top of page
  • ridgerun

Retraining, Optimization, and Deployment: The RidgeRun.ai Approach to Machine Learning and Neural Network Modeling




How We Enhance Deep Learning Models for High-Quality Marketplace Deployment


“We’ve invested in talented engineers to develop AI models. The only problem is that our model runs very slow when running it on the target hardware.”


“We have invested a lot of time in our application but our deep learning model does not perform as effectively as we hoped for our use case.”


“We’re focused on research; we need help with production.”


“We want to use a community model for our product, but we need help on optimizing the model for our specific use case.”


These are some of the most common concerns that RidgeRun.ai clients bring to us, as they try to bring their machine learning model to market. At some point in the research and development process, engineers recognize that additional engineering support is necessary to ensure a model is ready for a specific use case.


At RidgeRun.ai our approach to this problem is simple. Our advantage lies in the ability to run your model efficiently on specialized hardware. Engineers who develop models, such as convolutional neural networks and transformer neural networks, are often accustomed to first get things done but not necessarily efficiently. Running a deep learning model efficiently in the cloud, in a desktop, or at the edge createscreate different sets of challenges and will drive the success of most marketplace-deployed designs.


Instead, we harness the expertise we’ve developed over the past two decades developing embedded software solutions for our parent company RidgeRun. We understand the critical hardware that artificial neural networks require for optimal execution, freeing up a significant bottleneck that prevents many engineers from moving forward with their projects. What’s more, our team of experts know how to help you get the most out of your model so you can experience marketplace success as soon as possible.


Getting a model running efficiently for production would typically require three main areas of focus:


  1. Train the model to perform with the desired accuracy for the target application.

  2. Optimize the model, so that we can reduce its footprint and execution time.

  3. Deploy the model by taking care of all possible hardware accelerators to make sure it will run at its best and provide an invaluable user experience.


RidgeRun.ai Streamlines and Improves Your AI Model


Our team applies a rigorous machine learning operations (MLOps) framework to transform your model into a marketplace-ready use case.


Whether your expertise lies in designing deep learning models, such as convolution neural networks and transform neural networks, RidgeRun.ai’s dedicated engineering team can accelerate your project for business success.


This process includes these powerful RidgeRun.ai services:


Model training: Most of our customers use well-known deep learning models that perform well on academic or experimental datasets; however, when using these models in their specific use cases and in real industrial scenarios, they find out the model does not perform as expected. In these scenarios, our team helps to fine tune and optimize the model for the customer’s use case, which involves retraining, transfer learning, and hyperparameter optimization, among other techniques.


Model conversion: We help our clients convert their model from generic frameworks to vendor-specific hardware from superior SoC manufacturers, including NVIDIA, Google, and Intel. We are prepared to discuss what framework will best suit your needs to match your target platform, including neural processing units (NPUs) and graphic processing units (GPUs).


Model quantization: Your model may operate efficiently in the research laboratory, but optimizing its speed and size prepares it for consumer use. This means that you may desire a tradeoff in which you “prune” your model to gain speed at the expense of precision. We can manage the pruning process to ensure your model runs as intended in real-world use cases.


Model distillation: In certain situations, a client may wish to adapt a larger model to train a smaller model for a more specific use case. For example, an artificial neural network that specifically detects certain objects, like pedestrians, can be distilled from a more general object detection model.


Model improvement and tuning: We are fluid with the latest development tools, including industry-standard PyTorch, TensorFlow, Keras, and more to further strengthen your development and its predictive qualities when required.


Model reproducibility: Performing at scale is absolutely essential to your model’s success. By using primary development tools such as DVC (data version control) and Git to improve your model with precision and accuracy, your model will be fully reproducible for project integration.


Model visualization: Visualization tools establish that your model is learning effectively and providing the precision you expect. These tools monitor your model throughout the training process by allowing us to view performance until it reaches the required level of accuracy.


Let Us Work As an Extension of Your Team For Machine Learning and Neural Network Development


Whether your goal is the development and deployment of a convolutional neural network, a transformer, or any machine or deep learning project you can imagine, RidgeRun.ai offers essential engineering support to bring your vision to life.


Along with our expert tools for successful deployment, you can expect:


  • Exceptional communication and transparency: RidgeRun.ai is an available teammate. We’re right beside you throughout the process to ensure the continual improvement of your project.

  • Precision and agility: We leverage our tools, resources, frameworks, and more to deliver the success you’re looking for. When we need to pivot and make key changes, we aren’t afraid of optimization to better adapt your product as necessary. We understand machine learning development and deployment is an evolving process that only works well for those who strive for excellence in real-time.

  • Dedication to your success: We commit ourselves to the work of deploying your product faster, so we can learn, make fixes, and grow as your use case gains widespread adoption. Successful AI partnerships need to be nurtured to produce excellent projects that make an impact. We believe that AI is only as effective as the consistent expertise that can be harnessed on its behalf.


Begin a partnership with RidgeRun.ai today to see your model make the transformation from development to deployment and add value as an effective tool in today’s tech marketplace.

15 views0 comments

Comentários


Os comentários foram desativados.
bottom of page