Data Science Production Methods

Creating a data science project and executing its modules is the primary step in the production environment, which is where every startup or some established companies fail. While implementing a new module of an existing data science project seems to difficult, working on the module due to the discontinuation of complex tools and techniques used in the design environment is even more so.

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Strategic Data Packing
Consider any project that you want. It’s is a known fact that there exists no project without data as data is a default. Each database comprises a huge amount of data in distinct formats and a huge amount of code - let’s say n-number of lines of code with different scripting languages enables us to turn raw data into predictions. The packing of data or code typically happens during production.

A typical release process includes:

Putting a versioning tool in place in order to control the code versions.
Building a packaging script to pack the code in a zip file format.
Deploying it to production.
Optimization and Retraining Models
To get accurate results, teams work in small iterations. These iterations play a vital role in the process of optimization and retraining. It is essential to have a process layed out in several phases, namely: validation, retraining, and the deployment of modules. However, the modules need to be regularly updated to fit into the new behavior and underlying data changes.

If you need to retrain your models, then it is suggested to gather more knowledge from Data science training make this a distinct step in the production workflow of your data science team. For example, setting up your system to retrieve a predictive model data weekly, give this model a rating based on the performance, and then validate the results returned, while a human operator verifies the results as well.

Increasing the number of tools leads to a greater number of problems. Maintaining the production as well as design environment with the latest versions along with the packages is recommended. A data science project depends on up to 100 R packages, 40 for Python, and several hundred Java/Scala packages. For acquiring more information source is Data science online course.