"Intel® processors offer the high performance, reliability, and stability that a2 requires for this mission-critical solution. We are also more than happy to join the Intel partner ecosystem and enjoy the technical and business support associated with the Intel brand."
Murat Mutlu, managing partner
“Our solution is designed to employ Intel®-optimized machine-learning hardware and software technologies to train, test, and operationalize a model to help detect COVID-19 and 14 other thoracic diseases using chest scans. We used the Intel DevCloud and Intel® Distribution of OpenVINO™ toolkit to optimize and deploy our machine learning models across multiple Intel® platforms. As a result, our team was able to accelerate prototyping and deployment at lower costs on the best performing Intel® architecture for our solution.“
Moloti Nakampe, Accrad research and development
“Because we serve customers with so many different needs, it’s important to quickly achieve the right balance of price and performance for each of our applications. Intel DevCloud lets us test multiple platforms in parallel. That’s a lot of time savings—and time is money—so it’s a no-brainer.“
Eduard Vazquez, research technical manager
"At Brightskies, we innovate the technology of tomorrow. One of the key domains we cover is autonomous driving. Our partnership with Intel aims at enabling autonomous driving on Intel® Xeon® processors. Intel DevCloud enabled a fast and efficient approach for the testing of our self-driving software stack on Intel® platforms, achieving the most optimal kick off for development activities even before receiving any actual hardware. We believe that, in the COVID-19 era, Intel DevCloud can enable advanced levels of software testing in the automotive domain."
Hossam Yahia, chief technical officer
“Because developers can quickly evaluate the performance of their applications in multiple edge computing systems by using Intel DevCloud, they can not only shorten the inspection time to go to market, they can also expect tremendous benefits in terms of investment and maintenance in verification equipment. We are confident that Intel DevCloud will accelerate and streamline operations and create new value for more IoT businesses and for more customers."
Tomohiro Nagao, senior manager, Healthcare Business Unit
“In the absence of a variety of hardware configurations, it is incredibly challenging to evaluate the performance of today’s AI models on CPU-driven edge compute boxes. Intel DevCloud hit the nail on the head when it provided us with the access and support needed to benchmark our AI models on CPUs with Intel® Distribution of OpenVINO™ toolkit. Since then, we have been using Intel Distribution of OpenVINO toolkit as a default option on appropriately-sized CPU edge compute boxes for our on-premise customer deployments."
Vijay Gabale, cofounder, leading partnerships, CPO and CTO
“Using the Intel DevCloud allows Luxonis* to iterate on our products seamlessly in the physical world, with real-world experimentation and data collection, as well as in the cloud environment for fast improvement of our neural models and computer vision flow. This results in our customers typically getting their proof of concepts up and running in less than one week and products maturing for market in just a few months. DevCloud allows us to evaluate performance without the need of hardware in hand, and can iterate across hardware, software, AI, training iterations, and overall performance easily. Leveraging these kinds of tools, we currently have over 100 customers building products off of our platform, which covers over 20 verticals, including e-mobility, cargo - air and ground, food processing, agriculture (in-field, farming and ranching), defense, safety systems (in manufacturing, oil/gas, etc.) to name just a few."
Brandon Gilles, chief executive officer, Luxonis embedded computer vision and AI
“With a high performance medical application like ours, a major share of the AI workloads are carried out by the Intel Distribution of OpenVINO toolkit. As our customers' platforms don't adhere to a single hardware configuration, it is crucial that we assess various hardware platforms in advance so we can verify that the performance falls within our specifications. Intel DevCloud serves this exact purpose and enables us to benchmark our AI workloads on a variety of platforms not found on premise.“
Roee Shibolet, vice president of research and development
"At Neurolabs, we provide computer vision solutions powered by synthetic data. Our clients expect robust on-premise deployments with fast prediction times. This is where Intel Distribution of OpenVINO toolkit shines, giving us greater flexibility over different hardware configurations and standardizing the model deployment. Intel DevCloud enables us to quickly benchmark our models across multiple architectures and determine the best option for our customer use cases."
Patric Fulop, cofounder and chief technology officer
Nihon System Kaihatsu
"By benchmarking our trained model on Intel® hardware enabled on the Intel DevCloud, we could accelerate the product development and validation time. It is highly recommended to use Intel DevCloud for AI solution development."
Masaki Ishihara, chief engineer
"Rosmart provides automatic defects detection machines to do the visual inspection work. It can always keep the high quality standard, high inspection efficiency, and low labor cost."
Alex Zhang, director of research and development
"Because of the user experience we are trying to achieve, it is important to find the perfect balance of price and performance for each of our products. Though Intel DevCloud and its well prepared tools and environment, we were able to decrease the time it took to do a wide array of hardware tests on our interactive digital signage solutions, like Intelligent Label."
Ryota Tone, business development manager, Content Business Division
"Sightcorp's deep learning audience measurement products are hardware agnostic; therefore, it is very important for us to be able to rapidly test and iterate multiple Intel® platforms in parallel while still maintaining the inference speed needed to aggregate quality data in real-time. Intel DevCloud allows all of this to happen without dealing with physical hardware, which is perfect for the time we are currently living in and time spent working from home. For our customers, we strike the right balance between price and performance for each of our products so they can go to market faster and with a clear understanding of device capabilities."
Joyce Caradonna, chief executive officer
"We are pushing speech technology into emerging markets where there is a lot of language diversity and a variety of hardware challenges. That means that we need to test a lot of models (for many languages and accents) on edge devices. Intel DevCloud gives our data scientists a way to quickly spin up a notebook and test our models in a diverse set of scenarios. We can then translate these learnings directly into real product modifications and configuration, such that our customers are happy with the performance of our multilingual dialogue products right out of the box!"
Daniel Whitenack, data scientist
"As a member of the Intel® AI: In Production Program, we use Intel DevCloud and Intel Distribution of OpenVINO toolkit extensively, where CPU-run engines that are optimized with the latter are currently in production. So far, we have used DevCloud primarily for benchmarking purposes, which enabled us to determine the optimal edge hardware configurations, algorithmic choices and engine parameters, production-level load estimations and scalability assessment under different production scenarios. With our solution, the results obtained from the exercise in [Intel] DevCloud show that computing deep neural networks on CPU has become on par with GPU in terms of performance, and it is constantly improving with newer tools like int8 quantization, making advanced edge inference solutions like ours feasible with CPU. Using OpenVINO toolkit, the per image inference time speeds up by 10x as compared to unoptimized CPU execution, which allows us to run our solution as a real-time IoT application operating on Intel Xeon processors at multiple stores."
Erdem Yoruk, chief scientist
"Intel DevCloud provided confidence that our solution will be able to accommodate the number of cameras that customers have in their environment, and it helped remove guesswork from the pilot process."
Kamal Mannar, head of applied intelligence
"WonderStore uses Intel DevCloud to train models with data sets of over 30,000 pictures in half the time vs. on-premise servers. Intel DevCloud allows us to choose our hardware configuration to optimize training, enabling us to create vertical CV models for each customer’s needs."