By Naveen Kumar Kalyankar, Regional Vice President- India & GIC/GCC at OutSystems
Machine learning (ML) has become key in our global technology landscape today, powering the growth in large language models and Generative AI. It is the backbone of various applications, from recommendation engines in e-commerce to predictive maintenance in manufacturing. However, deploying machine learning models into applications can be a complex and challenging process, often requiring a hand-off between data science teams, machine learning engineers, and development teams.
Low-code application development, which radically simplifies software creation and minimizes manual coding, has emerged as a pivotal tool for fast-tracking the development of applications, revolutionizing the way we create and deploy intelligent software. Known as AI low-code, or low-code AI, these platforms bridge the gap between developers and AI and target specific pain points that are often faced by organisations that are looking to build world-class, enterprise-grade machine learning applications. For mission-critical and consumer-oriented applications, this means being able to integrate machine learning capabilities rapidly and without complexity, taking efficiency to the next level.
The Synergy of AI Low-Code and Machine Learning: Accelerating Innovation
The challenges associated with application development are well-known to most organizations. Building enterprise applications is a complex task, and there is a constant shortage of developers. Moreover, the business environment evolves at a pace that software and systems often struggle to match.
By enabling machine learning with low-code AI, organizations can transform the way they deliver enterprise applications, with several key benefits:
Seamless Integration: Integrating machine learning into applications for driving innovation is simplified with low-code platforms. Many of these platforms offer pre-built AI models, and algorithms and connectors that can be seamlessly incorporated into applications without the need for extensive coding or scripting.
This approach employs the visual development model and drag-and-drop functions characteristic of low-code to seamlessly infuse AI and machine learning into existing systems and new software.
Accelerated time-to-market: Low-code platforms significantly expedite the integration of machine learning into applications, reducing development time compared to traditional methods. This speed is essential for businesses aiming to swiftly introduce innovative AI-powered solutions to the market and stay competitive.
Rapid Prototyping: Machine learning driven by low-code facilitates the rapid prototyping of AI applications. This allows data, development, and IT teams to quickly test and iterate on their ideas, aligning them with business requirements and expectations.
Democratized Development: AI low-code platforms make application development accessible to individuals with diverse coding backgrounds, reducing the reliance on specialized AI developers. By eliminating the need for extensive coding, AI low-code platforms reduce the learning curve associated with AI. The result is that organizations can create intelligent applications, including chatbots and predictive analytics, without relying heavily on specialized AI experts. This alleviates the demand for a limited pool of experts, and creates flexibility in developer teams that can tap on more diverse talent for further innovation.
In conclusion, the marriage of low-code development and machine learning has ushered in a new era of software innovation and accessibility. This powerful synergy not only accelerates application development but also empowers organizations to swiftly release innovative AI-powered solutions to meet ever-evolving market demands. Low-code platforms enable rapid prototyping, streamlined integration, and seamless development, transforming the way we create software.
Businesses can harness the full potential of AI and machine learning, propelling them into the forefront of the digital landscape. The future of application development is marked by flexibility, efficiency, and the democratization of AI with low-code, enabling organizations to create intelligent applications that shape the future of industries, from finance to healthcare, and beyond.
