Deep learning capabilities for data processing with minimal lead time

Nia Deep learning capability significantly reduces the amount of manual effort or lead time required to fine tune models for specific problems, thereby solving a prominent problem in AI lifecycle. The key aspects of Nia Deep Learning are its “unsupervised feature learning” and “deep learning” algorithms that can automatically learn feature representations from unlabeled data. Nia Deep Learning builds on algorithms like sparse coding and deep belief networks that can process huge volumes of unlabeled data to learn a good feature representation. These methods surpass others on a number of problems in vision, audio, and text.

Deep learning capabilities help overcome the limitations of ML methods when dealing with more complicated real-world applications involving natural signals such as human speech, natural sound and language, natural image and visual scenes. DL has enormous potential in many domains of healthcare, science, business and government.

Nia AI Platform has integrated Deep Learning libraries and simplified infrastructure management and deployment that allows for increased collaboration between teams and expedite time to market. Nia AI Platform has in-built security features to safe guard against adversarial attacks.

  • Unsupervised feature extraction and representation learning
  • Faster time to market
  • Design more powerful models for higher efficiency

Typical Industry Challenges

  • Traditional ML methods have some inherent limitations when processing real world data
  • Building Deep Learning Algorithms requires specialized Deep Learning Engineers and access to open source libraries
  • Implementing Deep Learning Algorithms in production environments require safeguards from adversarial attacks

How Nia Deep Learning Can Help

  • Faster Time to Market with integrated platform
  • Collaboration can enhance team work, cross pollination of talent and expedites development
  • Security provides safeguards in production environment