Machine Learning Tools
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The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to…
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Dataloop's platform is presented as a one-stop shop for generating datasets from raw visual data - It includes data management environment, intuitive annotation tool with automatic annotation capabilities, and data QA and debug tools.
Austin-based CognitiveScale offers Cortex 5, a platform for developing and deploying enterprise-grade artificial intelligence (AI) and AI lifecycle management platform for a variety of purposes such as risk calculation for banking, insurance or healthcare, continuous learning systems,…
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NEC offers the RAPID AI machine learning application, designed to perform surface or spatial inspections, or other pattern-detection or defect detection related tasks.
Mu Sigma headquartered in Northbrook offers muHPC, a machine learning platform built over Spark and Hadoop to perform or improve big data analytics.
Marlabs headquartered in Piscataway offers mAdvisor, is a cognitive computing application relying on machine learning and natural language processing to provide text and speech analytics, computer vision, and automated prediction.
Epistema, headquartered in Tel-Aviv, offers Ment, an AI-driven BI tool designed to integrate with a company's collaboration app (e.g. Slack) to provide better peer understanding and improve workplace decisions.
DataEdge, a cloud-based Data Analytics and AI Engineering platform provides modular, scalable, and API-driven solutions to unlock data-powered insights. Configured to HITRUST standards, DataEdge is a no-code platform, which can be deployed in hours without development time.
Innodata offers annotation of text, images, audio and video data for complex models. Ideal for computer vision, sentiment analysis, entity linking, text categorization, and syntactic parsing and tagging models. Innodata’s web-based Annotation Platform enables teams to expertly label…
SentiSight.ai is a web-based platform that can be used for image labeling and for developing AI-based image recognition applications. It has two major goals: the first is to make the image annotation task as convenient and efficient as possible, even for large projects with many…
Now from mParticle since the July 2022 acquisition, Vidora offers a real-time Machine Learning platform which focuses on consumer data. Vidora Cortex integrates machine learning into marketing, ad-tech, and product workflows, to enable use cases that include predictions, prescriptions…
ScoreData helps businesses leverage their data to improve the quality of their engagement with their customers. ScoreData ScoreFast is a machine learning platform that enables data scientists and business managers to create run-time consumer scores for fraud detection, churn-management,…
TIMi, headdquartered in Brussels, offers their eponymous framework to push the barriers of analytics, predictive analytics and Big Data, while offering an environment to develop new ideas and make business decisions.
Resemble AI creates Custom Voices using proprietary Deep Learning models that can produce realistic Speech Synthesis. Resemble AI augments AI voice with a text-to-speech AI voice generator and real-time APIs to build immersive experiences.
Flex Logic headquartered in Mountain View offers InferX X1, is an AI edge inference accelerator that enables megapixel neural network models in high performance applications. The InferX Edge Inference SDK finds an optimal hardware mapping for each layer of a neural network without…
Tonkean uses AI to autonomously coordinate, execute and manage your business workflows, across data and people, so nothing falls through the cracks. The company's platform automatically connects to the interfaces users already use such as forms, email, chat, or other tools, based…
OctoML offers an acceleration platform that helps engineering teams deploy machine learning models on any hardware, cloud provider, or edge device quickly.The platform, Octomizer, is built on top of the open-source Apache TVM compiler framework project. It supports a wide variety…
Shelf Engine helps businesses increase sales by accurately predicting the perfect amount of perishable goods to order.
Sima.ai develops embedded edge hardware for machine learning applications and is developing a machine learning system-on-a-chip product. Edge computing AI chipsets are essentially designed with low power requirements to support fast inferencing. The company says that its hardware’…
FortisAI is a modern data analytics system architecture employing both natural language processing (NLP) and other machine learning capability to perform a wide range of mission support functions. The architecture, which advantages the latest artificial intelligence (AI) and high…
Magic FinServ is a digital technology services company for the FinTech industry. Magic FinServ brings a combination of financial services domain knowledge & technology skills in Blockchain & Artificial Intelligence. The company offers end to end services so that customers…
CitiusTech headquartered in Princeton offers the Medictive machine learning and predictive analytics engine for health systems, payers, ACOs, life science entities and others, supplying artificial intelligence approaches to analysis.
Learn More About Machine Learning Tools
What are Machine Learning Tools?
Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.
They can be used, for example, to build recommendation engines, predict search patterns, filter spam, build news feeds, detect fraud and security threats, and much more. There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.
Supervised algorithms are machine learning tools with training wheels. They require a person to program both the input and the desired output, as well as provide feedback as to the accuracy of the end results.
Unsupervised algorithms require very little human intervention by instead using an approach called “deep learning” to review massive banks of data and arrive at conclusions based on previous examples of training data; they are, therefore, generally used for more complex processing tasks such as image recognition, speech-to-text, and natural language generation.
Semi-supervised algorithms tend to fall in the middle ground.
Reinforced algorithms force models to repeat a process until it produces the most favorable outcomes. Attempts that produce these favorable outcomes are rewarded and attempts that produce unfavorable results are penalized until the algorithm learns the optimal process.
Machine Learning Tools' Features & Capabilities
Most vendors offering machine learning tools include:
- Data collection and preparation
- Model building
- Training and application deployment
Additional Tools
Some vendors also offer additional tools to
- Store aggregated data on the Cloud
- Build models with easy drag and drop capabilities
- Access libraries with pre-modeled routines and functions
Machine Learning Tools Comparison
When beginning your process for a machine learning tool with which to get started, walk through the following flow of questions to help you narrow down your options:
- Am I looking for a managed machine learning platform? (Think time-and-cost efficiency.)
- Do I need a mobile-supported tool?
- For what scripting language do I need support? (R, Python, Java, C++, etc.)
- If you're on a budget, ask yourself: Can I effectively manage my training data with the storage space I have? (You may need to consider moving to the cloud, which is typically more expensive.)
- Do I need support for multiple model types? (Binary, regression, multi-class, etc.)
- Would I prefer having an extensive library with which to start?
- Are my projects expected to grow (and thus requiring a need for scalability)?
- Do I need a full cycle deep learning system?
Ready to start comparing your shortlist head-to-head? Start a Machine Learning Tools comparison
Pricing Information
Many vendors offering machine learning tools will offer a free trial or a free version with a limited batch of predictions. There are several free and open-source machine learning platforms, like Google's Tensorflow, for developers on a budget. For paid plans, prices are generally Pay-As-You-Go, tiered by price per hour and training units, typically starting at $0.10/Unit/Hour. Deep learning capabilities are typically more expensive, at $1+/GPU/Hour.