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. (by h2oai)
MLflow
Open source platform for the machine learning lifecycle (by mlflow)
Our great sponsors
| H2O | MLflow | |
|---|---|---|
| 7 | 44 | |
| 6,196 | 13,914 | |
| 1.2% | 2.7% | |
| 9.8 | 9.6 | |
| 2 days ago | 6 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
H2O
Posts with mentions or reviews of H2O.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-14.
- A Tiny Grammar of Graphics
-
20+ Free Tools & Resources for Machine Learning
H2O.ai H2O is a deep learning tool built in Java. It supports most widely used machine learning algorithms and is a fast, scalable machine learning application interface used for deep learning, elastic net, logistic regression, and gradient boosting.
-
Data Science Competition
H20
MLflow
Posts with mentions or reviews of MLflow.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-27.
-
Integrating Hugging Face Transformers & DagsHub
While Transformers already includes integration with MLflow, users still have to provide their own MLflow server, either locally or on a Cloud provider. And that can be a bit of a pain.
-
Any MLOps platform you use?
I have an old labmate who uses a similar setup with MLFlow and can endorse it.
MLflow - an open-source platform for managing your ML lifecycle. What’s great is that they also support popular Python libraries like TensorFlow, PyTorch, scikit-learn, and R.
-
Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
-
ML experiment tracking with DagsHub, MLFlow, and DVC
Here, we’ll implement the experimentation workflow using DagsHub, Google Colab, MLflow, and data version control (DVC). We’ll focus on how to do this without diving deep into the technicalities of building or designing a workbench from scratch. Going that route might increase the complexity involved, especially if you are in the early stages of understanding ML workflows, just working on a small project, or trying to implement a proof of concept.
-
AI in DevOps?
MLflow
-
AWS re:invent 2022 wish list
I am seeing growing demand for MLflow (https://mlflow.org/) and I am seeing a lot of people looking at Databricks as commercial offering for MLflow. Alternatively, some popele are implementing something like Managing your Machine Learning lifecycle with MLflow. Therefore, I think this was on my wish list last year, but I really hope AWS announce a Managed MLFlow Service. I know version 2.X is too new but at least 1.X would be great start.
-
✨ 7 Best Machine Learning Experiment Logging Tools in 2022 🚀
🔗 https://mlflow.org
- [D] Who here are convinced that they have a really good setup that keeps track of their ML experiments?
-
JBCNConf 2022: A great farewell
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
What are some alternatives?
When comparing H2O and MLflow you can also consider the following projects:
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.
guildai - Experiment tracking, ML developer tools
dvc - 🦉Data Version Control | Git for Data & Models | ML Experiments Management
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
neptune-client - :ledger: Experiment tracking tool and model registry
scikit-learn - scikit-learn: machine learning in Python
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