Question as a starting point:

1. What question(s) am I trying to answer? Do I think the data collected can answer that question?

2. What is the best way to phrase my question(s) as a machine learning problem?

3. Have I collected enough data to represent the problem I want to solve?

4. What features of the data did I extract, and will these enable the right predictions?

5. How will I measure success in my application?

6. How will the machine learning solution interact with other parts of my research or business product?

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Questions for Data

1. Question is Sharp ?
2. Data measures what you care about ?
3. Data is accurate ?
4. Data is connected ?
5. Is there a lot of data ?

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Question for Algorithms

https://docs.microsoft.com/en-us/azure/machine-learning/studio/data-science-for-beginners-the-5-questions-data-science-answers

1. Is this A or B ? => Classification algorithms ( two-class or multiclass clasification)
2. Is this weird or not normal ? => Anomaly detection algorithms
3. How much or how many ? => Regression algorithms (continuous data)
4. How this is organized ? => Clustering algorithms (Which viewers like the same types of movies?)
5. What should I do now ? => Reinforcement learning algorithms (Decision process and rewards system , e.g: autonomous driving)

Model interaction

1. Define the interaction with the data science models: REST? RPC calls RabbitMQ? Kafka Message Bus?
2. Plan to use notebooks (Jupyther, Azure Notebook, Google colab)
3. Model retraining (Cron for the pipeline or Airflow)