MLflow Alternatives
Similar projects and alternatives to MLflow
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Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
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Scout APM
Scout APM: A developer's best friend. Try free for 14-days. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.
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Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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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.
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zenml
ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.
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neptune-client
Neptune client library - integrate your Python scripts with Neptune
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
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handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
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MySQL
MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
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catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
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onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
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dagster
An orchestration platform for the development, production, and observation of data assets.
Reviews and mentions
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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.
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[P] DagYard - DVC x MLflow x Colab x Gdrive - Automatically Configured
MLflow tracking automates the logging process of experiments and sends live information to a local or remote server while the training is still running.
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Data Science toolset summary from 2021
MLflow - https://mlflow.org/
- How to store preprocessing and feature engineering pipeline?
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MLOps project based template
ML workflow - MLflow
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[D] Facebook Visdom vs Google Tensorboard for Pytorch
Oh I think most of the paid tracking solutions have auto refresh. As for the free ones? At clear.ml we have them for quite a while, for MLflow there is an open feature request. https://github.com/mlflow/mlflow/issues/2099
- [D] How do you ensure reproducibility?
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[D] How to maintain ML models?
MLflow is a MLOps tool that may help you: https://mlflow.org/
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[D] Looking for good refreshers on stats / ML to go back to the ML engineer interview game after 2 years doing mostly Software.
Any CI/CD experience you had working as a conventional SDE should translate well to "MLOps". Here are some resources to help you review what kinds of considerations might be important for productionizing ML projects: https://mlflow.org/, https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment
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Top 5 tools to get started with MLOps !
MLFlow : https://mlflow.org
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What GitHub template do you guys follow?
I would say yes because you won’t know if the newer model has better or worse performance until you test it so you may elect to keep the older one. As your data changes and the model drifts one of the previously trained models might begin to perform better. You also will likely want to switch approaches and may have several different implementations. I suggest checking out MLFlow and setting up a model pipeline and registry. https://mlflow.org/
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[P] Integrating Git, DVC, and MLflow into one
Hey r/MachineLearning. I'm one of the creators of DAGsHub, and I wanted to share something cool that we've been working on. DVC (dvc.org) and MLflow (mlflow.org) are two open-source projects that are very widely adopted, each for its own specialty. DVC excels at data versioning, and MLflow is used for many things (it's actually multiple tools combined into one), but mainly for its experiment tracking capabilities. Both tools have a built-in tradeoff – since they are open source, setting up storage for DVC and a central tracking server for MLflow can be a pain – requiring you to create cloud accounts, add permissions, and more.
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Data science experiments management with Weights & Biases platform
MlFlow is an open source platform for managing the machine learning lifecycle. This platform is great for single use, but not very suitable for use in large teams, or if you have a large number of experiments.
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"[Discussion]" Should I be using DVC (Data Version Control) in my day-to-day work?
Recently (just a few days back), we started looking into setting up our own wandb-like server using mlflow. It's neat. So, highly recommended to try if you want to track all your experiments.
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What is the best way to handle results/data + metadata? [Discussion]
Have you tried https://mlflow.org/ ? Should fit the bill quite nicely in your case.
Stats
mlflow/mlflow is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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