Claire, the Machine Learning Operations Manager at MDFT Pro, is establishing standardized workflows for the data science team to deploy predictive models that will be used by the company’s student management applications. MDFT Pro, a well-known training agency, needs to implement models for predicting student success, recommending courses, and optimizing class schedules, all of which will be accessed by various educational applications and systems. Claire needs to ensure the team follows proper machine learning development practices and performs all necessary steps in the correct sequence before deploying models to production where they will impact student experiences and business decisions.
What is the correct sequence of three essential processes that should be performed before deploying a machine learning model as a service?
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Explanations for each answer:
Data preparation, model training, model evaluation is correct. The correct sequence is data preparation (cleaning and formatting data), model training (teaching the algorithm), and model evaluation (testing performance before deployment).
Model training, data preparation, model evaluation is incorrect. Data must be prepared and cleaned before model training can begin, as models cannot learn from raw, unprocessed data effectively.
Data preparation, model evaluation, model training is incorrect. Model evaluation cannot occur before model training since there would be no trained model to evaluate for performance and accuracy.
Data encryption, model training, model evaluation is incorrect. While data security is important, data encryption is not one of the core sequential steps in the machine learning model development process.
Data preparation, model training, model retraining is incorrect. Model retraining occurs after deployment based on new data and performance monitoring, not as part of the initial pre-deployment sequence.