The next generation of AI platforms: No-Code AI

By
Vladimir Kubikov
Client
4
min read
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Companies are increasingly trying to benefit from their data by incorporating machine learning into their processes. However, managing these machine learning operations (MLOps) can present challenges. In this article, we identify three main issues and discuss how a new kind of AI technology, known as “no-code AI”, can help companies tackle these challenges.

No-code AI tools allow users to create artificial intelligence (AI) solutions, and they’re predicted to become more popular from 2023 onwards. However, there is not much research on how they can be beneficial for organizations. In this article, we’ll fill some of that research gap by explaining how these user-friendly AI development tools can aid in MLOps and help companies benefit from intelligent systems.

In the past ten years, we’ve seen significant progress in AI, with rapid growth in machine learning and deep learning. This has been possible due to huge increases in digital processing power and data availability, leading to a surge in AI use in various areas. As a result, many organizations are now using AI technologies that can handle vast amounts of data, learn skills and knowledge, and function independently.

The adoption of DevOps practices has sped up software development and deployment cycles. This has given rise to MLOps, a similar approach using Machine Learning (ML), which has its unique challenges due to the dynamic nature of ML models.

Challenges in MLOPS

In the ML workflow, data preparation is first, involving annotation for supervised learning. Then, model development includes testing and adjusting algorithms. The third stage evaluates performance metrics like accuracy. When the model performs well, it’s integrated into the organization (stage four), and in the fifth stage, it’s regularly updated and monitored to stay effective.

However, only a fraction of ML projects make it to the production stage, revealing the need for MLOps. It offers valuable practices, tools, and cultural philosophy to integrate ML models effectively into production. Its importance grows as development teams expand.

Despite its potential, MLOps is still in its early stages, and many challenges lie ahead. This paper discusses these challenges, the benefits of no-code AI, and how to effectively operationalize ML.

Challenge 1: Gaps between business experts and technical experts

Garg et al. (2021) note that the various teams involved in MLOps often aren’t in sync and usually communicate late in the design process. This miscommunication can happen between AI and domain experts because they have different knowledge and objectives.

AI experts usually discuss technical aspects and how to optimize ML models. In contrast, domain experts focus on resolving issues related to their organizational duties. Over time, this could limit the organization’s ability to create valuable datasets and might even perpetuate biases in datasets due to lack of context awareness.

So, it’s important for data scientists and engineers (technical experts) and domain experts (who understand the business side) to work closely together. This is crucial not just in IT and software development, but even more so in AI and ML. This is because the creation and application of these systems heavily involve professional cultures originating from computer and data science.

One of such tools that helps to bring technical and business users together is AINSYS. AINSYS bridges the gap between business users and IT specialists through its unique ETL architecture that normalizes data, handling enterprise-level loads, and simplifies integration into organizational workflows. Its no-code integration framework allows workgroups to create, test, and edit data pipelines quickly without extensive documentation or IT expertise. The system is user-friendly and fosters collaboration among technical and non-technical personnel.

Multithreading allows tasks to be performed in parallel, and a single source of truth allows for efficient distribution of tasks, minimizing error risks. AINSYS also ensures data privacy while working with external developers by providing selective access to the necessary data, and its no-code tools offer unlimited customization.

Challenge 2: Slow problem-solution iterations due to ineffective architectures

Machine Learning (ML) grew out of an academic tradition that involved the use of non-native software packages and academic datasets. However, such solutions don’t scale well and may be difficult to transfer into business settings. They were primarily developed for research purposes, not for implementation in a business environment.

Moreover, these solutions were largely created by data scientists in experimental settings where the goal was to create proofs of concepts and optimize algorithms, not necessarily to create solutions that can be deployed in organizational environments.

Real-world settings are often ‘messier’ than controlled lab settings, so models based on lab work need to be tested in the environments where they are supposed to operate.

While digital-native platform enterprises have already established both ML infrastructures and MLOps, these structures are more challenging to set up for traditional firms. The scarcity of data science expertise, the reliance on non-automated data pipelines, and manual approaches to model training and comparison generally lead to slow iterations between problems and solutions.

Challenge 3: Infrastructure management

As organizations advance in building and deploying ML, the demand for a robust infrastructure capable of handling these processes increases. This includes efficient pipelines for data manipulation, model creation, and deployment.

Additionally, training ML models depends on the availability of powerful hardware like graphical processing units (GPUs). Originally designed for 3D graphics in video games, GPUs are now fundamental to ML workflows as they speed up computationally intensive processes, such as deep learning.

The infrastructure should also support the training of new ML models as the organizational environment evolves, necessitating new datasets. Furthermore, it’s crucial to have systems in place to properly monitor deployed models and ensure they meet key criteria like sustainability, robustness, fairness, and explainability (Tamburri, 2020).

The emergence of no-code AI

A growing trend is the rise of ‘lightweight’ AI platforms that let non-experts train ML models. They are known as AI as a Service or AI service platforms and can often match or surpass coded solutions (Sundberg and Holmström, 2022). Examples include:

  1. BigML
  2. Google AutoML
  3. Microsoft Azure ML
  4. Clarifai
  5. Levity (Pawar et al., 2021)

These platforms have various potential applications and offer low-cost solutions for citizen science, emerging markets, and SMEs. User-friendly, low-code AI could democratize AI adoption and encourage multidisciplinary use. New ‘drag-and-drop’ interfaces simplify AI development, training, and testing, providing flexibility and context-sensitive organization AI development .

Cloud-based ML services further enhance cost-effectiveness by offering pre-trained neural networks and supporting the full development process. Services like IBM Watson and BigML facilitate the ML workflow and integrate with an organization’s IT infrastructure. Researchers call for easy-to-use AI platforms in various operational contexts to encourage usage by non-experts. For instance, despite the potential of ML and DL in medical drug development, the lack of user-friendly, code-free applications hinders their usage.

No-code platforms, with their drag-and-drop interfaces, enable rapid idea testing and project initiation for developers of all skill levels. As these tools become mainstream, organizations of all sizes are increasingly utilizing them. They provide a user-friendly application layer combined with a data science layer, facilitating cost-effective AI development for many companies.

How no-code AI can leverage machine learning operations

We’ve previously discussed three key challenges in today’s MLOps: disconnect between business and IT experts, delayed production of AI models, and the high infrastructural demands due to complexity. Now, let’s explore how no-code AI can help address these issues.

Bridging the Divide between Business and IT Professionals

No-code AI platforms

  1. Allow non-coders to contribute to ML workflows (Yan, 2021).
  2. Enable developers to focus on critical code by automating certain tasks.
  3. Foster skill unity and reduce skill shortage impacts within an organization.
  4. Shift the team’s focus onto important business goals and practical ML applications.

This approach encourages cross-skilling and reduces the reliance on a few key individuals, bridging the gap between domain experts and technical experts such as data scientists. As more employees engage in the ML workflow, no-code AI acts as a facilitator that fosters interactions between different team members, enhancing AI accessibility and promoting AI democratization. These ‘lightweight’ AI platforms enable broader user access, including non-experts, without heavy investment in new skills.

No-code solutions provide domain experts the tools for developing operational AI and ML systems without coding experience. This helps to establish relevant ground truths based on their knowledge and to choose appropriate methods for transfer learning. Activities such as data annotation, augmentation, and ML model output interpretation significantly rely on domain experts’ knowledge (Lebovitz et al., 2021; van den Broek et al., 2021).

These solutions also foster continuous interactions between ML and domain experts, encouraging the formation of cross-functional teams within an organization. Intuitive interfaces simplify processes like data annotation, creating a shared language around AI within an organization.

Benefits for organizations include:

  1. Continuous interactions between ML and domain experts, fostering cross-functional teams.
  2. Ability to diversify by creating and interpreting their own data.
  3. Development of ‘ambidextrous’ ML systems that can adapt to both local and global AI ecosystems.

Platforms like Hugging Face (2023) create communities to share models, scripts, and solutions, and integrate popular models like GPT-4 (Kim et al., 2021). This integration is also seen in Microsoft’s Azure OpenAI service and Amazon’s SageMaker platform. Hence, we expect generative AI models to be part of many future AI solutions.

No-code tool AINSYS acts as a bridge between business users and IT professionals by leveraging a unique ETL architecture that normalizes data, manages substantial data loads, and streamlines integration into organizational workflows. Its no-code integration framework lets working groups swiftly design, test, and revise data pipelines, bypassing the need for exhaustive documentation or advanced IT skills. This system is designed with usability in mind, fostering a collaborative environment among both technical and non-technical personnel.

Multithreading enables simultaneous task execution, and a single source of truth facilitates the effective assignment of tasks, reducing the likelihood of errors. AINSYS maintains data privacy when collaborating with external developers by granting them access only to the necessary data. Moreover, its no-code and low-code tools provide endless customization possibilities.

Facilitating Quicker Iterations between Problem Identification and Solution Development

No-code AI, as found in research, aids rapid prototyping and deployment, thus accelerating time to production. This democratization of machine learning (ML) technology:

  • Helps organizations swiftly develop and roll out AI solutions
  • Bridges the expertise gap
  • Frees up AI experts to focus on core tasks and enables non-technical experts to contribute without coding.

The BigML platform, for example, allows users to easily test and compare hundreds of models at the click of a button. This boosts the efficiency of ML model development and collaboration between teams.

Furthermore, no-code tools:

  • Help businesses rapidly develop AI systems, enabling a quicker time-to-market;
  • Decrease the dependency on hard-to-find AI engineers
  • By ensuring a swift and widespread adoption of ML solutions, no-code AI minimizes the risk of AI projects falling into the proof of concept graveyard.

No-code AI platforms support efficient scaling, offer access to computing resources and expertise, and enable networking and community engagement. The end result is improved performance, flexibility, and cost-effectiveness, vital for a robust inter-firm AI ecosystem.

Assisting in the management of infrastructure

As organizations build and deploy more machine learning (ML) systems, the need for supporting infrastructure increases. This involves data pipelines, training setups, and performance assessment routines. Without a robust infrastructure, ML systems risk being improperly deployed and monitored.

No-code AI enables organizations to concentrate on their primary operations. It promotes responsible AI development and improves security by outsourcing maintenance tasks to service providers, minimizing the need for troubleshooting. This service model also removes the hassle of setting up and managing software and hardware infrastructure.

No-code AI includes integrated functions for pre-processing, designing, and deploying models. It also supports plugins for making predictions using ML models. A well-managed infrastructure allows for precise tracking and rectification of failures, and streamlines the development environment.

These no-code solutions also allow for interaction with external services and data sources, implement security measures, and foster collaborative development. Access to additional resources like pre-trained ML models and cloud services can lead to cost savings and efficiency.

Conclusion

No-code AI platforms offer the opportunity to democratize AI by making it accessible to a wider range of individuals, regardless of their technical background. This inclusion of diverse perspectives and ideas can lead to more effective AI deployment and decision-making processes. Additionally, organizations can save time and resources by utilizing no-code AI tools, reducing the need to hire specialized data scientists or developers.

The availability of no-code AI platforms is increasing, and organizations should integrate their use into their AI strategies. By leveraging these tools, managers can close the gap between business and technology experts, accelerate iterations between problems and solutions, and effectively manage infrastructure. Collaboration between managers and technical experts is crucial to align business goals, data, and model development, ensuring the success of no-code AI initiatives.

Furthermore, no-code AI platforms can be used as a means to achieve responsible AI practices. By forming cross-functional teams and empowering employees to monitor and evaluate the performance of ML models, organizations can detect and address issues such as data bias and harmful predictions. This continuous monitoring and evaluation aligns with the principles of responsible AI and helps ensure that AI systems provide accurate and actionable insights.

No-code platforms like AINSYS streamline the workflow between business users and IT professionals through its unique ETL architecture and no-code integration framework. This user-friendly system promotes collaboration, supports parallel task execution, and maintains data privacy. With unlimited customization possibilities through no-code and low-code tools, AINSYS is an innovative solution for managing your data needs. Ready to revolutionize your data management? Choose AINSYS today.

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