We firmly believe that no AI that is meant to be accessible publicly can go without quality software, a mediator between the AI and users. The web app takes ages to load or constantly returns errors — people will likely use a weaker model but with a better application! What makes us so sure about it is Tensorway’s descent from custom software development company Anadea which delivered hundreds of solutions to all kinds of businesses. From our experience at Anadea, we know well what factors ensure the success of software for businesses. There’s really a lot to talk about, but we better leave it to our conversation!
Machine Learning, on the other hand, includes a range of algorithms and techniques that analyze and process data. These algorithms can be used for tasks such as classification, regression, clustering, and prediction and can be applied to various industries and applications.
Ultimately, the best approach will depend on the specific requirements of the task at hand.The best parameters of ML models are chosen under human supervision, meanwhile, DL models have more advanced optimization algorithms. ML model optimization involves selecting the best model parameters, whereas, in DL, only model hyperparameters are chosen, and then the model optimizes itself via a backpropagation algorithm.
Effective representations of visual, textual, or audio information allow deep learning models to be so effective in different tasks.
First of all, deploying your ML/DL model to production with Tensorway involves choosing the most suitable method based on your business needs.
API endpoint: This approach is versatile, enabling your model to process and return predicted outputs upon receiving API calls. It's great for diverse software ecosystems.
Web application: Ideal if you wish to make machine learning more accessible within your organization, especially for non-technical users, as it offers an intuitive graphical user interface.
Mobile application: For mobile-first businesses or applications with the need for offline functionality and low latency, deploying the model within a mobile app might be the best option.
Database: This method streamlines data management processes, perfect for businesses with complex data handling tasks or for accelerating predictions on large datasets.
Serverless platforms: Cost-effective for models with inconsistent usage patterns as they automatically scale and charge only for the compute time used.
Once the model is deployed, we continue to monitor its performance, handle updates, and manage scaling requirements, ensuring the model remains reliable and accessible.
We build software products to accommodate user growth by using technologies and architectures that adapt to increasing demands. At our AI software development solutions company, we also focus on creating systems that remain operative even in case of a component failure, providing uninterrupted service. Our team constructs software that integrates AI seamlessly, efficiently balancing loads and providing fault tolerance.