Why Pre-Trained Models Matter for Machine Learning
AI and machine learning is revolutionizing nearly every industry, but developing high-performance models from scratch can be a daunting task that demands significant computing resources and large datasets. Creating a new machine learning model from scratch can add months to the development of AI applications, which can negatively impact ROI.
Pre-trained models are powerful tools that accelerate model development, improve AI application performance, and make machine learning more accessible to a broader range of use cases and domains. Developers can use pre-trained models and other predefined building blocks as a starting point for bringing new AI solutions to market faster and cheaper.
In this article, we’ll dive deeper into the benefits of pre-trained models and how they can be used by developers to eliminate months of work. We’ll also discuss how to bring greater efficiency to the overall development of AI solutions.
The Benefits of Pre-Trained Models
A pre-trained model is a deep network model that was previously trained on a large dataset to accomplish a specific task, such as identifying cancer spots in medical images or detecting suspicious behavior in video surveillance feeds. This means these models have already learned meaningful features from vast amounts of data that can be a great starting point when developing AI applications. Developers can use the model as it is or use transfer learning to customize the model for new use cases.
Transfer learning leverages the knowledge encoded in pre-trained models for new, similar tasks with only a small amount of additional labeled data. Since pre-trained models are usually trained on large datasets curated by domain experts, developers can also leverage this expertise without having to create their own datasets. This makes pre-trained models useful in specialized domains where labeled data is scarce and creating labeled datasets is difficult.
In short, pre-trained models can act as a foundation for further training to develop innovative AI applications in a wide range of industries. These models greatly reduce the amount of data, computing resources, and time required to achieve good performance on a new task. Although training a model from scratch can take months, using a pre-trained model can significantly reduce this training time to just weeks.
Pre-trained models are also democratizing AI, with popular foundation models like GPT-4 making large-scale generative AI more accessible to a wider range of organizations. The barrier to entry barrier is becoming lower for researchers, developers, and practitioners who may not have access to enough computational resources or labeled data. Foundation models and other pre-trained models, therefore, are driving innovation and expanding the reach of AI applications.
Reducing Development Time with Machine Learning Platforms & Modular Hardware
While collecting data and training machine learning algorithms can be time-consuming, there are other reasons that developing AI-powered solutions from scratch takes so long. Once the model has been trained, it needs to be fine-tuned to meet performance targets and deployed into a machine learning pipeline. The hardware running the AI application also needs to be designed, configured, tested, and certified before it’s ready to go to market.
Machine learning platforms like NVIDIA Clara Guardian, Google TensorFlow, and Intel OpenVINO can greatly streamline the development of AI applications. Along with pre-trained models, these platforms provide transfer learning tools, inference engines, and many other out-of-the-box capabilities. By utilizing a machine learning platform, developers can avoid reinventing the wheel and focus on the unique aspects of the application. This can accelerate AI application development by up to 10x.
Along with software building blocks, technology developers can also use predefined hardware building blocks rather than starting from scratch. Instead of spending months designing a fully-custom device, technology developers can choose from hardware building blocks that are pre-tested, pre-certified, and optimized for machine learning use cases. Then the device can be branded and customized for specific requirements within a short period of time. This can reduce the typical hardware development from two years to just four months, which is 6x faster than starting from scratch.
For example, using hardware and software building blocks from AHEAD and NVIDIA, a computer vision healthcare application can be built 80% faster than designing, testing, certifying, manufacturing, and deploying the solution from scratch. By eliminating everything but the last mile of development work, developers can create an AI solution within a matter of months. This leads to substantial cost savings for technology developers, helping them achieve a much greater return on investment.
Contact AHEAD to learn more about using hardware and software building blocks to fast-track AI application development.