Explain the stages of building a Machine Learning model?

Data Science, AI and ML
1

Data Collection: It is the first stage of any kind of machine learning model. In this stage, the appropriate data is decided, and then it is collected using some algorithm or manually.

Data Processing: In this stage, the data that we have collected in the first stage is preprocessed by handling all the null values, categorical data, etc. Also, in the same stage, the data features are made in the same range if they are not already.

Model Building: In this stage, first, we choose appropriate algorithms to create the model, and then, with the help of sklearn, for example, the model is built.

Model Evaluation: After the model is created, it is evaluated using some techniques of statistics like accuracy score, z score, accuracy matrix, and more.

Model Saving and Testing: After a successful evaluation of the model, it is saved for future use, and real-time testing is done using it.

2
  1. Make sure the business problem and value levers are well defined
  2. Find out what data and technologies are available
  3. Draft possible solutions, including data science and technology architectures, looking out for quick win opportunities
  4. Assess viability of the project comparing potential results x difficulty x cost x time x management’s expectations
  5. If realistically viable, then start the actual development work
  • Gathering, exploring and preparing data
  • Training and testing initial models focusing on quick wins (i.e. don’t implement the latest deep learning algorithm)
  • Developing technology infrastructure MVP (e.g. data pipelines, automation scripts, APIs…)
  • Implementing quick wins
  • Measuring created value (e.g. more sales, less costs, improved client satisfaction)
  • Repeat, always improving incrementally (better techniques, better data, better infrastructure)