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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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Options for configuration of python libraries - Stack Overflow
2 projects | reddit.com/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 | reddit.com/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 | reddit.com/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 | reddit.com/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 | reddit.com/r/selfhosted | 25 Feb 2023
I have an old labmate who uses a similar setup with MLFlow and can endorse it.3 projects | reddit.com/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 | reddit.com/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 | reddit.com/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] The hype around Mojo lang
2 projects | reddit.com/r/MachineLearning | 5 May 2023
Other companies followed the same route to promote their paid product, e.g. plotly -> dash, Pytorch Lightning -> Lightning AI, run.ai, neptune.ai . It's actually a fair strategy, but some people may fear the conflict of interest. Especially, when the tools require some time investment, and it seems like a serious vendor lock-in. Investing some time to learn a tool is not such a big deal, but once you adapt a workflow of an entire team it can be tough to go back.
[P] New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio
3 projects | reddit.com/r/MachineLearning | 21 Apr 2023
track and compare your model performance visually. In addition, Neptune integration can be used.
Any MLOps platform you use?
5 projects | reddit.com/r/selfhosted | 25 Feb 2023
Neptune.ai, which promises to streamline your workflows and make collaboration a breeze.
A huge list of AI/ML news sources
2 projects | dev.to | 25 Nov 2022
Blog – neptune.ai - Metadata store for MLOps, built for teams that run a lot of experiments. (RSS feed: https://neptune.ai/blog/feed)
Who needs MLflow when you have SQLite?
5 projects | news.ycombinator.com | 16 Nov 2022
Machine Learning experiment tracking library for Rust
4 projects | reddit.com/r/rust | 23 Oct 2022
Therefore I am looking for frameworks which can help me with tracking all the ML experiments. There are an endless plethora of such libraries for Python, most notably perhaps [wandb](wandb.ai), but others include Neptune, Comet ML and TensorBoard.
[D] Maintaining documentation with live results from experiments
3 projects | reddit.com/r/MachineLearning | 29 May 2022
In the case of neptune.ai we don't have this feature but you can query and retrieve the metadata you logged programmatically using the Python Client and use it to create a custom report/dashboard using tools like notion, streamlit, gradio, dash and etc. You also can have a cron-job that updates the report periodically or when there is a new experiment logged to Neptune.
What are the differences between MLflow and neptune?
2 projects | reddit.com/r/mlops | 21 Mar 2022
Hello u/MLBoi_TM! I was wondering: The pros/cons you've listed, is this comparing Managed MLflow <> neptune.ai or the OSS MLflow compenent <> neptune.ai?2 projects | reddit.com/r/mlops | 21 Mar 2022
The key difference between MLflow and neptune.ai on a shallow level is really that neptune.ai does not offer a standalone OSS solution. Apart from that, its offering overlaps with MLflow's in the sense that it focuses on experiment tracking (incl. metadata store) as well as model artifact management ("model registry"). Of course, there' lots of differences in the detail then. However, since I've never used neptune.ai, I cannot really comment on that.
Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
4 projects | dev.to | 6 Dec 2021
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.
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.
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.
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
dagster - An orchestration platform for the development, production, and observation of data assets.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows