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Options for configuration of python libraries - Stack Overflow
2 projects | /r/learnpython | 14 May 2023
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
[D] Is there a tool to keep track of my ML experiments?
2 projects | /r/MachineLearning | 13 May 2023
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
Looking for recommendations to monitor / detect data drifts over time
3 projects | /r/datascience | 15 Apr 2023
Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?
Integrating Hugging Face Transformers & DagsHub
2 projects | /r/mlops | 27 Mar 2023
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?
5 projects | /r/selfhosted | 25 Feb 2023
I have an old labmate who uses a similar setup with MLFlow and can endorse it.3 projects | /r/learnmachinelearning | 25 Feb 2023
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
3 projects | /r/selfhosted | 24 Feb 2023
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
4 projects | dev.to | 12 Jan 2023
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?
2 projects | /r/devops | 6 Dec 2022
AWS re:invent 2022 wish list
2 projects | dev.to | 23 Nov 2022
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.
[D] Drop your best open source Deep learning related Project
5 projects | /r/MachineLearning | 30 Dec 2021
Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
[D] Facebook Visdom vs Google Tensorboard for Pytorch
5 projects | /r/MachineLearning | 26 Sep 2021
I'm talking about ClearML😅 trying not to shill for open-source but ~5000 teams have already chosen 💪 https://github.com/allegroai/clearml
[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
16 projects | /r/MachineLearning | 30 Mar 2021
There are mainly two solutions that are 100% open source and free to install and use, and that may solve most of the requirements of ML practitioners: Hopsworks and ClearML. Among this two, if I had to chose one right now, it will be ClearML. Hopsworks might be much more complete, but ClearML seems to have a bigger community behind it and to be easier to install and use. So ClearML will be something to take a look at in case we go for an all-in-one package. I also like the idea of having a platform with an UI with all our projects.
[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?
11 projects | /r/MachineLearning | 28 Mar 2021
What are some alternatives?
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
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
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
dagster - An orchestration platform for the development, production, and observation of data assets.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows