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The data science lifecycle follows a systematic workflow for a data modelling project such as- plan of action, data model building, model evaluation, monitoring and deploying models, etc.

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First, a project is planned and its possible outputs are determined. In-base data tools or open-source libraries are utilized for building machine learning models by data scientists.

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When Schrödinger first discovered the correct laws of quantum mechanics, he wrote an equation which described the amplitude to find a particle in various places.

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Often users require efficient tools, useful data and resources. They need APIs to assist with data profiling, data ingestion, or feature engineering. Before deploying models data scientists require a high level of accuracy in their models.

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Evaluation metrics and visualizations are generated by the model evaluation for various purposes which may include-measurement of model performance against the new data, and much more.

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Internal mechanics explanation of the machine learning models has not always been possible.

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Getting a machine learning model and fixing it into the right process is a hard-working and difficult task.

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Also, deploying a model process can be made easier by utilizing the machine learning models.

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Model deployment is not the end of the process. Model monitoring must be properly carried out.

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