|4 days ago||7 days ago|
|MIT License||Apache License 2.0|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Reverse Engineering Video Game Stock Prices
1 project | reddit.com/r/quant | 14 May 2022
Prophet is a forecasting model made by Facebook.
ARIMA models are solid though
1 project | reddit.com/r/datascience | 10 May 2022
I like FB Prophet and LinkedIn's GreyKite
LSTM/CNN architectures for time series forecasting[Discussion]
3 projects | reddit.com/r/MachineLearning | 6 May 2022
Basic Time Series Prediction
1 project | reddit.com/r/learnmachinelearning | 30 Apr 2022
I like Meta's Prophet. It's a flexible model that I feel is easy to understand and explain to non-technical stakeholders. Implementations for R and python. It can be as simple as a univariate series, or accept exogenous explanatory variables (categorical & continuous). Seasonality is part of the model, out of the box. It also has decent default parameters if you're not looking to do a lot of tuning. Lastly, the documentation is quite thorough and approachable. https://facebook.github.io/prophet/
prophet: NEW Data - star count:14301.0
1 project | reddit.com/r/algoprojects | 23 Apr 20221 project | reddit.com/r/algoprojects | 22 Apr 20221 project | reddit.com/r/algoprojects | 21 Apr 20221 project | reddit.com/r/algoprojects | 20 Apr 20221 project | reddit.com/r/algoprojects | 19 Apr 20221 project | reddit.com/r/algoprojects | 18 Apr 2022
mlflow: Open source platform for the machine learning lifecycle
1 project | reddit.com/r/u_TsukiZombina | 16 May 2022
MLflow VS VevestaX - a user suggested alternative
2 projects | 12 May 2022
MLOps with MLflow on Kraken CI
2 projects | dev.to | 29 Apr 2022
Besides building, testing and deploying, Kraken CI is also a pretty nice tool to build an MLOps pipeline. In this article, it will be shown how to leverage Kraken CI to build a CI workflow for machine learning using MLflow.
Serving Python Machine Learning Models With Ease
4 projects | dev.to | 12 Apr 2022
For MLFlow users you can now serve models directly in MLFlow using MLServer and if you're a Kubernetes user you should definitely check out Seldon Core - an open source tool that deploys models to Kubernetes (it uses MLServer under the covers).
Data Science Workflows — Notebook to Production
7 projects | dev.to | 8 Feb 2022
But as you can imagine, tracking each experiment with Git can become a hassle. We’d like to automate the logging process of each run. The same as for large file versioning, many tools emerged in recent years for experiment logging, such as W&B, MLflow, TensorBoard, and the list goes on. In this case, I believe that it doesn’t matter with which hammer you choose to hit the nail, as long as you punch it through.
[D] Tips for ML workflow on raw data
2 projects | reddit.com/r/MachineLearning | 21 Jan 2022
Machine Learning adventures with MLFlow - Deploying models from local system to Production
1 project | reddit.com/r/learnmachinelearning | 22 Dec 2021
Its a bug with mlflow -> https://github.com/mlflow/mlflow/issues/3755 Keep the server on, open another terminal export MLFLOW_TRACKING_URI env variable, if on windows set the env variable.....should work.
Old guy programmer here, need to brush up on Python quickly!
13 projects | reddit.com/r/Python | 6 Dec 2021
mlflow for logging and visualizing ML model experiments
Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
4 projects | dev.to | 6 Dec 2021
So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure.
[D] 5 considerations for Deploying Machine Learning Models in Production – what did I miss?
3 projects | reddit.com/r/MachineLearning | 21 Nov 2021
Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
zenml - ZenML 🙏: MLOps framework to create reproducible pipelines. https://zenml.io.
scikit-learn - scikit-learn: machine learning in Python
dvc - 🦉Data Version Control | Git for Data & Models | ML Experiments Management
darts - A python library for easy manipulation and forecasting of time series.
greykite - A flexible, intuitive and fast forecasting library
guildai - Experiment tracking, ML developer tools
neptune-client - :ledger: Experiment tracking tool and model registry