Acceleration of Data Science with Low-Code

These days, data science is becoming increasingly important for making speedy data-driven decisions. Machine learning is also an important part of modern data science (ML). Machine learning (ML) models can detect patterns in data and imbue an app with advanced artificial intelligence (AI) capabilities. While the advantages of machine learning in application development are obvious, machine learning professionals are hard to come by, and data scientists who can produce AI/ML solutions are in high demand. 

To bridge the AI/ML talent gap, some people are resorting to low-code/no-code solutions. This area of tooling includes everything from open-source libraries to end-to-end platforms for collecting data, developing models, and testing them. Many of these low-code platforms have drag-and-drop capabilities, which might assist skilled data engineers automate laborious operations and democratise AI for non-technical users. 

If you can programme an idea from the ground up, there is a low-code/no-code solution or API out there that will perform the heavy lifting for you. The field of data science is no exception. We will look at how low-code/no-code can be useful in data science, as well as some of the benefits and use cases. We will also provide you some examples of low-code AI/ML platforms and frameworks so you can experiment on your own. 

The Need for Low code in Data Science: 

In 2022, data analysis will be critical for a successful business. According to a global report by EY, 81 percent of CEOs think that data should be at the centre of all decision-making. While most companies want to be data-driven, getting there is not easy – creating actionable data procedures takes a lot of time and effort. 

This is partly because applications generate large amounts of unstructured data. In fact, according to various industry analysts, 80-90 percent of the data in an organisation is unstructured. Video, text, audio, social media data, and other types of data can be found in these segregated data lakes. Applications generate logs in a variety of forms, making it difficult to standardise them before using them in machine learning applications. 

Another consideration is the sheer difficulty of creating ML/AI applications. As a company creates more data, it should be able to evaluate trends to better understand the impact of features and improve customer experiences. AI capabilities such as sentiment analysis and image recognition can help most digital apps. Developing meaningful machine learning models, on the other hand, necessitates expertise. Data collection, cleansing, model training, feature engineering, exploratory data analysis, and other advanced processes are all required. Indeed, ranks machine learning as the most in-demand AI skill, making it difficult to find qualified candidates. 

Uses of Low-Code in Data Science: 

With that in mind, here are some examples of how low-code/no-code might assist data science: 

Reduce the time it takes to collect data: To begin with, low code could aid in the smooth integration of APIs to collect data from diverse sources. An internal database or a third-party SaaS system could be used. You may quickly extend the amount of data sources by using pre-built connectors, which will improve your algorithm. When acting on data in real time, programmatic, automatic relationships become even more important. 

As previously said, most of the world's data is unstructured and will require cleaning in order to provide relevant insights. To cleanse data and prepare diverse sources in a format suitable for ML training, no-code automation could be used. Matching, sampling, shuffling, and scaling are examples of data type transformations. "Finding effective ways to bring that data together under a more consistent schema or format is crucial," said Merico senior leadership member Maxim Wheatley. 

Amateurs should have access to no-code AutoML: Many libraries and no-code platforms can train an algorithm using raw data. Using such platforms, advanced machine learning skills could become more accessible. Google's Cloud AutoML, Ludwig by Uber AI, Baidu's EZDL, and Obviously.ai are all no-code AutoML platforms. "These organisations' broader ambitions of being at the cutting edge of technology are served by open-sourcing and no-code AI platforms," says AI consultant Alexandre Gonfalonieri. 

Streamline the training, testing, and deployment of machine learning: TensorFlow is a well-known open-source deep learning tool, but it requires time and code to implement. Other low-code libraries can help data scientists with many elements of training and implementing machine learning with less code. PyCaret, for example, allows you to create ML models from start to finish. Auto-ViML, Apple's CreateML, RunwayML, and Teachable Machine are some of the other tools available. Some may require Python or R knowledge to properly utilise, while others are code-free. 

Data de-silo and business insights: With several pre-built components in an easy-to-use interface, low-code/no-code platforms enable data research. The tools could, in effect, help close the gap between data science and business units. This improved cross-departmental collaboration could assist improve overall business results. "In order to grow and double-down on what's working, smart teams are figuring out more quantitative ways to define and analyse success," Wheatley says. 

Lower the bar for data organisation: Drag and drop interfaces have the potential to considerably assist in the organisation and structure of data with useful flows. "[Low-code] might add 'plain-English' scenario-driven or question-driven inquiries to focus on the consequences and goals of a data query, rather than the method that gets you there," Wheatley notes. 

Create reports and dashboards: Low code can assist data scientists create visualisations of their data in addition to ML/AI preparation. This could help with quarterly evaluations or auditing a company's data footprint. Many low-code development platforms have modules for creating slick user interfaces and graphics from data. 

Conclusion:  

Knowledge workers in a variety of areas can use low-code AI/ML to "fix their own issues." A marketing analyst, for example, was able to create a sentiment analysis natural language processing (NLP) solution in a couple of days utilising a low-code tool supplied by KNIME. Image classification is critical for content moderation efforts, and it can help quality engineers detect product flaws on the manufacturing floor automatically. In the financial sector, AI/ML used to anomaly detection is becoming more crucial to detect anomalies and prevent fraud. 

Low-code development will account for 65 percent of new applications by 2024, according to Gartner. While low code can help with agility, it is important to acknowledge the limitations of such tools as well as the risks of over-automation. "It's safe to anticipate that your data scientists will be wary of no-code/drag-and-drop technologies," Gonfalonieri writes. Many data scientists are used to coding and may find a no-code development platform restricting. 

Although low-code lowers the barrier to entry, Wheatley emphasised that "in many circumstances, low-code will not replace or eliminate the requirement for professionals or technical contributions." 

With that stated, AI is becoming more popular, and low-code solutions can assist reduce the number of steps required to automate many parts of complicated machine learning projects. "Many sectors are adopting low-code because it provides tremendous options for innovation and experimentation while keeping prices down," said Jonathan Grandperrin, Mindee's CEO. "Professional developers will gain time back from some of the more mundane programming activities, such as data extraction," Grandperrin explains. "This will speed up the creation of apps for commodity functions while allowing them to spend more time on enterprise-class apps that still require higher programming skills."