RAD Machine Learning Visual Studio, Design, Train, and Deploy Deep Learning Models Without Coding

Deepcognition machine learning studio tool supports Keras, Tensorflow, PyTorch, MxNet, Caffe2, Chainer 

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You can upload the data in several different formats, we handle the encoding of the data for you. You can also access data from other cloud Repositories like S3, Google Cloud and more.

You can also pull data from your local folders and start creating Deep Learning models in seconds.

Model Building

The simple drag & drop interface helps you design deep learning models with ease. Pre-trained models as well as use built-in assistive features simplify and accelerate the model development process. You can import model code and edit the model with the visual interface.

Code is generated as you are building your Model. Our version of AutoML let you build an initial version of the model with click of a button.

Hyper parameter Tuning & Training

Easy to do Hyperparameter Tuning.

Train your Hyper parameters using our Multi GPU training system where you can deploy multiple GPUs at the same time to cut your training time.

Experiments & Comparison

All the models you create are saved as experiments for you to compare all of your previous work. You can always switch back to old model if needed. Tabular comparison of all the model changes to assist you with model optimization.

Model Deployment

You can download the model as a binary model or as a Python Library. You have One-Click Deployment of the model as REST API and we also generate a form based Web App for you to showcase your solution.

• Our goal is to provide developers, engineers and researchers with an easy to use AI development and deployment platform. Our platform can be used in the cloud or on your infrastructure. We strive to become the platform of choice for all developers and users of deep learning AI.

With Deep Cognition you can choose from a simple but powerful GUI where you can drag and drop neural networks and create Deep Learning models with AutoML, to a full autonomous IDE where you can code and interact with your favorite libraries.

Visual Editor and Code IDE

DESIGN, TRAIN, AND DEPLOY DEEP LEARNING MODELS WITHOUT CODING

Our platform simplifies and accelerates the process of working with deep learning across popular frameworks such as TensorFlow and MXNet.

SIMPLIFY YOUR WORKFLOW WITH PRE-TRAINED MODELS AND AN AI WIZARD

Use pre-trained networks such as VGG16, ResNet and Inception V3 or build your own. Complete networks can be created in seconds with an AI Wizard.

IMPROVE AI GOVERNANCE, COMPLIANCE, AND SECURITY

Manage your models in one platform from experiment to training, testing, and deployment.

AutoML + HYPERPARAMETER TUNING

Deep Learning Studio can automagically design a deep learning model for your custom dataset thanks to our advance AutoML feature. You will have good performing model up and running in seconds.

Our platform is now available for everyone without any subscription cost. We have recently made Deep Learning Studio free for everyone and with single account you can access both cloud as well as desktop software. We are also giving away 2 Hours of Free GPU compute time for new users and desktop users can use their local GPUs for free.

Deep Learning Studio is a deep learning platform for creating and deploying AI. The simple drag & drop interface helps you design deep learning models with ease. Pre-trained models as well as use built-in assistive features simplify and accelerate the model development process. You can import model code and edit the model with the visual interface. The platform automatically saves each model version as you iterate and tune hyper-parameters to improve performance. You can compare performance across versions to find your optimal design. We support transparent multi GPU training to speed up the training time.

Deep Learning Studio is available in both cloud and desktop version. With cloud account, you have an option to rent on-demand high powered GPUs to train the model. You will be able to soon publish your model as REST API on your server or Deep Cognition’s server. Desktop version runs on your local machine and utilizes machine’s GPUs for training. Both versions come with full featured drag and drop editor

AutoML: Our first version of AutoML will design an initial deep learning model on your dataset. This model may need further tuning to improve performance. Company is currently working on next version of AutoML which will explore multiple architectures & hyper-parameters to generate an optimized deep learning model for you

Deep Cognition is offering the tool at no subscription cost and creating a community of developers. Also, we will be offering pre-configured deep Learning environments for TensorFlow, MXNET and Keras for the developers who prefer coding over visual modeling.

High Performance Open Source Deep Learning Server

Deep Detect Versatile Machine Learning

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Templates for the best neural architectures

Deep neural networks with a proven track record are included as templates. These include Googlenet, Alexnet, ResNet, character-based nets for image and text classification.

Range of model quality assessment measures

Assessing your model quality made easy. From F1-score to multiclass log loss, measures and their history can be accessed during the learning phase.

Collection of input connectors

Handle large repositories of images with extreme ease. Massage and pre-process data from CSV files directly from the API prior to learning a statistical model.

Get state of the art results with no code involved

Classify images, detect objects, deal with text and numerical data from your application or the command line by series of simple calls to the deep learning server. 

Seamless switch between development and production 

Use one or more deep learning servers for development and production, test, move and reuse models, it has never been easier to bring the full machine learning cycle into production! 

Easy API and flexible template output formats 

A simple yet powerful and generic API for use of Machine Learning. It is simple to setup, test, and plug into your existing application.

DeepDetect is a deep learning API and server written in C++11. It makes state of the art deep learning easy to work with and integrate into existing applications. It has support for backend machine learning libraries Caffe, Tensorflow and XGBoost.

DeepDetect boasts the following features:

General

• High level & generic API for machine learning & deep learning
• JSON communication format
• Remote Python client library
• Embedded server with support for asynchronous training calls
• High performance, benefits from multicore CPU and GPU
• Flexible input / output connectors for text, images, raw data (CSV, SVM)
• Flexible template output format to simplify connection to external applications (e.g. Elastic search, …)
• No database dependency and sync, everything is organized on the filesystem

Machine Learning / Deep Learning

• Support for state of the art Deep Learning via Caffe and Tensorflow libraries, and decision trees via XGBoost
• Templates for the most useful neural architectures (e.g. Googlenet, Alexnet, NiN, mlp, convnet, logistic regression, ResNets, …)
• Range of pre-trained state of the art models for text and images
• Range of built-in model assessment measures (e.g. F1, multi class log loss, …)
• Support for multiple Machine Learning services, training and prediction calls in parallel
• Makes the most out of CPUs and GPUs
• Supervised learning, regression and prediction over images and other numerical and textual data, auto encoders, object detection, …

Data

• Built-in input connectors to ease the setup of a machine learning pipeline
• Easy management large datasets of images
• Easy management and preprocessing of CSV data files
• Connector to handle large collections of images with on-the-fly data augmentation (e.g. rotations, mirroring, …)
• Connector to handle CSV files with preprocessing capabilities
• Connector to handle sparse data in SVM format
• Connector to handle text files
• Output connectors for various external applications can be setup through templates via the API, without code (e.g. for Elastic search, XML, SQL, …)

Machine learning frameworks and algorithms supported by the server

• Caffe, one of the most powerful deep neural network frameworks. Caffe has support for many neural architectures, including logistic regression, multi-layer perceptron, convolutional networks, recurrent nets, etc…
  
• XGBoost one of the most regarded machine learning frameworks for a wide range of applications. Gradient boosted trees are a form of decision trees.
  

Big Compute HPC Platform for Enterprise Sector Projects

ENTERPRISE BIG COMPUTE PLATFORM

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Transform your on-premise HPC system with the ScaleX® platform and gain instant access to the world's largest high performance computing infrastructure in the cloud.

PROBLEM SOLVED, FASTER

Rescale’s ScaleX® platform helps solve the world’s most challenging engineering, scientific, and mathematical problems with unprecedented speed by leveraging HPC in the cloud.

TRANSFORMED IT AGILITY

Instantly shift workloads to the cloud and work in tandem with on-premise systems. Access the very latest cloud hardware including GPU and InfiniBand.

ON-DEMAND TURNKEY PLATFORM

Browser-based workflow with immediate access to over 250 applications, ported and tuned for HPC.

ACCELERATED TIME TO MARKET

No waiting in queues or schedulers for resources. Reduce turnaround times and access instant HPC capacity.

REDUCED CAPITAL EXPENDITURES

Pay-as-you-go for hardware and software. Avoid under-utilized on-premise machines and reduce costs through managed resource allocation.

Turnkey access to over 250 applications, ported and tuned for HPC.

Pay-as-you-go (on-demand) licensing available or you can bring your own license server. 

Rescale offers access to global data centers, the very latest HPC hardware and a complete library of engineering, scientific and mathematical software.

Deep Learning on Rescale

Deep Learning is a sub-field of machine learning that focuses on predictive models that have large numbers of parameters, typically organized as a layered computational graph. It is fast becoming the preferred model choice for large datasets with samples that have many features.

Rescale provides GPU-based HPC nodes and clusters for training deep learning models in the cloud. Rescale supports batch training of models as well as interactive data analysis through Rescale Desktops. A wide variety of GPU configurations are available from lower cost previous-generation K80s to the latest multi-GPU P100s with NVLink interconnect. Clusters can be preconfigured with your choice from the most popular deep learning frameworks.

Bring Your Own AWS Account

Bring Your Own Azure Account

By Industry

Aerospace

Automotive

Consumer Products

EDA / Electronics

Oil & Gas

Life Sciences

By Focus

Autonomous Driving

Automation (API, CLI)

CAE and MDO

Digital Twin

Driver Assistance (ADAS)

Disaster Recovery

IoT and Big Data

Machine Learning

SPDM

Scale yourself Open Source Self Service Deep Learning Frameworks in the Cloud powered by GPU

Deep Learning Frameworks in the Cloud powered by GPU

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vScaler enables anyone to quickly deploy scalable, production-ready deep learning environments via an optimized private cloud appliance. Spin up application specific environments with the appropriate Deep Learning frameworks installed and ready for use, including Tensorflow, Caffe and Theano. These frameworks are accelerated using the world’s fastest GPUs, purpose-built to dramatically reduce training time for Deep learning and Machine Learning algorithms and AI simulations.

Self Service Deep Learning Environments

vScaler empowers your end users to set up the environments they need for their work or research. With instant access to resources on-demand, our platform eliminates the need for system administration skills and allows researchers to concentrate on the task at hand.

NVMe Accelerated Storage

Modern GPUs used in AI and ML have an amazing appetite for data - up to 16GB/s per GPU. Starving that appetite with slow storage, or wasting time copying data back and forth is a waste of GPU cycles, which is why vScaler incorporates NVMe accelerate storage to ensure the most efficient use of our GPU resource.

Train your team

Our team of experts offer hands-on training in the latest AI and accelerated computing methods used to solve real-world problems. Designed for developers, data scientists, and researchers, our training can be delivered online or onsite via instructor-led courses.

Open Source

Built on Open standards, vScaler leverages a leading open source Infrastructure as a Service (IaaS) provider

Adaptable

Adapt our cloud solution to suit your needs – not something you can do with proprietary software!

Scalable

Scale-up or down with the touch of button as demand dictates, all under a single management portal.

Predictable

vScaler delivers predictable and dependable performance through design and optimization for all of your workload needs.

Big Data

Our interface takes all the administrative burden out of configuring a complex analytical cluster and software eco-system. Select from your preferred distribution (Hadoop, Cloudera, Hortonworks, MapR) and begin running data-intensive applications or IOT (Internet Of Things) analytics in minutes.

Tiered & Accelerated Storage

Leveraging multiple storage platforms, each with unique underlying technologies, vScaler can provide a wide range of solutions ideal for low latency, high performance data access. From DRAM to NVDIMMs, right through to traditional spinning disk, vScaler can deliver a tiered, optimal storage solution to eliminate bottlenecks.

High Performance Computing

Our finely tuned software enables users to deploy HPC clusters of any scale. The HPC-on-Demand product provides a compelling solution for many challenges of delivering flexible infrastructure for research computing.

Deep Learning

vScaler enables users to spin up a deep learning environment with all the appropriate DL frameworks installed and ready for use. These frameworks are also accelerated using the world’s fastest GPUs, purpose-built to dramatically reduce training time for Deep learning and Machine Learning algorithms and AI simulations.

Incredible! Cloud GPU Computing only 121 Euro/Month Windows or Linux

GRAPHICS PROCESSING UNITS (GPU) IN THE CLOUD

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Tired of rendering a video or drawing a 3D model on the resources of your own computer? Cloud4Y offers videographers and graphic designers to stop buying expensive graphics stations!

Buy a cloud with a powerful video card (GPU) today and start paying in a month, when the order has already been completed. Work with resource-intensive applications more convenient and cost-effective with Cloud4Y!

Expand virtualization to any user on the network. Deliver better graphics, improve productivity, and grant access to business critical applications anywhere.

Architects, Engineers & Contractors — Accessing computer-aided design (CAD) tools from the field makes for easier collaboration between in-house and field teams.

Graphic Designers — Experience smooth, rich multimedia applications, including 3D-intensive programs.

Healthcare Professionals — Log on to your desktop and view diagnostic imaging without slowing down network traffic from any workstation within any health network facility.

GPU FEATURES

Powerful 

Solve complex problems faster and with less power consumed than with traditional CPUs. 

Cost-Effective 

Choose the GPU that best meets your needs and pay only when you need it. 

Fully Integrated 

We use the latest virtualization platform VMware, fully supports graphics accelerators without the need to use third-party solutions.

Starting at 121 Euro /mo*

• 8 vCPU
• 1 Gb vGPU (NVIDIA Tesla M60)
• 14 Gb RAM
• 200 Gb SSD
• Windows \ Linux

FREQUENTLY ASKED QUESTIONS (FAQ)

1. How does it work?

The Cloud GPU service is provided as a personal workstation (VDI) that uses computing resources such as the CPU, RAM and cloud graphics (vGPU). You connect to this desktop from anywhere in the world over the Internet. 

2. What system software is used to provide this service?

VMware Horizon View is used to connect to the desktop. The Window Server 2016 operating system will be installed inside the desktop itself.

3. How modern should my computer be?

The performance of your PC is almost irrelevant, and it is important that you can install a desktop access program, namely VMware Horizon View, on your PC.

4. What type of communication channel is needed?

For normal operation, you need a stable 10MB/s Internet channel and higher with the Latency as low as possible. With high Latency values, you can see a visible delay in response to your actions with the mouse, keyboard, etc. and visible lags in dynamically changing images on your PC-screen. Where does the high Latency value come from: traffic pass from your PC to VDI through a large number of intermediate nodes, poor communication channels of your Internet provider and/or between intermediate nodes, high physical distance from your PC from our data center, etc. 

5. What Applications can I use on a remote desktop with graphics?

You can install absolutely any Applications at your decision, including "heavy" software - Sony Vegas Pro, GRAPHISOFT, Solidworks, Compass, Nanocad, T-Flex, Polymatics, as well as the entire line of Adobe CC (in particular, Adobe After Effects, Adobe Photoshop, Adobe Illustrator, Adobe Premiere, etc.), and Autodesk (AutoCAD 2017, AutoCAD Electrical 2017, AutoCAD Map 3D 2017, 3DS Max, Revit 2017, Inventor 2017, Vault 2017).

6. Is it possible to rent your cloud graphics by the hour?

No. This service can be purchased for at least 1 month.

7. How much does it cost?

On our web-site is indicated the cost of ready-made solutions with a strictly defined configuration. Also for your needs, our managers can prepare an individual configuration of the server (with individual pricing) with as much CPU, RAM, vGPU and disk space as you need by your request.

8. Can I change the server parameters?

Yes, of course. If you decide that you need more Gb of vGPU or processor cores while using, you have the option to increase these parameters in just a few minutes.

9. Is it possible to use your GPUs for crypto-currency mining?

Yes, but we do not recommend it. The revenue from mining is ghostly and uncertain, but the payment for our services is immediate and complete.

LIMITLESS ZERO-SETUP SCALE FAST AUTOMATE COMPLEX DATA PIPELINE MANAGEMENT DEEP LEARNING PLATFORM FOR DATA SCIENTISTS

Volahi DEEP LEARNING MANAGEMENT PLATFORM

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Machine Orchestration, Version Control and Pipeline Management for Deep Learning

THE DEEP LEARNING PLATFORM FOR DATA SCIENTISTS

TRACK EVERYTHING

We believe that effective version control is the only way to achieve reproducibility, regulatory compliance, an audit trail & quick results.

Whether from today, or 10 years from now, you’ll be able to select a deployed model and clearly trace back through its hyperparameters, training data, script version, associated cost, sibling models & even the team members involved in training it.

VISUALIZE AND MONITOR

You’ll see everything in real-time as your trainings progress, no longer stuck manually launching models and keeping track of CSV files. Get visual feedback on everything from a single model’s performance to a convergence of several parallel hyperparameter sweeps. See how your parameter sweeps are progressing while comparing competing models by accuracy, depth, or any custom parameter. You can also output custom parameters into stdout and see it graphically in the Valohai web interface.

INTEGRATE EVERYWHERE

Valohai works with any runtime you have and runs any machine learning code you write. Unlike other deep learning tools, we don’t tie you down to one vendor (not even to ourselves – even the configuration format is open source).

Run your TensorFlow, Keras, CNTK, Caffe, Darknet, DL4J, PyTorch, MXNet, or anything from bash scripts to C-code in your Docker wrapper of choice. Store your training data and labels in an Azure Blob, AWS S3 bucket, or your own FTP server. Access your code in any public or private Git repository and run it on your cloud or on-premises hardware of choice.

STANDARDIZED WORKFLOW

Valohai puts the same tools and industry-leading best practises at your fingertips used by powerhouses like Uber, Netflix, AirBnB and Facebook for managing their internal machine learning pipelines.

Valohai’s streamlined machine learning pipeline ensures that steps integrate together, regardless of who wrote the code or which language or framework was used. Generate images with Unity, transform in custom C-code, train with TensorFlow in Python, Deploy to a Kubernetes cluster. Everything works out of the box!

AUTOMATE COMPLEX DATA PIPELINES

Everything in Valohai is built API-first for easy integration of your ML pipeline into your existing software pipeline, e.g. through Jenkins or any other continuous integration platform.

POWERFUL MACHINE ORCHESTRATION

LIMITLESS PERFORMANCE

Valohai lets you scale up vertically and horizontally to do distributed learning and parallel hyperparameter sweeps at the speed of light (in an ethernet cable). Run your model in parallel on a hundred GPUs or tell Valohai to sweep through different hyperparameters to find the best model for your data in parallel on dozens of TPUs. Valohai is built for finding and optimizing your model for big data and immense models that scale with you, as you grow from data exploration to production.

ZERO-SETUP INFRASTRUCTURE

Train your models in the cloud or on your own server-farm with the click of a button, the call of an API, or a CLI one-liner. Valohai enables you to use the right amount of processing units - maximizing your results while saving time & money.

SCALE FAST AND WITHOUT EFFORT

Valohai supports massive-scale concurrency on top of AWS, Microsoft Azure, Google Cloud Platform & on-premises hardware (e.g. OpenStack). Just click a button and launch your code within Dockers containers on your hardware of choice.

AUTOMATE YOUR VERSION CONTROL

Fulfill regulatory compliance without added work. Valohai automatically tracks all your experiments, with a clear picture of how each model was trained, from data to parameters & statistics to algorithm. Rerun previous experiments anytime.

PIPELINE MANAGEMENT

Don’t worry about environments, configurations or shutting down servers when your training is done. Streamlined and expandable Valohai API allows you to concentrate on trials & mastering your models!

Serverless Deep Learning Saving up to 80%Scale Any Machine Learning Pipeline to Elastic Cloud Servers

Serverless Deep Learning SNARK HYPER 

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Scale any machine learning pipeline from a single server to hundreds of cloud instances with zero friction.

SNARK AI

With the exponential increase of training data and the computational complexity of machine learning models, Deep Learning on the cloud has become very engineering heavy. Single training experiment of a production ready model may take up to 2 weeks. If one wants to explore more variations or fine-tune hyper-parameters, production lifecycle becomes really slow. Picking the right instance, managing cloud instances for optimal utility rate, handling spot/pre-emptive instances, running multiple experiments at the same time, all require a lot of DevOps work from deep learning engineers, who may better spend their time developing models.

Scale Any Machine Learning Pipeline to Elastic Cloud Servers

Any Framework

Compatible with any machine learning framework. Run large-scale computation on PetaBytes of data with any R / Python / Java / C++ code.

Easy

Run any local machine learning pipeline on a fleet of cloud instances without any change in your code. Zero hassles in data migration and environment setup for new cloud servers.

Monitor

Cloud resource (CPU/GPU/RAM) utilization tracking and machine learning experiment analysis. Transparent overview of your cloud spending for each of your task.

Scalable

Unlimited persistent storage for all of your tasks. Never worried about hard drive filling up during the execution of your jobs. Scaling your task to thousands of CPUs and hundreds of GPUs with zero friction.

Separation of Persistent Data Storage and Elastic Compute Fleet.

• Your code base and data stay in persistent storage which is cost efficient (~20$/TB per month) and infinitely scalable.
• The persistent storage can be mount as local folders to each new cloud instance saving the trouble of data migration and environment setup.

Execute Parameterized Commands Across a Fleet of Cloud Instances

• Hyper-parameter Search. Run machine learning training with different hyper-parameters on different cloud instances.
• Batch Prediction. Run any trained model on Petabytes of data across a fleet of cloud instances in parallel. Instant model evaluation on any big dataset.
• Feature transformation. Compute features from Petabytes of raw data across thousands of cloud instances in parallel. Never limited to Spark libraries anymore. Use any R / Python / other package available.

Machine Learning Experiment Tracking and Insight Analysis

• Track and evaluate thousands of models and quickly surface the best-performing ones across your organization.
• Automatic model reporting tools.
• Monitor the CPU/GPU/RAM utilization of each cloud resource.

Enabling elastic ML compute to run big data ETL, feature transformations, machine learning and deep learning pipelines with any R/Python/Matlab/C++ code.

Up to 100x speed up for your machine learning pipeline

• Scaling any machine learning pipeline from a single server to an elastic group of 100 cloud instances to achieve 100x speed up.
• Scale your favorite R/Python packages to thousands of CPUs across hundreds of machines. Never limited to Spark libraries anymore for large scale computation.
• Typical use cases include hyper-parameter search, batch prediction, and feature transformation.

Save 2 hours/day for each machine learning engineer

• Saving ML engineers’ time for configuring cloud infrastructure, monitoring cloud resource utilization and ML environment setup in each new cloud instance.
• Let ML engineers easily create model reports from training logs.

Saving up to 80% on your cloud spending

• Choosing the most cost-efficient hardware from cross-cloud including AWS/Azure/GCP.
• Snark support Pre-emptible / Spot instances which are 70% cheaper than on-demand instances. Snark reschedules the jobs automatically for any spot interruption/instance pre-emption.

Get started with Snark Today