Chatbots such as Siri and Alexa have become unpaid personal assistants of individuals across the globe. They fire numerous questions to these chatbots and wonder how these machines can achieve such high levels of accuracy. Thanks to one of the most compelling areas of data science known as deep learning. Do not get confused between machine learning vs deep learning.
Recent developments in the field of deep learning technology take inspiration from how the human brain’s neural networks function. There is an upsurge in the amount of investment in the field of AI and deep learning.
According to a report by Extrapolate, the deep learning market revenue is projected to reach $18.1 billion by 2030, expanding at a CAGR (compound annual growth rate) of 34.6% between 2022 and 2030.
There are various companies that are investing heavily in this field to offer comprehensive tools for businesses to leverage deep learning technologies. In this blog, let us explore the top 10 deep learning companies.
Top 10 Deep Learning Technologies and Services Vendors in 2024
Following is the list of the best technology companies that offer deep learning tools and services to enhance their IT infrastructure:
1. NVIDIA
NVIDIA is a key player in the technology sector that provides a wide range of deep learning technologies and services. It is primarily focused on its robust GPUs (graphics processing units) designed for AI workloads. NVIDIA A100 Tensor Core GPU is one of the key offerings that helps improve deep learning training and inference. The company also provides NVIDIA DGX systems, which is an integrated AI system for deep learning research and development. NVIDIA's CUDA and cuDNN are the two software libraries that serve as a catalyst to accelerate deep learning applications. It provides organizations with a comprehensive cloud-native suite of AI and data analytics tools. Furthermore, the NVIDIA Clara platform is customized for healthcare AI applications.
2. Intel Corporation
Intel Corporation is one of the pioneer deep-learning technology companies that offer a suite of deep learning technologies and services. The products and services are designed to improve AI performance across multiple applications. Intel® Nervana™ Neural Network Processors (NNP) is a key product that is specially designed for deep learning training and inference. It delivers Intel® Xeon® Scalable processors for enterprises to improve their AI workloads and data center performance. Developers can leverage the Intel® Distribution of OpenVINO™ to deploy deep learning models throughout multiple hardware, such as CPUs ( central processing units), GPUs, and VPUs (vision processing units). Moreover, Intel integrates cutting-edge deep learning frameworks like TensorFlow and PyTorch into its AI Analytics Toolkit to optimize its architecture.
3. Qualcomm
Qualcomm delivers a vast range of deep learning technologies and services with a strong emphasis on mobile and edge AI applications. Qualcomm® AI Engine is one of the key products that has multiple hardware and software components embedded to accelerate AI tasks on devices. It is integrated with Qualcomm Snapdragon™ mobile platforms to improve on-device AI capabilities for smartphones, IoT devices, and automotive applications.
The company has a neural processing software development kit (SDK) that assists developers in improving and deploying deep learning models on Snapdragon-powered devices. TensorFlow, PyTorch, and ONNX are a few of the well-known AI frameworks offered by Qualcomm to deliver efficient model execution throughout multiple hardware configurations. It provides the AI Model Efficiency Toolkit (AIMET), which has advanced techniques such as quantization and pruning to improve neural networks for better performance and lower power consumption.
4. Google LLC
Google LLC provides a wide range of deep learning technologies and services using its extensive computational resources and advanced research. TensorFlow is an open-source deep learning framework and a key offering of Google that assists in executing various machine learning tasks. It allows developers to develop, train, and deploy deep learning algorithms across various platforms, right from cloud to mobile and edge devices.
Google’s Cloud AI offers an advanced infrastructure for enterprises to execute AI workloads, including tools like AI Platform for model training and deployment. AutoML is a service that enables developers to build tailored machine learning models with minimal coding. IT teams can leverage BigQuery ML to design and run machine learning models directly in SQL (Structured Query Language). Google has also made TPU (Tensor Processing Unit) accessible for users through Google Cloud. TPUs are custom-designed application-specific integrated circuits (ASIC) that improve the performance of machine learning models, especially for large-scale deep learning tasks.
5. Microsoft
Microsoft is a global leader in deep-learning technology. It offers an extensive suite of deep learning technologies and services, primarily through its Azure cloud platform. Organizations can benefit from Azure Machine Learning service that offers tools to develop, train, and deploy machine learning models. These algorithms support popular frameworks such as TensorFlow, PyTorch, and scikit-learn. Additionally, it provides capabilities for automated machine learning (AutoML), model interpretability, and MLOps (machine learning operations). DeepSpeed is a deep learning optimization library used by Microsoft for effective training for large-scale models. It is specifically designed for training transformer models that are essential for natural language processing tasks.
6. Meta, Inc.
Meta, Inc. offers a range of deep learning technologies and services that empower enterprises with its vast array of social media and virtual reality products. The Fundamental AI Research (FAIR) team at Meta has developed PyTorch, an open-source deep learning framework that is widely used in both academia and industry for developing machine learning models. The company uses deep learning to improve its products like Facebook, Instagram, and WhatsApp. Meta also makes significant investments to advance in the field of deep learning through projects such as DeepFace. It has facial recognition capabilities with the highest accuracy. The company is focused on advancing virtual reality (VR) and augmented reality (AR) technologies through its Reality Labs. It incorporates deep learning technologies to deliver immersive experiences in products like Oculus VR.
7. Amazon Web Services Inc.
Amazon Web Services (AWS) provides an extensive suite of deep learning technologies and services aimed to accelerate the development, training, and delivery of machine learning models. Amazon SageMaker is a fully managed service that allows developers and data scientists to design, develop, and deliver machine learning models faster. It supports popular deep learning algorithms like TensorFlow, PyTorch, and MXNet, offering embedded algorithms and tools for model tuning and deployment. Machine learning professionals can utilize AWS Deep Learning AMIs (Amazon Machine Images) to access the infrastructure and tools required to accelerate deep learning projects. It inherently has deep learning frameworks and is optimized for use on AWS EC2 (Elastic Compute Cloud) instances to ensure scalability and performance in training large models. AWS Inferentia is a custom chip designed for effective model inference and minimizing latency and cost. AWS Trainium is also a custom chip designed by AWS to deliver high performance for training complex deep learning models.
8. IBM Corporation
IBM is at the forefront of technology companies globally. The company provides various deep learning technologies and services through its IBM Watson platform, catering to both enterprises and developers.Data scientists and developers can use IBM Watson Studio, a comprehensive platform to collaboratively develop, train, and deliver AI frameworks leveraging frameworks such as TensorFlow and PyTorch. IBM Deep Learning as a Service (DLaaS) provides seamless access to deep learning models through IBM Cloud. IBM PowerAI is an advanced deep learning algorithm on IBM Power Systems. It is designed to offer quicker training times and effective scaling for AI workloads.
9. Samsung Electronics
Samsung Electronics develops deep learning technologies and integrates them into multiple products and services. The company primarily focuses on improving user experiences and efficiency in various domains. It has established various Samsung AI Centers globally, including Silicon Valley and Seoul. Samsung's AI Centers execute research and development in deep learning, natural language processing, computer vision, and robotics.
Bixby is Samsung's AI-powered virtual assistant that leverages deep learning to understand natural languages, recognize images, and personalize user interactions. The company’s smartphones, smart TVs, and home appliances have Bixby integrated to provide voice control and contextual recommendations. It has been building Neural Processing Units (NPUs), which are dedicated hardware accelerators that can enhance deep learning tasks.
10. Micron Technology
Micron Technology is a popular technology company known for its memory and storage solutions. It leverages deep learning technologies to improve its products and services. The company offers AI acceleration solutions with memory and storage capabilities designed for AI and deep learning workloads. It offers advanced DRAM (Dynamic Random Access Memory) and NAND (Not AND) flash memory products, which are essential for training and inference tasks in AI models.
Top 10 Deep Learning Companies in a Nutshell
The deep learning landscape is constantly evolving as we navigate through 2024. This technology has immense potential to overcome various complex challenges in multiple domains. There are various vendors that have made significant investments in the research and development of advanced deep learning technologies. This blog can serve as a guide for IT deep learning professionals and decision-makers to explore the top 10 deep learning companies in 2024 globally.
