Designed for Deep Learning - Tailored for Computer Vision
Allegro offers the first true end-to-end A.I. product life-cycle management solution with a focus on deep learning applied to computer vision. Allegro's suite of management and technical tools cover the entire development, production and deployment continuum.
Comprehensive biases elimination, synthetic data creation, probing and examination tools, and a version control system that correlates datasets to specific models.
Automate image and video annotation, distribute tagging tasks across teams and easily explore and refine datasets.
Distribute and automate training tasks, debug faulty predictions, compare results in real-time, and work with multiple frameworks in parallel.
Facilitate true teamwork within and between data scientists and engineers, constructed around automation tools.
Leverage detailed model performance gauging, debug detectors with flexible channel statistics and easily implement hyperparameter optimization.
Spawn continuously evolving "model offspring" optimized per edge-device, internal compute and physical environment, and seamlessly update deployed models on the fly.
Deep learning that keeps learning: continuously feed discrete specialized networks with real-world data streams and perpetually expand and refine your datasets and detectors.
Expedite tedious technical operations with cloud services integration, amalgamated deployment and load balancing tools.
Allegro transforms applied deep learning into a truly continuous learning process, independently evolving per edge-device based on its computing power and distinct environment.
Allegro aggregates and repurposes data from deployed edge-devices, separated into discrete data streams which accurately reflect each device's unique environment.
Allegro enables you to spawn model subsets per edge-device and continuously train each one with newly acquired data from the edge-device where it operates. Creating increasingly accurate personalized models which are built to run within the compute constraints of the respective edge device. Essentially, your edge-devices become smarter, each tailored to its own unique environment and resources.
Allegro ensures your data remains proprietary and private, allowing you to achieve better results with less data, and providing unprecedented transparency and version control.
Allegro provides you with comprehensive probing tools, enabling you to explore your datasets in ever finer details, allowing you to zero-in on points of interest.
Allegro&aposs multi-faceted version control tool provides you the ability to manage dataset versions as well as couple dataset versions to specific models and source code. Branch, merge, and run historical comparisons in real-time.
Mix and match data sets with detailed control. Create synthetic data optimized for your use cases leveraging in-flight augmentations with flexible optimizations.
Allegro's platform allows you to train your models on your proprietary datasets, without forcing you to share either with 3rd party providers. Maintain total IP ownership and control, ensuring your operations adhere to your company's regulations, as well as those of your customers.
Allegro optimizes development, deployment and scaling operations by automating repetitive and time-consuming tasks, and structuring teamwork.
Allegro's proprietary auto-annotation process replaces expensive and time-consuming manual image and video annotation operations.
Scale-up hyper-parameter optimization with automatic job creation and race conditions. Distribute jobs across GPU workstations and remote VMs with queue management.
Construct a workflow and orchestrate teamwork for dataset preparation, model optimization and deployment- scaffolded around Allegro's automation tools.
One-click deployment of experiments and test environments into Daemons and ready-to-install Containers, whether on-premises or on AWS/Azure/GCP.
Allegro simplifies the creation of post deployment network versions optimized for edge-devices, continuously refining each edge-device's network according to its own unique environment.
Allegro provides robust tools for network performance optimization. Allowing customers to use vendor-specific frameworks for target chipsets.
After detectors are created, Allegro allows to create network offspring dedicated per edge-device. Continuously refine and evolve each network with data gathered from each edge-device's own surroundings.
Allegro is tailored for computer vision including scene recognition and video comprehension. The platform integrates dedicated tools and features for computer vision needs.
Allegro enables the creation of superior products and unique sales propositions, as well as facilitates the establishment of recurring revenue streams.
Allegro brings to the deep learning economy what SaaS brought to the traditional software economy. Our solution transforms the creation of neural networks from a one-time deal to a perpetually improving system.
Allegro allows you to provide customers with unique features. With Allegro your customers can retain data privacy; continuously evolve, specialize and improve their deployments; all while optimizing neural networks for edge-devices.
Allegro's ability to engage each point on the Deep Learning Continuum, means your products are faster to customize, deliver and update. Allegro allows for more accurate time and task workload estimates, responsive event management, easier infrastructure scaling, continuous model improvement, and personalization per customer as well as per edge-device.
With Allegro, teams can finally bring their creativity and innovation to the foreground by increasing workforce and hardware utilization. Scale productivity with a comprehensive set of tools designed to significantly improve both time to market and performance at production scale.
Allegro provides orchestration, parallelization and automation tools, allowing efficient and optimized usage of expensive CPU\GPU\DSP resources.
Allegro transforms Data Scientists from a bottleneck to a leveraged resource. Engineers can leverage the best of class data scientist outputs to produce product derivatives at scale.
Our team management and collaboration tools, along with the automation of time consuming tasks, removes operational roadblocks and allows teams to focus on creativity and innovation.
Allegro negates the need to do heavy and costly data transfers for training and production needs.
With Allegro traditionally linear development processes become parallel ones. Furthermore, we transform research to engineering by merging data-scientists into product teams, resulting in maximal team performance.
Easily mold and shape existing models into tailor-fit robust products built to answer each customers' individual challenges and unique needs.