MInD Platform Description
Our MIND platform will help you solve your problem. We then train your staff to maintain the solution, and re-initialize the underlying models if necessary. Our products and services are used by businesses in the industrial, medical, consumer service, and consumer service industries to automate processes that were previously only possible with human intervention. Quality assurance in the food industry. Counting and classifying cells in biomedicine. Analyzing gaming performance. Measuring geometrical characteristics (position, size, profile, distance, angle. Tracking objects in agriculture. Time series analysis in sport and healthcare. Our MInD platform allows you to build AI solutions for your business. It provides all the tools you need to develop deep learning solutions in each of the five stages.
MInD Platform Alternatives
MXNet
The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.
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Auger.AI
Auger.AI offers the best solution to ensure accuracy of machine learning models. Our MLRAM tool (Machine Learning Review & Monitoring) ensures that your models are always accurate. It even calculates the ROI for your predictive model! MLRAM can be used with any machine-learning technology stack. Inaccurate predictions can cost you money if your ML system's lifecycle doesn't include consistent measurement. Frequent retraining models can be costly and may not solve the problem if they are experiencing concept drift. MLRAM is a valuable tool for both data scientists and business users. It includes features such as accuracy visualization graphs and performance alerts. It also allows for anomaly detection and automated optimized retraining. It takes only one line of code to connect your predictive model with MLRAM. Qualified users can get a one-month free trial of MLRAM. Auger.AI is the most accurate AutoML platform.
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Dataloop AI
Manage unstructured data to develop AI solutions in record time.
Enterprise-grade data platform with vision AI. Dataloop offers a single-stop-shop for building and deploying powerful data pipelines for computer vision, data labeling, automation of data operations, customizing production pipelines, and weaving in the human for data validation. Our vision is to make machine-learning-based systems affordable, scalable and accessible for everyone.
Explore and analyze large quantities of unstructured information from diverse sources. Use automated preprocessing to find similar data and identify the data you require. Curate, version, cleanse, and route data to where it's required to create exceptional AI apps.
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Caffe
Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.
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Integrations
No Integrations at this time
Company Details
Company:
Machine Intelligence
Headquarters:
Hungary
Website:
mi.services
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Product Details
Platforms
Windows
Type of Training
Documentation
In Person
Customer Support
Phone Support
Online
MInD Platform Features and Options
Deep Learning Software
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
MInD Platform User Reviews
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