Results for “ml-experiment-tracking”
15 skills
majiayu000 ml
Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.
567 · bundle
neuralblitz mlflow
Manages the machine learning lifecycle with experiment tracking, model versioning, reproducible runs, and deployment through the MLflow platform.
1
orchestra-research mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.
10.4k · bundle
More results
qhjqhj00 mlflow
Track ML experiments, manage the model registry with versioning, deploy models, and reproduce experiments using MLflow's framework-agnostic platform.
3 · bundle
orchestra-research experiment-tracking-swanlab
Track ML experiments with open-source run logging, local or self-hosted dashboards, and media visualization using SwanLab.
10.4k · bundle
orchestra-research weights-and-biases
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B.
10.4k · bundle
paramchordiya mlops-and-infra
Enforces ML infrastructure, experiment tracking, reproducibility, model packaging, CI/CD, monitoring, and infrastructure-as-code standards at principal-engineer level.
0
jeffallan ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
sakamoto-family-smile mle-workflow
Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
0
mukul975 detecting-data-and-model-poisoning
Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.
24.6k · bundle
mhassan0000 mle-workflow
Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
1
leandrobenjaminl ml-modeling
Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.
0 · bundle
paramchordiya ml-engineering
Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.
0
phoroth langfuse
Instrument LLM applications with Langfuse for tracing, prompt versioning, evaluation, and dataset management across Python and JavaScript SDKs.
3
orchestra-research ml-training-recipes
Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.
10.4k · bundle