MLflow VS neptune-client

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.

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.

Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?

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.

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.

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.

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.

MLflow

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.

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.

track and compare your model performance visually. In addition, Neptune integration can be used.

Neptune.ai, which promises to streamline your workflows and make collaboration a breeze.

Blog – neptune.ai - Metadata store for MLOps, built for teams that run a lot of experiments. (RSS feed: https://neptune.ai/blog/feed)

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.

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.

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?

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.

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.