Prophet
MLflow
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Prophet | MLflow | |
---|---|---|
88 | 27 | |
14,469 | 11,867 | |
1.6% | 2.9% | |
6.4 | 9.8 | |
4 days ago | 7 days ago | |
Python | Python | |
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.
Prophet
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Reverse Engineering Video Game Stock Prices
Prophet is a forecasting model made by Facebook.
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ARIMA models are solid though
I like FB Prophet and LinkedIn's GreyKite
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LSTM/CNN architectures for time series forecasting[Discussion]
Prophet
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Basic Time Series Prediction
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
MLflow
- mlflow: Open source platform for the machine learning lifecycle
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MLflow VS VevestaX - a user suggested alternative
2 projects | 12 May 2022
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MLOps with MLflow on Kraken CI
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.
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Serving Python Machine Learning Models With Ease
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).
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Data Science Workflows — Notebook to Production
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
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Machine Learning adventures with MLFlow - Deploying models from local system to Production
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.
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Old guy programmer here, need to brush up on Python quickly!
mlflow for logging and visualizing ML model experiments
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Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
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.
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[D] 5 considerations for Deploying Machine Learning Models in Production – what did I miss?
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