ML KnowledgeML Case

How would you detail the workflow from training a machine learning model to its deployment in production?

Machine Learning Engineer

Google

Palo Alto Networks

Reddit

YouTube

Zillow

Etsy

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Answers

Anonymous

4Strong
In dev, the model training of different datasets must be tracked. This means:
* Tracking the dataset used for training, validation and testing
* Tracking ML algorithm and its parameters
* Train the algorithms + different parameters on the same training dataset, validate and test on the same validation & testing dataset.
* Compare the output of the algorithms on the test dataset.
* all of the above steps must be tracked (using MLFlow)

Then when the final algorithm is picked, the code + model must be pushed to git repo. 
The MLOps framwrok insures the code and model goes to standard workflow of dev/staging/prod. In Staging, a code review will be done (by someone else and maybe on different dataset). When all ticked, it will go to Prod and then realease
  • How would you detail the workflow from training a machine learning model to its deployment in production?
  • What are the key steps involved in taking a machine learning model from training to production?
  • Could you walk me through the process of training a machine learning model and then deploying it?
  • Can you describe the life cycle of a machine learning model from conception to deployment?
  • What does the journey of a machine learning model look like from initial training to final deployment?
  • How do you take a machine learning model from the training phase to a live production setting?
  • Can you outline the stages of implementing a machine learning model into a production environment?
  • What is the standard procedure for rolling out a trained machine learning model in a production context?
  • How do you ensure that a machine learning model is properly transitioned from training to production use?
  • What considerations do you take into account when deploying a machine learning model into production?
  • Can you explain the process of training and deploying a machine learning model in a production environment?
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Interview question asked to Machine Learning Engineers interviewing at NetApp, ServiceNow, YouTube and others: How would you detail the workflow from training a machine learning model to its deployment in production?.