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Quickstart Guide

Build and deploy custom SOTA fine-tuned models to serverless Google Cloud TPU infrastructure in minutes.

Welcome to the AlphaDeep Documentation page. This guide will walk you through the core platform web interface and the 4-step pipeline to build, test, and serve custom models on Google Cloud TPUs.

1. Platform Web Workflow

The AlphaDeep platform guides you through a seamless, 4-step pipeline to take raw datasets from upload to highly optimized serverless endpoints:

Step 1: Upload Data

Securely upload raw training images, annotations, or documents to our secure enclaves. The AI Engineering Agent parses and structures the datasets automatically.

Figure 1: Uploading and managing raw datasets in the developer console.

Step 2: Review & Annotate

Inspect labels, bounding boxes, or text annotations. Modify dataset errors using our automated clean-up assistant and integrated human-in-the-loop validation tools.

Figure 2: Reviewing data tags and fine-tuning parameters.

Step 3: Fine-Tune Adapter

Launch training jobs on shared Google TPU Pod clusters. The AI Agent automatically runs hyperparameter searches and provides real-time training analytics.

Figure 3: Monitoring loss convergence and parameter metrics in real-time.

Step 4: Serve on TPUs

Deploy instantly to high-throughput endpoints. Test predictions live using our interactive model playground to evaluate performance before production rollout.

Figure 4: Testing generated model results inside the playground console.

2. Billing & Cost Optimization

Because the open-source Blinx Kernel hot-swaps weights in microsecond-scale timelines, you pay strictly per request for inference. Endpoints automatically scale down to zero when idle, meaning zero billing overhead for cold resources.