![]() With A/B testing, there could be multiple machine learning models being used concurrently. To complicate the matter even further – to ensure the quality of the machine learning models, techniques such as A/B testing are used. That’s why most data scientists will tell you that they spend 80% of the time doing data preparation, cleaning and feature engineering. Unlike traditional software development where the product is based on code, data science machine learning models are based on both the code (algorithm, hyper parameters) and the data used to train the model. The building of machine learning models is similar to traditional software development in the sense that the data scientist needs to write code to train and score machine learning models. ![]() What is a CI/CD data pipeline and why does it matter for data science? The outcome is a faster development life cycle and a lower error rate. Saving time allows then to focus on their core job function - getting the insight out of the data and helping business makes better decisionsĬontinuous integration and continuous delivery (CI/CD) is a software development approach where all developers work together on a shared repository of code – and as changes are made, there are automated build process for detecting code issues. Efficiency: Data professionals save time spent on data processing transformation.Error reduction: Automated data pipelines eliminate human errors when manipulating data.Consistency: Data pipelines transform data into a consistent format for users to consume.Save the processed data to a staging location for others to consumeĭata pipelines in the enterprise can evolve into more complicated scenarios with multiple source systems and supporting various downstream applications.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |