Machine Learning & AI Prototyping

We rapidly prototype, validate, and deploy AI solutions—turning ideas into production-ready models in weeks, not months.

Service Tag

+ Proof-of-Concept (POC)
+ Model Training & Tuning
+ MLOps Pipelines
+ API & Edge Deployment

Technologies

+ Python / TensorFlow / PyTorch
+ Hugging Face / LangChain
+ MLflow / Kubeflow
+ AWS SageMaker / GCP Vertex

From hypothesis to high-impact AI—fast and reliable

At Humming Byte, we accelerate AI adoption with battle-tested prototyping. Our data scientists and ML engineers build end-to-end solutions—from data prep and feature engineering to model serving and monitoring—so you can test ROI before full investment.

We specialize in explainable AI, cost-effective training (spot instances, quantization), and seamless integration with your stack. Whether it's computer vision, NLP, recommendation engines, or generative AI, we deliver production-grade prototypes with clear success metrics.

1–3 weeks. Week 1: data assessment + baseline model. Week 2: iterative tuning + API. Week 3: dashboard + evaluation report. We use pre-trained models and transfer learning to accelerate.

Not always. We use semi-supervised, self-supervised, or active learning to minimize labeling. For NLP/computer vision, we leverage Hugging Face and foundation models (BERT, CLIP, Llama).

Yes—using TensorFlow Lite, ONNX, or TorchScript. We optimize for size (<10MB) and latency (<50ms) on mobile, IoT, or embedded. Includes quantization, pruning, and hardware acceleration (GPU/TPU).

We set up MLOps with MLflow, Prometheus, and Evidently AI. Track data drift, prediction skew, and performance decay. Auto-retrain triggers and canary deployments ensure reliability.

Yes—fine-tuning Llama, Mistral, or GPT via LoRA/PEFT. Build RAG pipelines, agents (LangChain), and evaluation frameworks (RAGAS). Deploy via vLLM, TGI, or serverless endpoints.