Machine Learning

Train and compare models in Classification and Regression Learner apps

Track trending Hugging Face models and datasets with notable shifts explained

Tune esp-ppq quantization settings to recover accuracy on ESP-DL targets

Upload models and datasets and run cloud training on the Ultralytics Platform

Diagnose YOLO training runs from results.csv, mAP gaps, overfitting and divergence

Train a custom LoRA or cloned voice on Scenario from a curated dataset

Orchestrate ML training, batch inference and RAG indexing with Prefect or Airflow

Set up QLoRA and full fine-tuning infrastructure with TRL, Axolotl and DeepSpeed

Autoscale LLM inference on Kubernetes with KEDA, GPU metrics and spot nodes

Turn a Mac mini into an always-on private local LLM server with remote access

Set model registry standards, approvals and lifecycle policies for AI releases

Serve ML models on Kubernetes with KServe and Triton, including canary rollouts

Design multi-tenant LLM hosting with isolation, quotas and billing attribution

Run local LLMs with Ollama and Open WebUI for private dev environments

Deploy vLLM for high-throughput inference with batching, quantization and an API

Check GPU and compute availability before experiments and stop runs that cannot start

Turn an experiment plan into implemented code, GPU runs and first results

Check progress of running experiments and collect their outputs when they finish

Deploy and run ML experiments on local, remote or serverless GPU machines

Watch WandB training metrics for NaN values, loss divergence and idle GPUs

Find ML training datasets across Hugging Face, OpenML, GitHub and paper references

Implement and run the main experiment for a selected idea against an accepted baseline

Plan and write experiment code with a judge feedback loop, then submit the run

Inspect LLM profiler traces for layer timings and kernel boundaries

Benchmark SGLang, vLLM and TensorRT-LLM to find the best serving command for a model

Parse SGLang and vLLM startup logs to estimate KV cache and max concurrency

Find kernel hotspots, fusion gaps and overlap in LLM serving profiler traces

Estimate FLOPs and MFU for LLM serving shapes with an operator-level template

Look up prior model PR history to guide SGLang and vLLM serving optimizations

Plan day-0 SGLang support for a new model with validation gates and a PR plan

Add layer labels and compact GPU lanes to a Torch Profiler Chrome trace

Fine-tune Gemma with SFT, DPO or RLHF on local hardware and export GGUF or LiteRT

Compare Japanese NLP libraries and models side by side in a criteria table

Search Japanese NLP tools, models, datasets and dictionaries by keyword or question

Build ML pipelines with preprocessing, tuning, evaluation and model explainability

Segment crop field images into soil, crop and weed classes with panoptic masks

Predict which students answer held-out diagnostic questions correctly

Score anomalous machine sounds from normal-only training clips, DCASE style

Caption images in Indigenous American languages from a few labeled examples

Write Python solutions to programming problems that pass hidden unit tests

Parse sentences into dependency trees using a Universal Dependencies treebank

Predict scientific properties, trends and outcomes with model-based estimates

Build classification, regression and clustering models with scikit-learn

Fit Cox models and random survival forests on censored time-to-event data

Run named entity recognition, POS tagging and dependency parsing with spaCy

Run HuggingFace transformer models for classification, NER and embeddings