Hands-On Transfer Learning with Python

Evaluation

Once the modeling step results in a model(s) that satisfies the success criteria, performance benchmarks, and model evaluation metrics, a thorough evaluation step comes into picture. In this step, we consider the following activities before moving ahead with the deployment stage:

• Model result assessment based on quality and alignment with business objectives
• Identifying any additional assumptions made or constraints relaxed
• Data quality, missing information, and other feedback from data science team and/or subject matter experts (SMEs)
• Cost of deployment of the end-to-end ML solution