🔹Successfully Deploying Machine Learning Models
There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all — it’s a lifecycle. Why? It’s an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion.

📌 Via: @cedeeplearning

link: https://www.rocketsource.co/blog/machine-learning-models/

#end_to_end
#deployment
#machine_learning

✔️Successfully Deploying Machine Learning Models

There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all — it’s a lifecycle. Why? It’s an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion. We will go deeper into the reasons for this in the section below as we address the requisite steps for operationalizing a model, but the high-level post-deployment steps are called out in the following diagram. Here’s what that deployment looks like in action
———————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#machinelearning
#lifecycle
#deployment
#datascience
#deeplearning

👆🏻👆🏻Successfully Deploying Machine Learning Models

1. Validate Use Case
2. Data Finalization
3. Explore and Diagnose
4. Cleanse
5. Develop
6. Features
7. Build
8. Infer
9. Publish
10. Deploy
11. Consume
———————————
📌Via: @cedeeplearning

#machinelearning
#datascience
#deployment
#lifecycle
#AI
#data
#deeplearning