|12 months ago||2 days ago|
|MIT License||Apache License 2.0|
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We haven't tracked posts mentioning neptune-contrib yet.
Tracking mentions began in Dec 2020.
✨ 7 Best Machine Learning Experiment Logging Tools in 2022 🚀
2 projects | reddit.com/r/learnmachinelearning | 25 Sep 2022
[D] Who here are convinced that they have a really good setup that keeps track of their ML experiments?
2 projects | reddit.com/r/MachineLearning | 14 Sep 2022
JBCNConf 2022: A great farewell
6 projects | dev.to | 23 Jul 2022
She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis
Keeping Your Machine Learning Models on the Right Track: Getting Started with MLflow, Part 2
2 projects | dev.to | 21 Jul 2022
In our last post, we discussed the importance of tracking Machine Learning experiments, metrics and parameters. We also showed how easy it is to get started in these topics by leveraging the power of MLflow (for those who are not aware, MLflow is currently the de-facto standard platform for machine learning experiment and model management).
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
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
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
dvc - 🦉Data Version Control | Git for Data & Models | ML Experiments Management
guildai - Experiment tracking, ML developer tools
tensorflow - An Open Source Machine Learning Framework for Everyone
neptune-client - :ledger: Experiment tracking tool and model registry
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
gensim - Topic Modelling for Humans
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator