Learn What Distinguishes a Trusted Partner for Your AI Project
In a field that requires significant resources and a willingness to chart new territory, it is important to find an AI engineering team with significant experience. Your chosen team’s level of expertise will help determine whether you rise above the competition with your project.
Although there are several factors that distinguish exceptional AI engineering and consulting firms from others, vetting a team to find out their depth of understanding of critical AI concepts should be the starting point.
In this article, RidgeRun.ai, a leading AI engineering and consulting firm, explores two of these concepts, artificial neural network development and machine learning operations (MLOps).
Neural Network Development
A neural network is a structure for non-linear computing that is made of layers. Data is fed through a first layer, known as an input layer. A computer then uses one or more “hidden layers” in which it assigns weights to this data. This is another way of saying that the computer assigns meaning to the data it receives. The computer is trained on a significant amount of data until it can receive information it has never seen before and make a prediction about that new information. The more complex the data, the more hidden layers to make computations more advanced.
A neural network with hidden layers is often called an artificial neural network because of its similarity to how a brain functions. These hidden layers mirror the human brain’s ability to navigate disparate data sets and synthesize them to develop more complex understanding and, by extension, execute complex tasks. Classical machine learning algorithms frequently consist of fewer layers. When the neural network includes multiple layers, it's known as Deep Learning.
For instance, a task like object classification may have four layers, whereas a task like a user chatting with ChatGPT requires thousands of layers to perform functions like understanding the context of your conversation across multiple back and forth interactions - not to mention generating a single response.
The complexity of neural networks largely defines the different types of deep learning models that engineers use for their AI-based applications. These neural networks include:
Convolutional neural networks (CNN): A CNN is a particularly useful neural network for audio and video inputs. In the case of image recognition, a CNN processes the data it receives in layers, each of which work to extract features and common patterns from the image. One quality that makes CNNs more useful for images than typical machine learning models, is their ability to take advantage of the spatial nature of images in order to make predictions about them. Convolutional neural networks are widely used in a field of AI called computer vision, which enables tasks most consumers are familiar with, such as a computer’s ability to “see” the road in front of you to warn you about obstacles or to organize your phone’s photo album by the person they recognize in them. RidgeRun.ai is particularly adept with computer vision models and has developed significant use cases for this form of AI that can be applied across a wide range of industries.
Transformers neural networks (TNN): A TNN is another widely used deep learning model that aids in the development of natural language processing (NLP) use cases. A TNN is able to weigh multiple data inputs simultaneously to better contextualize data, reduce irrelevant data, and efficiently use computing resources for the data’s most significant aspects. TNNs have been successfully used to better interpret language, such as a search engine request. However, in the case of RidgeRun.ai’s expertise with computer vision, TNNs can understand the complexity of an image more effectively, during tasks such as classification, detection, and even image enhancement.
To perform the complex computing tasks that distinguish artificial neural networks, engineers may use neural processing units (NPUs), graphics processing units (GPUs), tensor processing units (TPUs) or deep learning accelerators (DLAs). These units are capable of processing incredible amounts of data for exceptional AI performance. RidgeRun.ai is a preferred partner of the GPU inventor NVIDIA and has significant experience using their system-on-chip (SoC) hardware to power machine learning and deep learning models.
Machine Learning Operations (MLOps)
MLOps is a set of machine learning and deep learning practices derived from a more familiar term in the tech industry, DevOps. The role of a development operations team in software is to create a seamless workflow to monitor reproducible model development, product quality, fix security and software issues, make updates, and release new versions. This process ensures the extension of a product’s lifecycle, due to its continued relevancy and optimized performance for users.
MLOps essentially performs the same tasks as a DevOps team. The important difference is that machine learning operations can include continued dataset training, so the artificial neural network is better informed and highly accurate when performing computer vision or deep learning tasks, among others.
RidgeRun.ai provides MLOps services on a consulting basis, alongside several additional service offerings. Our MLOps services provide intensive engineering support for your application, so that your AI model performs at optimal levels as expected, continues to integrate new learning, and offers the scalability you require for business success.
Think of RidgeRun.ai’s MLOps commitment as continued engagement with your project even after development, including perfecting, updating, and versioning your model. We’re the part of your team that many businesses wish they could find: a highly skilled group of engineers with a deep investment that spans your product’s entire lifecycle.
Partner with RidgeRun.ai for Proven Neural Network Engineering and Consulting
RidgeRun.ai’s depth of experience began in 2006, when parent company RidgeRun established itself as a leading development expert for embedded Linux and system-on-chip hardware. The expansive growth of edge computing and GPUs provided opportunities to deepen our expertise with machine learning and deep artificial neural network models, specializing in audio- and video-based AI.
We were quickly recognized as NVIDIA preferred partners and pioneers of the GStreamer framework, as we began serving a set of high-profile global clients on sophisticated project teams. It became clear that we possessed the expertise and resources to focus a division of our company solely on AI, and RidgeRun.ai was born.
RidgeRun.ai is now a premier engineering and consulting team for software developers and business leaders who want to experience success with their AI-based projects and require further expertise to create a high-performance product at a reduced time to market.
Ridgerun.ai thrives as an extension of your team, with capabilities that include developing, optimizing and deploying deep learning models, including vision transformers, language models, recurrent and convolutional neural networks, and MLOps for continuous integration and deployment.
We exist to support your accomplishments in the field of AI and offer unmatched engagement to bring your project to market. Contact the RidgeRun.ai team to discuss a partnership today. Send us a message online or call us in the U.S. at 1 (800) 798-6093.
Comments