Data Science Production Methods
Data Science Production Methods
In this article, we take a look at some of the things that developers and data scientists should keep in mind while designing a data science project.
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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.
In this post, let’s have a look at the things that we should keep in mind while designing a data science project.
Keys to Building an Optimally Designed Production Pipeline
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 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.
Involve the Business Team
According to experts, most of companies report about the troubled relationship bewteen teams, such as difficulties while deploying into production and limited involvement of the business teams.
The team should understand that the project development stage is a critical stage and ensure that the business users are aware of the product's usage and that the project is understandable. Once the data product is released to production, the business users want to see the success/profit rate increasing while accessing the performance module, since they base their work on this. However, there are different ways to do this - the best one among them is to set up a live dashboard to monitor/drill-down into module performance. Using a key metric with automatic emails is a safer option and the information is then at the hands of business teams.
Consistent Strategy for Your IT Environment
Recently, data science has been used in technologies such as Python for Data science, Hadoop, R, and Spark, in addition to distinct open source frameworks and their related libraries.
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 managing these sorts of issues, there are two popular solutions:
- Managing a common package list.
- Setting a virtual machine environment for each data project.
A strategy to rollback is nothing but a backup process during production to avoid failures (in other words, an insurance plan). For any data project, there should be a good rollback strategy that includes all aspects such as data, the code base, data schemas, and software dependencies. The critical point is it should be a process accessible to users who are not trained as data engineers, even though the project is a failed one.
The Auditability of Projects and Processes
Audits play an essential role in a project for knowing the output of what we code. While working on a data science project, you need to track the data workflow, which is very important. This will help in the future to prove that no illegal data is being used or privacy verifications being overlooked, that sensitive data is secure from data leaks, and to maintain exceptional quality and data flows.
Some simple ways of version control are SVN and Git. However, storing the information logs in database systems which include modifications, table creation, and schema changes are the best practices.
Scalability of a System and Processes
As we discussed in the beginning, data is one of the key aspects of any project and its role is increasing every single day. As data continues to increase in volume, it becomes crucial to maintain its data performance. Thus, setting up a system which is elastic enough for handling significant transitions, large data volume, and complexity and scalability is essential.
However, scalability issues can come about unexpectedly through the bins which we have not deleted, huge log files, and unused datasets. They can be prevented by analyzing the strategies and checking the workflows at the time of execution.
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