Overcome a Major Hurdle In the Development Process With Synthetic Data
One of the most significant bottlenecks during the process of creating an AI application is the time you must invest gathering data. Training your model is absolutely critical to ensure that your application performs as expected.
For example, suppose your application is meant to detect a person falling asleep, so that they can be alerted through an advanced driver-assistance system to prevent a crash. You’ll need to train the model on many instances of what drowsy behavior looks like across a diverse range of human beings, as no two human faces are alike. This training enables the model to more precisely recognize drowsiness and protect drivers, passengers, and their vehicles.
Businesses often don’t want to find out they need to invest more time in data training. Delays due to model training only seem to limit your expected time to market. However, data augmentation through synthetic data training can be a useful way to get around time constraints. With help from our deep learning and machine learning consulting services, your application can remain on an expedited timeline and still perform effectively for users.
What Is Data Augmentation?
Data augmentation is a method that machine learning and deep learning consultants use to extend an existing dataset by adding additional variation such as new images, rotation, noise, etc. An engineer will include synthetic instances of key data without affecting its performance, in fact, using synthetic data along with real data is widely encouraged. To continue our drowsiness detection example, synthetic data sets of CGI humans getting drowsy are just as effective as training a model with live human subjects. Our AI consulting team has seen clients significantly shorten their time to market by using synthetic data, as opposed to the long, arduous task of training “organically.”
It’s a hard pill to swallow when recognizing that data acquisition and curation comprises the majority of the time spent creating an AI application. Moreover, for some use cases, gathering such data is prohibited. However, with synthetic data augmentation, that process becomes far more manageable without losing out on performance. Synthetic data simply enhances the data that already exists to provide better training for your model.
The use of synthetic data for augmentation may even be preferable from an ethical point of view. For example, using computer-generated imagery disincentivizes developers from using human subjects for data training, thereby potentially impacting their privacy when that same data is used for another use case that the subjects did not consent to.
Further, in cases where the application must recognize a diverse array of human beings, synthetic data can ensure that you have training data that accounts for race, sex, age, ethnicity, and more. Comprehensive access to data is a helpful tool for combating biases that prevent your application from providing an effective solution for users.
How Our AI Consulting Team Supports Your Data Augmentation Initiative
Thorough data training is an important investment to ensure that your application is primed to deliver for users based on a significant depth of knowledge. Investing time in training distinguishes strong, impactful AI use cases from ones that can’t deliver innovations for customers.
RidgeRun.ai’s consulting team is committed to ensuring you have the data your project needs for success. Our services include:
Key relationships: Our deep learning and machine learning consulting company can connect you with a trusted partnership to acquire synthetic data that is useful for your application.
In-house synthetic data: Depending on your project’s scope and needs, our engineers can develop synthetic data. Check out our latest article on how we generated synthetic haze and fog for a diverse range of applications, including computer vision applications that can identify and remove haze for many applications like driver assistance and public safety.
Curation and vetting: The quality of the data you put in directly impacts the quality of your model’s output. Our team carefully reviews synthetic data to analyze its efficacy.
Harnessing your data: Retain our services for model deployment, optimization, and machine learning operations (also known as MLOps consulting).
Bring Your Project to Market With RidgeRun.ai
Our AI consultants help you remove significant challenges that get in the way of putting your application in front of users, no matter the stage of development it’s in. We provide deep learning and machine learning services that matter to software developers and business leaders today, including:
Model design.
Model training.
Data labeling.
Data processing.
Model tuning and optimization.
Converting your model for exceptional performance.
Leveraging generative AI for your use case.
Continuous improvement.
MLOps consulting.
We remain on the cutting edge of what application designers and businesses require to prepare you for the AI marketplace with authority and confidence. Contact our team today for high-touch, comprehensive AI consulting in deep learning and machine learning.
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