What is MLflow?
Who uses MLflow?
MLflow Integrations
Here are some stack decisions, common use cases and reviews by companies and developers who chose MLflow in their tech stack.
We are trying to standardise DevOps across both ML (model selection and deployment) and regular software. Want to minimise the number of tools we have to learn. Also want a scalable solution which is easy enough to start small - eg. on a powerful laptop and eventually be deployed at scale. MLflow vs Kubernetes (Kubeflow)?
Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?
MLflow's Features
- Track experiments to record and compare parameters and results
- Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
- Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms
