Model serving
From a checkpoint to a managed workload. Read about model revisions, preprocessing compatibility, bounded jobs, and the difference between isolated inference speed and complete service latency. These guides connect modality-specific behavior to practical execution and delivery decisions.
Begin with the architecture article, then review the generation, video, or speech workflow you intend to serve. Measure representative workloads, preserve configuration details, and test cancellation and failure. A service boundary should make a research capability easier to operate without disguising its limitations.
Text-to-Speech GAN APIs: Where the Vocoder Fits
Connect text processing, acoustic modeling, and waveform synthesis without confusing a GAN vocoder with a complete speech system.
Read the guide
Image Generation APIs: Seeds, Latent Controls, and Reproducibility
Expose controls that match the checkpoint, preserve artifact provenance, and evaluate diversity alongside visual quality.
Read the guide
Video Prediction GANs: Evaluate Possible Futures, Not Certainty
Define context, prediction horizons, temporal baselines, and sampling rules for a more inspectable video prediction workflow.
Read the guide
Designing a GAN API: From Checkpoint to Reliable Interface
Turn a GAN checkpoint into a clear, versioned interface with useful job states, output provenance, and realistic evaluation.
Read the guide