🤖 MLOps: The Missing Link in Your Machine Learning Strategy 🔗

MLOps bridges the gap between data science and engineering, creating sustainable ML systems that actually work in the real world.

A proper MLOps workflow includes:
🔄 Automated data ingestion
🧪 Continuous model training
📊 Performance monitoring
🚨 Drift detection
🚀 Seamless redeployment

👀 https://link.illustris.org/mlopscode2prod

#MachineLearning #MLOps #DataScience #AIEngineering #ModelDeployment #DataDrift #AIPipelines

🚀 Deploying AI models has never been easier! With Neurolov AI’s decentralized GPU network, you can get started in just 4 simple steps:

1️⃣ Sign Up & Access Resources: https://app.neurolov.ai
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Learn how to build and deploy a Hugging Face NLP model using Docker.

https://linuxexpert.org/building-and-deploying-a-hugging-face-model-with-docker/

#HuggingFace #Docker #NLP #MachineLearning #AI #Python #ModelDeployment #FastAPI #Transformers #DataScience #APIDevelopment #AIModels

Question about R, mlflow and models...

I am trying to register a R model using the crate flavor in mlflow, and I have some doubts.

I have been able to log and register the model. I have also tested that I can load the model again and use it for prediction (inputs/outputs are data.frames).

I was thinking... that would mean I should write the inference part in R, wouldn't it?

How could I deploy the model so it can be served as a general web service (REST API), not actually relying on final users to use R?

I'm now quite tired, but the only solution I have found is to maybe use plumbr to expose an API receiving a JSON with all the inputs as simple types, and generating the data.frame inside, as I have always done.

Do you think this can be done directly using a crated function? Has anybody done something similar?

Thanks in advance. I think this is a discussion worth having, as there is a lack of documentation on this topic for us R users. :(

#rstats #ml #machinelearning #models #mlflow #ai #datascience #data #prediction #mlops #modeldeployment