An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
machine-learning
deep-learning
data-science
python
tensorflow
mlops
pytorch
hyperparameter-optimization
hyperparameter-tuning
machine-learning-algorithms
model-compression
nas
neural-architecture-search
neural-network
automated-machine-learning
automl
bayesian-optimization
deep-neural-network
distributed
feature-engineering
Обновлено 2024-07-03 13:54:08 +03:00
Time Series Forecasting Best Practices & Examples
machine-learning
python
deep-learning
artificial-intelligence
r
best-practices
jupyter-notebook
automl
lightgbm
hyperparameter-tuning
model-deployment
prophet
retail
tidyverse
time-series
azure-ml
demand-forecasting
dilated-cnn
forecasting
Обновлено 2023-05-01 00:54:37 +03:00
Peregrine is a workload optimization platform for cloud query engines. The goal of Peregrine is three-fold: 1. make it easier to ingest and analyze query workload telemetry into a common engine-agnostic representation, 2. help developers to quickly build workload optimization applications to reduce overall costs and improve operational efficiency, and 3. providing better experience to the customers in the form of workload insights, actionable recommendations, and self-tuning capabilities.
Обновлено 2020-08-31 07:53:14 +03:00