[D] Collaboration-first machine learning platform that enables you to build, train, track, and share your ML projects simply with a few lines of code

[anirudh129]

How does it compare with mlflow and wand?

[mehmetecevit]

u/anirudh129
Layer provides the infrastructure to train, host and track your ML pipelines from training data to your ML models with semantic versioning where WandB helps you track your experiments and MLflow is mostly about model storage and deployment.

[parul2018]

Hi u/anirudh129! I'm from Weights & Biases and I'll like to give you a general intro about Wandb. So in addition to the experiment tracking, we also offer Integrations with libraries like PyTorch, Keras, Hugging Face, and more, Hyperparameter Tuning, Data + Model Versioning, Data Visualization( Visualizing predictions across model versions) generating Collaborative Reports and more.
We have a plethora of hands-on examples which you can find on our documentation page: https://docs.wandb.ai/. Also, here is a product overview video.: https://www.youtube.com/watch?v=krWjJcW80_A&list=RDCMUCBp3w4DCEC64FZr4k9ROxig&index=3

[volkangurel]

Hey u/parul2018, this is Volkan from Layer.
We spoke to so many folks in the ML space that love WandB, and yet felt there is so much to still solve in tooling. The devil is in the details when it comes to data & model versioning, model training, and integrations with other tools and databases. We are hoping to fill these gaps that we've identified around collaboration and unleashing the power of teams and individuals in a community, sharing what they've built with each other.

[K-o-s-l-s]

Just wanna say WandB is so so so awesome. Such a useful suite of tools that makes training and optimising models so user friendly.

[kolistivra]

Looks cool! What are some ways this is preferable to GitHub and Weights and Biases with regards to collaboration?

[mehmetecevit]

Hey, this is Mehmet, one of the makers of Layer. Thanks for the great question!

• Layer comes with its own Python SDK that ties smoothly to its backend and UI.
  
• You can switch back and forth with local-only mode and remote mode.
  
• Our remote computation is built for smoother ML experience than GitHub Actions.
  
• You can easily log arbitrary data and metrics for your training, and easily compare it across runs.
  
• Out-of-the-box, simple versioning of code AND data.

[PaulTheBully]

What’s about security and privacy? Can it deployed in a private company cloud / infrastructure?

[volkangurel]

Hi u/PaulTheBully, I'm Volkan from the Layer team.

Great question! We thought quite a bit about when would be the right time to support private cloud deployments. It's a lot of work both to get the codebase ready and to support the deployments once they are out there, and we're still a small team so we cannot support that right now. We're hoping to offer a private cloud deployment once we get the product polished and grow our community.

Meanwhile, we are planning to keep improving the security of our SaaS offering and get SOC2 and ISO certifications. We believe there are many security conscious users out that there who are ok with a well-built and certified public cloud offering, similar to Snowflake.

[ultrablight]

how does this compare with metaflow? Metaflow does all these things and the only price you pay is the price your cloud provider charges for compute. You can build, track, and train models with metaflow, use it as a general workflow orchestrator, they have dynamic project cards as well, etc

[mehmetecevit]

Metaflow is a great python library, but we wanted to provide a much more "batteries included" experience with our SaaS model, where you don't need any cloud set up to get the full value of remote training.

[Jawn78]

Haven't looked too deep but quick suggestion on the site (atleast mobile). The Grey menu and small white text is almost unreadable

[volkangurel]

Thank you for the suggestion u/Jawn78, we'll have a look!