14_Operationalizing Machine Learning

Question 1

Operationalizing Machine Learning

Operationalizing Machine Learning refers broadly to the process of deploying predictive models to a production environment, together with ongoing measurement, monitoring and improvement of those models.

Question 2

KubeFlow

• ML Toolkit for Kubernetes
• Data modelling with Jupyter Notebooks
• Tuning and training with TensorFlow
• Model serving and monitoring

Question 3

KubeFlow - Pipelines and Components

A pipeline is a description of a machine learning (ML) workflow, including all of the components in the workflow and how the components relate to each other in the form of a graph.

A pipeline component is a self-contained set of user code, packaged as a container, that performs one step in the pipeline. For example, a component can be responsible for data preprocessing, data transformation, model training, etc.

Question 4

KubeFlow with AI Platform

• Ingest Data
  
  • Cloud Storage
  • Transfer Service
• Prepare and Preprocess Data
  
  • Cloud Dataflow
  • Cloud Dataproc
  • BigQuery
  • Cloud Dataprep
• Develop & Train Models
  
  • Deep Learning VM
  • AI Platform Notebooks
  • Ai Platform Training
  • KubeFlow
• Test & Deploy Models
  
  • TensorFlow Extended
  • AI Platform Prediction
  • KubeFlow
• Discovery
  
  • AI Hub: Hosted AI repository of plug and play AI components