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  Brand Page

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  How Kinetiq Works

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  Solution

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  Benefits of Kinetiq

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Zoom Link: https://tamu.zoom.us/j/98805883312

🌌 Kinetiq: The Superintelligence Cloud

From Raw Data to Trained Models in a Single Prompt

💡 Inspiration

Machine learning is undeniably powerful, but often inaccessible. Setting up environments, choosing the right model, and tuning hyperparameters requires expertise that creates a barrier for students, researchers, and small teams.

The Mission: We wanted to build a platform where anyone could go from raw data to trained model results with just a prompt—no local GPU required, no environment headaches, and no ML PhD needed.

🧠 What it does

Kinetiq is an AI-powered, cloud-native ML training platform. Users upload their dataset (CSV or image ZIP), describe their goal in plain English, and Kinetiq handles the rest:

• 🤖 AI-Generated Code: NVIDIA Nemotron analyzes the data and writes multiple, production-ready Python training scripts (e.g., Random Forest, MLP, CNN).
• ☁️ Remote Execution: The generated scripts are sent to a high-performance Vultr cloud server, freeing the user's machine.
• 📊 Comparative Results: Once training completes, users see a side-by-side comparison of model accuracy, training time, and other metrics—all without writing a single line of code.

To evaluate model performance, Kinetiq computes metrics like accuracy: $$\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$$

🛠️ How we built it

⚔️ Challenges we ran into

• 🔗 Remote Execution Reliability: SSHing into a server, piping dynamically generated Python code, and handling stdout/stderr across network boundaries was tricky. We had to carefully manage timeouts, escaping, and error propagation.
• 📝 LLM Output Consistency: Getting Nemotron to produce valid, runnable Python code with a consistent output format required iterative prompt engineering and robust regex parsing.
• 🚀 Data Synchronization: Uploading and managing large ZIP files across Object Storage required optimizing for speed by extracting on the server rather than handling files individually.

🏆 Accomplishments that we're proud of

• ⏱️ Zero-to-Results in Minutes: A user can go from a raw CSV upload to seeing trained model metrics in under 5 minutes, with no local setup.
• 🧬 Model Diversity: The LLM chooses different algorithms based on the actual data characteristics, considering feature count, data size, and task type.
• 💨 Fully Cloud-Native: The user's laptop never does any heavy lifting; all compute happens on demand in the cloud.

🔮 What's next for Kinetiq

Our current model training happens sequentially: $$\text{Total Time} = \sum_{i=1}^{n} T_i$$

We plan to transition to a Parallel Execution Model: $$\text{Total Time} = \max(T_1, T_2, \dots, T_n) + \epsilon$$

• 🎮 GPU Support: Enable CUDA-accelerated training on Vultr GPU instances for deep learning tasks.
• 🚀 Parallel Model Training: Run all generated models simultaneously to reduce total wait time.
• 🌐 Model Deployment: One-click deployment of the best-performing model to a live API endpoint.
• 👥 Collaboration Features: Share projects with teammates and track experiment history.

Built With

• css
• django
• docker
• nemotron
• nvidia
• postgresql
• python
• pytorch
• react
• tenserflow
• vite
• vultr