Machine Learning is one of the branches of artificial intelligence. The basic principle is that machines receive data and “learn” from it. It is currently the most promising AI-powered business tool. Machine Learning systems quickly apply knowledge gained from training on large datasets, enabling them to excel in tasks such as face recognition, speech recognition,
AI Development Services
Artificial intelligence, machine learning, and deep learning are integral parts of many enterprises, factories, and complex software. Artificial intelligence is making huge strides forward – from advances in self-driving vehicles and the ability to beat humans in games to automated customer service and full automation and decision-making across industries. Artificial Intelligence is a cutting-edge technology that is poised to revolutionize your business. Artificial intelligence development is also driving software development services, embedded software, IoT, and IIOT applications. Software developers are currently exploring new ways of programming that are more prone to deep learning and machine learning. ShuraCore provides software development services based on machine learning, reinforcement learning, and deep learning. To solve business problems, we use the following technologies, frameworks, and approaches:
Machine learning is one of the branches of artificial intelligence. The basic principle is that machines receive data and “learn” from it. It is currently the most promising AI-powered business tool. Machine learning systems quickly apply knowledge gained from training on large datasets, enabling them to excel in tasks such as face recognition, speech recognition, object recognition, translation, and many others. Unlike programs with hand-coded instructions for performing specific tasks, machine learning allows the system to learn to recognize patterns and make predictions independently.
Machine Learning Applications:
- You have data that you can structure and use to train machine learning algorithms.
- You would like to take advantage of AI to outrun the competition.
- Better machine learning solutions can help automate various business operations and leverage more capabilities in the future.
Deep learning is a subset of machine learning. It uses machine learning techniques to solve business problems by applying neural networks to choose the best adaptation model in industrial enterprise management. Deep Learning can be expensive and requires vast amounts of training data. This is because many parameters need to be tuned for learning algorithms to avoid false positives. For example, a deep learning algorithm might be instructed to “figure out” what the object model looks like. A large amount of modeling data must learn to distinguish between the control object’s behavior to do the training. Use cases for deep learning:
- You have a lot of data.
- You have to solve problems that are too complex for machine learning.
- You have enough computing resources and manage hardware and software to train deep learning neural networks.
Frameworks and Technologies
Machine learning and artificial intelligence are technological breakthroughs. AI software allows you to solve more and more complex problems. We solve AI problems for industries: Industrial Automation and Robotics, the Internet of Things, and Electronic Design Automation. The popularity of AI technologies is growing, which means that the demand for them is also increasing. The rise of AI drives a larger developer community and new AI frameworks that make learning and working easier. ShuraCore specialists use the following frameworks and technologies in their work:
The MLIR (Multilevel Intermediate View) project is a new approach to building a reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain-specific compilers, and merge existing compilers. MLIR is designed for a hybrid intermediate representation (IR) to support multiple different requirements in a single infrastructure.
The MLIR project defines a standard IR that brings together the infrastructure needed to run high-performance machine learning models in TensorFlow and similar ML environments. This project includes the application of HPC techniques and the integration of search algorithms such as reinforcement learning. MLIR aims to reduce the cost of new hardware implementation and improve usability for existing TensorFlow users.
MLIR brings together the infrastructure for high-performance ML models in TensorFlow. The TensorFlow ecosystem contains several compilers and optimizers that operate at multiple software and hardware stack levels. We expect the gradual adoption of MLIR to simplify every aspect of this stack. ShuraCore uses the MLIR project to design compilers for the following hardware platforms:
Deep learning is a subset of machine learning. It uses machine learning techniques to solve business problems by applying neural networks to choose the best adaptation model in industrial enterprise management. Deep Learning can be expensive and requires vast amounts of training data. This is because many parameters need to be tuned for learning algorithms
Edge computing consists of several methods that bring data collection, analysis, and processing to the network’s edge. It means that computing power and data storage is where the actual data collection takes place. Edge AI devices include smart speakers, smartphones, laptops, robots, self-driving cars, drones, and surveillance cameras that use video analytics. Edge AI Benefits:
The Jetson family of modules all use the same NVIDIA CUDA-X™ software and support cloud-native technologies like containerization and orchestration to build, deploy, and manage AI at the edge. With Jetson, we can accelerate all modern AI networks, easily implement new features, and use the same software for different products and applications. With Jetson, we
With the growing popularity of using machine learning algorithms to extract and process information from raw data, there was a race between FPGA and GPU vendors to offer an HW platform that quickly and efficiently runs resource-intensive machine learning algorithms. Since deep learning is used in most advanced machine learning applications, it is considered the
NVIDIA Jetson is the leading Edge AI computing platform used by over a million developers and companies worldwide. With cloud support across all NVIDIA Jetson products, intelligent machine makers and AI-powered embedded systems developers are empowered to develop and deploy high-tech software functions to edge devices in areas such as industrial automation and robotics, smart cities, and smart agriculture, Industrial Internet of Things,