Cloud Machine Learning Engineering and MLOps
Week 1 Quiz
What is a key difference between Data Science and ML Engineering?
You got it! ML Engineering focuses on bringing models to production.
Why is an advantage of using a widely used ML Platform?
Many potential hires will know the technology stack
A widely used platform will be known by many users
A widely used platform will get frequent updates
What is an advantage of Flask for ML Engineering?
You got it! Microservices are easy to create in Flask due to the minimalistic design.
How can ML Engineering used?
You got it! ML Engineering is the process of creating ML systems that work.
What is Continuous Delivery?
You got it! CD or Continuous Delivery is the process of ensuring code is always in a deployable state .
What would be an example of an ML application?
You got it! OCR (Optical Character Recognition) is a good example of a Machine Learning app.
Why would a Microservice be valuable for ML?
You got it! Microservices which are single functions are ideal to serve out ML predictions.
What is an example of a Machine Learning Engineering platform?
You got it! Sagemaker is designed to train and deploy models.
What problems do Machine Learning platforms solve?
You got it! Platforms allow for distributed training of large models.
What advantage could a ML platform create for deployment?
You got it! Deploying software to endpoints that auto-scale is a key advantage.