Best Deep Learning Software of 2024

Interplay Platform is a patented low-code platform with 475 pre-built Enterprises, AI, IoT drag-and-drop components. Interplay helps large organizations innovate faster. It's used as middleware and as a rapid app building platform by big companies like Circle K, Ulta Beauty, and many others. As middleware, it operates Pay-by-Plate (frictionless payments at the gas pump) in Europe, Weapons Detection (to predict robberies), AI-based Chat, online personalization tools, low price guarantee tools, computer vision applications such as damage estimation, and much more.

Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses. Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you.

A class-agnostic, pixel-perfect automated annotation platform. Built for teams that have a lot of data and strict quality requirements but little time. Ground truth creation can be scaled up 10x. Collaborate with unlimited team members, annotators and seamlessly integrate into your deep learning pipeline. Create ground truth 10x faster with pixel-perfect annotations. Use V7's intuitive tools for labeling data and automating your ML pipelines. The ultimate image and Video Annotation Solution

OTO gives call centers visibility to all customer calls within 20 hours. In-call intonation analytics can be used to complement your NPS score. Identify the call agent engagement and set your WFM plan. Quickly pick calls for Quality Assurance. OTO is language-independent and allows you to output parameters from different angles. Our API allows companies to quickly analyze 100% of in-call conversations. Start analyzing your call data by signing up for a free trial! Voice is the most important touchpoint between you, your customer, and yourself. We can help you understand and maximize your voice data at scale. Our lightweight DeepToneTM engine allows you to access our powerful voice models on any device. It also provides you with an acoustic layer for almost every audio format.

We make it easy for you to add superhuman computer vision into your applications, devices, and processes to give yourself an unassailable competitive edge.

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.

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.

FARO Sphere XG, a cloud-based digital platform for digital reality, provides users with a centralized collaborative experience across all of the company's 3D modeling and reality capture applications. Sphere XG, when paired with Stream, enables faster 3D data collection, processing, and project management anywhere in the world. Sphere XG is a systematized tool that allows users to organize 3D scans, 360-degree photos and 3D models. It also allows them to manage data from different teams around the globe. Sphere XG allows you to view and share 3D point clouds, 360-degree photo documentation, and floorplans all in one place. You can also track the progress of your project over time. Ideal for 4D progress management, where the ability of comparing elements over time is crucial, project managers and VDC manager can better democratize the data and eliminate the necessity to use two platforms.

Planisware Enterprise helps you to capture your strategy and align your portfolios, projects and teams in order to have an impact on your bottom line. Planisware Orchestra allows you to make project decisions across your entire portfolio. It also helps you reach the next maturity level. Planisware Enterprise integrates budgets, forecasts and schedules with resources and actuals. Planisware is trusted by global organizations such as Ford, Philips and Pfizer as well as dynamic mid-sized innovators like Zebra, Beam Suntory and MSA Safety to manage their project pipeline. Planisware helps you to define your strategy and evaluate the results using roadmaps, budgets, investment buckets, and other tools. Through simulations and investment scenarios, you can define, prioritize, manage, and monitor your portfolio of projects. Capacity planning, resource scheduling, time tracking, and visibility are some of the ways you can gain visibility into your resources and manage them. You can manage your projects by controlling costs, scheduling, and deliverable management.

Sia™ is transforming higher education by optimizing student lifecycle management, from admission to retention. This AI tool efficiently handles transcript processing, facilitating credit transfers, and increasing student retention rates. It analyzes students' academic backgrounds and preferences, providing tailored course and career suggestions, thus improving engagement and academic planning. As a virtual assistant on university portals, Sia™ makes information easily accessible, lightening staff workload and elevating the student experience. This innovative system redefines administrative tasks, offering effective, individualized assistance for student achievement.

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.

Public video data collection is becoming more risky due to the increasing capabilities of facial recognition technology. Brighter AI's Precision Blur allows for the most precise face redaction in the world. Deep Natural Anonymization, a privacy solution that uses generative AI, is unique. It creates synthetic facial overlays to protect individuals against recognition while maintaining data quality for machine-learning. You can use the Selective Redaction user interface to anonymize specific information in videos. Some use cases, such as media or law enforcement, do not require all faces to be blurred. After the automatic detections, it is possible to (de)select individual objects. Our Analytics Endpoint provides relevant metadata such as the original objects' bounding box locations, facial landmarks, and person attributes. You can retrieve relevant information using JSON outputs, while also having compliant, anonymized photos or videos.

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.

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.

Data platform for vision AI that is enterprise-grade. Dataloop is an all-in-one solution for building and deploying powerful computer-vision pipelines. It automates data ops, data labeling, data ops, customizing data pipelines, and weaving the human-inthe-loop for data validation. Our vision is to make machine-learning-based systems affordable, accessible, and scalable for everyone.

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.

The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed.

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.

D-I-Y can be used in certain situations, such as weed control. It's a different story to build your AI infrastructure. ONTAP AI consolidates the data center's worth in analytics, training, inference computation, and training into one, 5-petaflop AI system. NetApp ONTAP AI is powered by NVIDIA's DGX™, and NetApp's cloud-connected all flash storage. This allows you to fully realize the promise and potential of deep learning (DL). With the proven ONTAP AI architecture, you can simplify, accelerate and integrate your data pipeline. Your data fabric, which spans from the edge to the core to the cloud, will streamline data flow and improve analytics, training, inference, and performance. NetApp ONTAPAI is the first converged infrastructure platform to include NVIDIA DGX A100 (the world's first 5-petaflop AIO system) and NVIDIA Mellanox®, high-performance Ethernet switches. You get unified AI workloads and simplified deployment.

Cauliflower can process feedback and comments for any type of service or product. Cauliflower uses Artificial Intelligence (AI) to identify the most important topics, evaluate them, and establish relationships. Machine learning models in-house developed for extracting content and evaluating sentiment. Intuitive dashboards that offer filter options and drill-downs. You can use included variables to indicate language, weight, ID and time. In the dropdown, you can define your own filter variables. Cauliflower can translate the results into a common language if necessary. Instead of reading customer feedback sporadically and quoting individual opinions, define a company-wide language.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

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.

CerebrumX AI Powered Connected Vehicle Data Platform - ADLP is the industry’s first AI-driven Augmented Deep Learning Connected Vehicle Data Platform that collects & homogenizes this vehicle data from millions of vehicles, in real-time, and enriches it with augmented data to generate deep & contextual insights.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.