Useful considerations Data scientists develop features and patterns by transforming raw data into meaningful ones. For flexibility, they resort to feature engineering. However, when data is vast and complex, they feel it is best to automate with easy-to-adapt designs. The mantra is not to complicate and find solutions where integration is seamless.
Develop data features
Feature engineering for flexibility
CONSIDERATIONS
Easy to adapt designs
Automation to reduce dependency
Conclusion Most businesses are adopting machine learning for key business decisions. However, the limited data quality and lack of ability to evaluate the quality of data, creates trust issues. In the upcoming time, there will be a continuous transition of technology with data automation really taking off. But for all that to happen, the quality of data, simplicity and flexibility of solutions, and easy adaptability are key. What is required is the collaborative work of data scientists and data engineers, expanding their boundaries and entering into each other’s domain to improve quality and model implementation. The key focus will remain productionization of the model with regular maintenance.
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