Moving from prototype to production in machine learning can be challenging.

5 best practices for machine learning in production.

(A thread) 👇🧵

cc: @abacusai

@abacusai 1. Keep your models up to date

As new data becomes available, your models need to be retrained on this new data to stay accurate and to the mark.

@abacusai 2. Monitor your models

It’s important to keep an eye on how your models perform over time. If you see that accuracy is declining, it may be time to retrain your model.

@abacusai 3. Timely Retraining

Even if your models are performing well today, new data can come along that invalidates your current models. Be prepared to retrain your models as needed.

@abacusai 4. Automate model training and deployment

Automating your machine learning pipeline using MLOps tools can save you a lot of time and effort.

Models can be upgraded automatically without any human intervention resulting in the best user experience all the time!

@abacusai 5. Assisted MLOps at Scale

End-to-end platforms like @abacusai can make it really easy to train and deploy your ML models on autopilot without getting your hands dirty on the infrastructure and deployment side.

So, you can just focus on your usecase!

👉 abacus.ai/

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