FOR DATA SCIENTISTS…

Perforated.ai empowers data scientists to build smarter, smaller, and more accurate neural networks with less effort and cost.  By automating the design and optimization process, Perforated.ai not only saves time and resources but also enhances model performance, making data scientists indispensable to their organizations.

  • Creating Neural Networks:

    • Unleash the power of Perforated AI to get the most out of your neural networks. Save countless hours on architecture design and parameter optimization, allowing you to focus on innovation and breakthrough solutions.

  • Building More Efficient Architecture:

    • With Perforated AI, effortlessly build more efficient neural network architectures. Our advanced algorithms ensure your models are not only smaller but also more accurate, giving you the edge in performance.

  • Being the Heroes of Sales and Customer Service:

    • Boost your team's impact with Perforated AI by delivering unparalleled accuracy in your models. Become the hero of sales and customer service by providing precise, reliable insights that drive customer satisfaction and retention.

How It Works Today Without Perforated AI

1. Manual Model Design:

  • Data scientists manually design neural network architectures, a process that involves trial and error to determine the optimal structure.

  • This often requires multiple iterations and consumes significant man-hours. In many cases, it results in time wasted. 

2. Parameter Optimization:

    • Optimizing input parameters is a painstaking process that involves running numerous experiments to find the best combination.

    • This requires substantial computational resources and time. Additionally, there is a threshold for how far this method can get you; at some point, the parameters are optimized, and further improvements become unattainable with this approach alone.

3. Scaling and Accuracy:

    • As models increase in size to achieve higher accuracy, the computational costs skyrocket.

    • Created models can reach the required accuracy for a solution, but the model is too large to deploy on restricted hardware.

    • Companies face high infrastructure costs and longer training times, making it difficult to scale efficiently.

4. Maintenance and Updates:

    • Updating and maintaining models to adapt to new data or changing conditions involves reworking the architecture and parameters.

    • This ongoing process further adds to the labor and computational costs. Scaling also has a threshold where eventually you just start overfitting rather than actually improving performance on test and validation datasets or in production systems.