and deploy an engine as web service on production using customizable templates. Once deployed as a web-service, it can respond to dynamic queries immediately, evaluate and tune multiple engine variations systematically, unify data from multiple platforms either in batch or real-time for comprehensive predictive analysis. Machine learning modeling can be speeded up with pre-built evaluation methods and systematic processes. These measures also support machine learning and data processing libraries like Spark MLLib or OpenNLP. You can create your own machine learning models and integrate them seamlessly into your engine. Data infrastructure management simplified. Apache PredictionIO®, a complete machine learning stack, can be installed together with Apache Spark, MLlib and HBase.

Reframe your perception of time with data analytics. Instantly access the data of your business and work directly in your dashboard, without having to go back and forth with your IT team. Indexima DataHub is a new space where operational and functional users can instantly access their data. Indexima's unique indexing engine, combined with machine learning, allows businesses to quickly and easily access their data. The robust and scalable solution allows businesses to query their data directly from the source in volumes of up to tens billions of rows within milliseconds. With our Indexima platform, users can implement instant analytics for all their data with just one click. Indexima’s new ROI and TCO Calculator will help you determine the ROI of your data platform in just 30 seconds. Infrastructure costs, project deployment times, and data engineering cost, while boosting analytical performances.

CCH Tagetik Corporate Performance Management software is trusted by companies to save time, reduce costs, and reduce risk. Connect data, processes, and people with one trusted source to get a quicker close and more forward-looking planning. CCH Tagetik Finance Transformation Platform powered by the Analytic Information Hub is the unified platform that connects finance & operations and streamlines consolidation & close planning, reporting & analysis, disclosures, and compliance.

NLG Studio is an Artificial Intelligence solution (AI) developed by Arria NLG to be used by small and medium-sized companies. It provides them with full-time skills similar to financial analysts. This includes spotting trends and identifying problems and forecasting what's likely next. Arria's patented technology has been used to create a SaaS-based solution that provides relevant details in just seconds via Natural Language Generation. This platform combines financial and business intelligence.

We are a software company founded by AI and engineering experts, who create exceptional technology to solve the most complicated process challenges. We want to create more sustainable businesses with improved performance in all metrics that matter: cost, quality, delivery, and human experience. Our advanced decision intelligence products – viabl.ai & XpertFactory – allow organisations to harness their two most valuable resources – the expertise of their staff and the predictive analytics of their data. We exist to help people make better decisions.

Luminoso transforms unstructured text data to business-critical insights. We empower organizations to interpret and act on the information people give us by using common-sense artificial intelligence. Luminoso requires little setup, maintenance or training. It also doesn't require any data input. Luminoso combines the world's best natural language understanding technology with a vast knowledgebase to learn words from context - just like humans - and accurately analyze text in minutes instead of months. Our software offers native support in more than a dozen languages so leaders can quickly explore data relationships, make sense out of feedback, and triage queries to drive value. Luminoso, a privately held company, is headquartered in Boston MA.

IWith your marketing ideas and our machine learning, you can optimize your web site for every single visitor. Simultaneously test all of your most creative marketing ideas to constantly showcase the messaging, content, and experiences that performs best. This is way beyond A/B testing and old-school, rules-based personalization. This is the new standard for high-converting landing pages across your website. This is Continuous Conversion™—and it changes… everything.

We offer the highest quality SLA (>95%) even for the most complicated workflows. Our team can assist with everything from the implementation of a solid quality rubric to raising edge case. We are an ethical AI company that has provided economic opportunities to over 52,000 people in underserved and marginalized areas. ML Assisted annotation allowed for efficiency improvements of up to 3-4x per class annotation. We are able to quickly adapt to ramp-ups and focus shifts. Secure work environments are ensured by ISO-certified delivery centers, biometric authentication, 2FA user authentication, and ISO-certified delivery centers. You can quickly re-prioritize tasks, give quality feedback, and monitor production models. All data types are supported. You can do more with less. We combine machine learning with humans to filter data and select images that are relevant to your use cases. Based on your initial guidelines, you will receive sample results. We will work with you to identify and recommend best annotation practices.

Machine learning applications can be deployed to production in minutes instead of months. Automate the transformation of raw data and generate training data sets. Also, you can serve features for online inference at large scale. Replace bespoke data pipelines by robust pipelines that can be created, orchestrated, and maintained automatically. You can increase your team's efficiency and standardize your machine learning data workflows by sharing features throughout the organization. You can serve features in production at large scale with confidence that the systems will always be available. Tecton adheres to strict security and compliance standards. Tecton is neither a database nor a processing engine. It can be integrated into your existing storage and processing infrastructure and orchestrates it.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

NimbleBox: Helps teams ship ML features to their customers faster by enabling them to be built as AI companies.

MLReef allows domain experts and data scientists secure collaboration via a hybrid approach of pro-code and no-code development. Distributed workloads lead to a 75% increase in productivity. This allows teams to complete more ML project faster. Domain experts and data scientists can collaborate on the same platform, reducing communication ping-pong to 100%. MLReef works at your location and enables you to ensure 100% reproducibility and continuity. You can rebuild all work at any moment. To create interoperable, versioned, explorable AI modules, you can use git repositories that are already well-known. Your data scientists can create AI modules that you can drag and drop. These modules can be modified by parameters, ported, interoperable and explorable within your organization. Data handling requires a lot of expertise that even a single data scientist may not have. MLReef allows your field experts to assist you with data processing tasks, reducing complexity.

Barbara is the Edge AI Platform in the industry space. Barbara helps Machine Learning Teams, manage the lifecycle of models in the Edge, at scale. Now companies can deploy, run, and manage their models remotely, in distributed locations, as easily as in the cloud. Barbara is composed by: .- Industrial Connectors for legacy or next-generation equipment. .- Edge Orchestrator to deploy and control container-based and native edge apps across thousands of distributed locations .- MLOps to optimize, deploy, and monitor your trained model in minutes. .- Marketplace of certified Edge Apps, ready to be deployed. .- Remote Device Management for provisioning, configuration, and updates. More --> www. barbara.tech

Combine Data Science, Business Intelligence and Data Management Capabilities into One Integrated, Self-Serve Platform. Analance is an end-to-end platform with robust and salable features that combines Data Science and Advanced Analytics, Business Intelligence and Data Management into a single integrated platform. It provides core analytical processing power to ensure that data insights are easily accessible to all, performance remains consistent over time, and business objectives can be met within a single platform. Analance focuses on making quality data into accurate predictions. It provides both citizen data scientists and data scientists with pre-built algorithms as well as an environment for custom programming. Company - Overview Ducen IT provides advanced analytics, business intelligence, and data management to Fortune 1000 companies through its unique data science platform Analance.

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.

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.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

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.

You can make changes to your models quickly and easily by turning on the black box, pip installing 3LC. Iterate quickly and remove the guesswork in your model training. Visualize per-sample metrics in your browser. Analyze and fix issues in your dataset by analyzing your training. Interactive data debugging, guided by models. Find out which samples are important or inefficient. Understanding what samples work well and where your model struggles. Improve your model in different ways by weighting your data. Make sparse and non-destructive changes to individual samples or a batch. Keep track of all changes, and restore previous revisions. Data tracking and metrics per-sample, per-epoch will allow you to go deeper than standard experiment trackers. To uncover hidden trends, aggregate metrics by sample features rather than epoch. Each training run should be tied to a specific revision of the dataset for reproducibility.