Alternatives to Neural Magic

Fraud.net is the world's leading infrastructure for fraud management. It is powered by a sophisticated collective Intelligence network, world-class AI, and a modern cloud-based platform that assists you: * Combine fraud data from all sources with one connection * Detect fraudulent activity in real-time for transactions exceeding 99.5% * Uncover hidden insights in Terabytes of data to optimize fraud management Fraud.net was recognized in Gartner's market guide for online fraud detection. It is a real-time enterprise-strength, enterprise-strength, fraud prevention and analytics solution that is tailored to the needs of its business customers. It acts as a single point-of-command, combining data from different sources and systems, tracking digital identities and behaviors, then deploying the most recent tools and technologies to eradicate fraudulent activity and allow transactions to go through. Get a free trial by contacting us today

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

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.

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 Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

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.

Neuralhub is an AI system that simplifies the creation, experimentation, and innovation of neural networks. It helps AI enthusiasts, researchers, engineers, and other AI professionals. Our mission goes beyond just providing tools. We're creating a community where people can share and collaborate. We want to simplify deep learning by bringing together all the tools, models, and research into a collaborative space. This will make AI research, development, and learning more accessible. Create a neural network by starting from scratch, or use our library to experiment and create something new. Construct your neural networks with just one click. Visualize and interact with each component of the network. Tune hyperparameters like epochs and features, labels, and more.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

Abacus.AI is the first global end-to-end autonomous AI platform. It enables real-time deep-learning at scale for common enterprise use cases. Our innovative neural architecture search methods allow you to create custom deep learning models and then deploy them on our end-to-end DLOps platform. Our AI engine will increase user engagement by at least 30% through personalized recommendations. Our recommendations are tailored to each user's preferences, which leads to more interaction and conversions. Don't waste your time dealing with data issues. We will automatically set up your data pipelines and retrain the models. To generate recommendations, we use generative modeling. This means that even if you have very little information about a user/item, you won't have a cold start.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

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.

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.

The best platform for the entire lifecycle of computer vision. You can go from image annotation to precise neural networks in 10x less time. Our best-in-class data labeling software transforms images, videos, and 3D point clouds into high-quality training data. You can train your models, track experiments and visualize the results. Our self-hosted solution guarantees data privacy, powerful customization capabilities and easy integration into any technology stack. Computer Vision is a turnkey solution: multi-format data management, quality control at scale, and neural network training in an end-to-end platform. Professional video editing software created by data scientists for data science -- the most powerful tool for machine learning and other purposes.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

DataMelt, or "DMelt", is an environment for numeric computations, data analysis, data mining and computational statistics. DataMelt allows you to plot functions and data in 2D or 3D, perform statistical testing, data mining, data analysis, numeric computations and function minimization. It also solves systems of linear and differential equations. There are also options for symbolic, non-linear, and linear regression. Java API integrates neural networks and data-manipulation techniques using various data-manipulation algorithms. Support is provided for elements of symbolic computations using Octave/Matlab programming. DataMelt provides a Java platform-based computational environment. It can be used on different operating systems and programming languages. It is not limited to one programming language, unlike other statistical programs. This software combines Java, the most widely used enterprise language in the world, with the most popular data science scripting languages, Jython (Python), Groovy and JRuby.

Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

Deci's deep learning platform powered by Neural architecture Search allows you to quickly build, optimize, deploy, and deploy accurate models. You can instantly achieve accuracy and runtime performance that is superior to SoTA models in any use case or inference hardware. Automated tools make it easier to reach production. No more endless iterations or dozens of libraries. Allow new use cases for resource-constrained devices and cut down on your cloud computing costs by up to 80% Deci's NAS-based AutoNAC engine automatically finds the most appropriate architectures for your application, hardware, and performance goals. Automately compile and quantify your models using the best of breed compilers. Also, quickly evaluate different production settings.

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.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

ConvNetJS is a Javascript library that allows you to train deep learning models (neural network) in your browser. You can train by simply opening a tab. No software requirements, no compilers, no installations, no GPUs, no sweat. The library was originally created by @karpathy and allows you to create and solve neural networks using Javascript. The library has been greatly expanded by the community, and new contributions are welcome. If you don't want to develop, this link to convnet.min.js will allow you to download the library as a plug-and play. You can also download the latest version of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create an index.html file with no content and copy build/convnet.min.js to that folder.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

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.

Neuton.AI, an automated solution, empowering users to build accurate predictive models and make smart predictions with: Zero code solution Zero need for technical skills Zero need for data science knowledge

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.

Fido is an open-source, lightweight, modular C++ machine-learning library. The library is geared towards embedded electronics and robotics. Fido contains implementations of reinforcement learning methods, genetic algorithms and trainable neural networks. It also includes a full-fledged robot simulator. Fido also includes a human-trainable robot controller system, as described by Truell and Gruenstein. Although the simulator is not available in the latest release, it can still be downloaded to experiment on the simulator branch.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

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.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

NeuralTools is a data mining program that makes accurate predictions based on patterns in your data. It uses neural networks in Microsoft Excel to create sophisticated predictions. NeuralTools mimics brain functions to "learn" structure and make intelligent predictions. NeuralTools allows your spreadsheet to "think" for yourself like never before. A Neural Networks analysis involves three steps: training the network using your data, testing it for accuracy and making predictions using new data. NeuralTools automates all of this in a single step. NeuralTools updates your predictions automatically when input data changes. This means you don't need to manually run predictions each time you get new data. Combine NeuralTools with Excel's Solver or Palisade’s Evolver to optimize difficult decisions and reach your goals like no other Neural Networks packages can.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

Build and deploy deep-learning models for custom use scenarios. Our automated machine-learning process allows customers create powerful AI solutions based on our best-in class models and tailored to their specific challenges. Digital platforms can quickly create custom models that fit their guidelines and requirements. Build large language models to support specialized use cases, such as bots for customer and technical service. Create image classification models for better understanding image libraries, including search, organization and more.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Alfi, Inc. engages in creating interactive digital out-of-home advertising experiences. Alfi uses artificial intelligence and computer vision in order to better serve ads. Alfi's Ai algorithm, which is proprietary to the company, can detect subtle facial cues and perceptual details in order to determine if potential customers are a good candidate for a product. The automation is completely anonymous and does not track, store cookies or use identifiable personal information. Ad agencies can access real-time analytics data, including interactive experiences, engagement, sentiment and click-through rates that are otherwise unavailable for out-of-home advertisers. Alfi, powered AI and machine learning, collects data that allows for better analytics and relevant content to improve the consumer experience.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

The Peltarion Platform, a low-code deep-learning platform that allows you build AI-powered solutions at speed and scale, is called the Peltarion Platform. The platform allows you build, tweak, fine-tune, and deploy deep learning models. It's end-to-end and allows you to do everything, from uploading data to building models and putting them in production. The Peltarion Platform, along with its predecessor, have been used to solve problems at NASA, Dell, Microsoft, and Harvard. You can either create your own AI models, or you can use our pre-trained ones. Drag and drop even the most advanced models! You can manage the entire development process, from building, training, tweaking, and finally deploying AI. All this under one roof. Our platform helps you to operationalize AI and drive business value. Our Faster AI course was created for those with no previous knowledge of AI. After completing seven modules, users will have the ability to create and modify their own AI models using the Peltarion platform.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

A powerful, flexible, intuitive framework for neural networks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be used on multiple GPUs without much effort. Chainer supports a variety of network architectures, including convnets, feed-forward nets, and recurrent nets. It also supports per batch architectures. Forward computation can include any control flow statement of Python without sacrificing the ability to backpropagate. It makes code easy to understand and debug. ChainerRLA is a library that implements several state-of-the art deep reinforcement algorithms. ChainerCVA is a collection that allows you to train and run neural network for computer vision tasks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be run on multiple GPUs without much effort.

The Accord.NET Framework combines a.NET machine-learning framework with audio and image processing library completely written in C#. It provides a complete framework to build production-grade computer vision, signal processing, and statistics applications, even for commercial use. The extensive set of sample applications provides a quick start for getting up and running quickly. A detailed documentation and wiki help fill in the details.

GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding.