As the organisations have started to recognise the value of data, the need for data scientists has seen an exponential rise since then. Unlike traditional methods, crucial decisions among organisations are now mostly data-driven.
However, at the present era, while researching on the grounds of machine learning and data science has become a go-to thing for organisations, deploying the sophisticated machine learning algorithms and yielding more than 90 per cent accuracy in the outcome of the model is a complex and arduous process.
The Software Development Life Cycle is the most important process followed by the software industry. It helps software developers to design, test as well as deploy robust and high-quality software products. Considering software development standards into data science model-building will help developers create robust and cost-effective machine learning models.
Recently, IBM, at Think Digital 2020 virtual conference, launched Watson AIOps for IT operations management with the latest technologies. According to sources, the AIOps utilises machine learning and advanced analytics along with automation technologies to assist firms in detecting IT incidents and responding to restore services quickly.
According to Microsoft Research, creating and running software products require large amounts of raw data about the development process and customer usage, which can be turned into actionable insights with the help of skilled data scientists. Unfortunately, data scientists with analytical and software engineering skills who have the ability to analyse the large raw data sets are usually hard to acquire in an organisation.
Implementation of software development standards into the domain of data science researches will indeed provide a beneficial path for data scientists.
Here, we will depict some of the crucial points as to why data scientists should follow the software development standards.
The Road To Clean Code
While developing software, software developers follow a specific methodology of writing codes that is comfortable for other readers and writers. The methodology includes mentioning class and functions, well-documented codes, clear inline comments, error messages, limit line length, among others.
A data scientist is also a programmer or coder who is concerned with coding a particular problem and hence finds solutions to the problem. However, one thing that they should follow from software developers is the way to write clean codes.
Most of the time, data scientists work on their own and while working on the complex computation, they usually miss simple rules such as clear inline commenting, well-documenting, keeping indentation consistent and other such. This in result makes the code messy and hard to understand for any other developers except the one who wrote it.
From Research To Product
Software development follows a structured approach when it comes to design, develop, test as well as maintain a high-quality product. In an SDLC, the development process makes it easy for developers to create and build a product easily without witnessing many failures. But, in a machine learning model building, data scientists are often in a dilemma about the model’s performance as it varies in the real-world use cases, which is often contrary to the performance during research. Consequently, data scientists from the early stage of a project should focus on writing production-level code. This will ensure an effortless workflow while taking the research into production.
The Need For Automation
Embracing the power of automation is one of the methods for increasing the efficiency of a product in software development. While building machine learning models, 80 per cent of the time by data scientists is invested in data wrangling. This, in result, makes the data science projects take longer to deliver impactful results for the business. Consequently, they should embrace automation solutions that can help them in expediting the development of machine learning models.
Ryohei Fujimaki PhD is founder and CEO of dotData said, “The benefit of this automated approach is that it provides data scientists with the assistance needed to test for scenarios that they may not have ever considered (discovering “unknown unknowns” to borrow a phrase).” He added, “Also, it allows data scientists to try significantly more use cases and dramatically shorten the time needed to reach highly impactful ones.”
Considering The Best Practices
Some of the best practices in software engineering include dynamic requirements, use of component architecture, quality assurance, control change, and other such. These are a set of proven approaches to the software development cycle. When combined, these approaches strike at the root cause of software development problems. Following these practices, data scientists will have the ability to manage the requirements of customers
Following The Path To QA
Quality Assurance and Testing are the most crucial tasks in the software development method. It helps developers to find and avoid possible errors and mistakes in the product while maintaining the integrity of the services.
Similarly, in machine learning workflows, a data scientist can perform a quality check on the data that is being used, the quality of the machine learning algorithms and other such. For instance, most of the time, a data scientist fails to build a machine learning model that can fortify adversarial attacks dataset. A proper quality check, similar to software development is essential to ensure one is offering robust AI-based products in the market.