Taylor AI

Provides a visual, form-based interface for non-ML practitioners to upload labeled datasets (CSV, JSON, or text formats), configure training hyperparameters (learning rate, batch size, epochs), and select base open-source model architectures without writing code. The platform abstracts away YAML configs, dependency management, and training loop implementation, translating UI selections into backend training jobs that execute on user-controlled infrastructure or managed cloud instances.

Executes training jobs on user-controlled infrastructure (on-premise servers, private cloud VPCs, or local machines) rather than Taylor AI's servers, ensuring training data never leaves the organization's network boundary. The platform provides containerized training environments (Docker images with pre-installed dependencies) and orchestration scripts that can be deployed to Kubernetes clusters, VMs, or bare metal, with encrypted communication back to the Taylor AI control plane for monitoring and artifact retrieval.

Hosts trained models as REST or gRPC APIs with built-in authentication (API keys, OAuth), rate limiting, request/response logging, and usage analytics (requests per day, latency percentiles, error rates). The platform provides SDKs for common languages (Python, JavaScript, Go) and handles scaling based on traffic, with optional caching for repeated requests and support for batch inference.

Provides tools to understand model predictions through feature importance analysis (SHAP, attention visualization), example-based explanations (similar training examples), and prediction confidence scores. For text models, the platform highlights which input tokens contributed most to the prediction; for classification models, it shows which features pushed the decision toward each class.

Enables multiple team members to collaborate on model training and evaluation with role-based access control (read-only, editor, admin), audit logging of all changes (training runs, model updates, configuration changes), and commenting/annotation on training runs and model versions. The platform tracks who made which changes and when, supporting compliance requirements and enabling teams to understand model development history.

Provides a curated catalog of open-source base models (LLaMA, Mistral, Falcon, BLOOM variants) that users can select for fine-tuning, with options to inspect and modify model architecture (layer count, attention heads, embedding dimensions) before training. The platform exposes model configuration as editable JSON/YAML, allowing users to create custom variants without forking the original codebase, and supports exporting modified architectures to standard Hugging Face format for portability.

Maintains a version history of trained model checkpoints, allowing users to compare metrics across training runs, revert to previous model versions, and manage multiple model variants (e.g., v1.0 for production, v1.1-experimental for A/B testing). The platform stores metadata (training date, hyperparameters, validation metrics, data version) alongside each checkpoint and provides APIs to query version history and download specific checkpoints for deployment or analysis.

Enables trained models to be exported to multiple inference-ready formats (Hugging Face Transformers, ONNX, TensorRT, vLLM) and deployed to various inference engines without retraining or format conversion. The platform provides inference APIs (REST endpoints or gRPC) that can be hosted on Taylor AI infrastructure or user-controlled servers, with support for batching, streaming responses, and hardware acceleration (GPU, TPU, CPU optimization).

Provides tools for importing raw text data, applying data cleaning transformations (deduplication, tokenization, format normalization), and optionally integrating with labeling services (crowdsourcing platforms, internal annotation teams) to generate labeled datasets. The platform validates data quality (checking for label imbalance, missing values, outliers) and provides statistics (dataset size, class distribution, token count) to help users assess whether their data is sufficient for training.

Evaluates trained models on user-provided test datasets and generates detailed performance reports (accuracy, precision, recall, F1, confusion matrices for classification; BLEU, ROUGE, perplexity for generation tasks). The platform supports custom evaluation metrics via user-defined Python functions and tracks performance over time as models are retrained, enabling detection of performance degradation or drift.

Implements parameter-efficient fine-tuning techniques (Low-Rank Adaptation, Quantized LoRA) that train only a small fraction of model parameters (1-5% of total) while keeping base model weights frozen, dramatically reducing memory and compute requirements. The platform automatically applies these techniques during training and stores only the small adapter weights, which can be merged with the base model at inference time or kept separate for modular deployment.

Automatically distributes training across multiple GPUs (data parallelism, tensor parallelism) or multiple machines (distributed training via PyTorch DDP or DeepSpeed) without requiring users to modify training code or understand distributed training concepts. The platform detects available hardware, configures communication backends (NCCL for GPU, Gloo for CPU), and handles gradient synchronization and checkpointing across nodes.

Reduces model size and inference latency through quantization (INT8, INT4, mixed precision) and pruning techniques, enabling deployment to edge devices (mobile, IoT) or reducing inference costs on cloud infrastructure. The platform provides post-training quantization (no retraining required) and quantization-aware training (QAT) options, with automatic calibration on representative data and validation to ensure accuracy loss is acceptable.