SOME THINGS TO REMEMBER
The construction of the model should be an incremental process, which starts with a simple model that adds complexity if needed after reviewing metric evaluations (the more complexity you add, the more your model will “cost” (time and C/GPU) you to perform).
As the model evolves, communicate results in many ways, like writing reports of different steps and plots that show metrics results coming with a demonstration with a test dataset, perhaps run workshops to help educate and gather feedback from your business partners. This direct interaction allows the teams to understand what is being done, and what is needed next time and develops more in-depth organisational knowledge and capacity.
Now that we have confirmed our model can deliver robust and reliable results, we might want to deploy it on the cloud or directly included it in an application running on any eligible device. Choosing where and how needs to be thought through and planned – ensuring the organisation is ready, and change management has been used to help smooth the deployment. Part of this is done a few steps back – engaging with the business with interactive sessions during model development.
Finally, we have now delivered this super, exciting solution’s development and deployment, we must facilitate updates and incorporate future changes easily. This again needs planning and consideration. One benefit of models and data science is as you collect more data and new insights, they can be incorporated into the model to enhance its efficiency, accuracy, and value.
To conclude, the backbone of data science in your company should be maintaining an agile and collaborative environment and researching technical topics for future challenges that you might confront.
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