With many big players in cloud platforms such as Microsoft, Google, Amazon, and others jumping into the AI (Artificial Intelligence) bandwagon to develop low/no code, what are the benefits and drawbacks of such a solution for highly developed and complicated?  The problems are huge and investments in capital and manpower are needed to solve AI issues.  As a result, many cloud-based platforms are trying to come up with low/no code solutions to develop and implement AI.

As mentioned previously the benefits of low code no code development cycle and platforms.  The benefits outweigh the drawbacks.  However, there are limitations to the low code no code approach such as security, learning curve, and lack of trust from IT managers and professionals.  This is especially true for such a demand in the field of AI.  Yet, there is a shortage of knowledgeable professionals and demanding jobs that can lead to citizen developers tackling AI low/no code developments.  According to Gartner, by 2025 some 70% of new apps will use low/no-code platforms.

Although major vendors such as Microsoft are making significant investments in this AI low/no code, it is still in its early stages.  Traditional application development frequently calls for specific knowledge. Organizations today suffer skills and resource shortages in the IT, software engineering, and digital business sectors, but other factors may have contributed to that widespread adoption.

Even if businesses and decision-makers in the business sector are convinced of the potential and strength of AI, many of them are still unable to begin putting it into practice. The main causes are costs and a lack of highly qualified talent. In a Forbes research, 83% of businesses claim that AI is a strategic priority for them right now, yet there is a shortage of data science skills.

The demand for all tech specialists has grown as businesses scramble to adopt the newest high-tech advancements, but the supply is failing to keep up. And this is particularly true in the field of AI. But the talent pool for AI is still quite small. The entrance barrier level for this profession is very high, and educational institutions are unable to generate skilled and qualified graduates quickly enough to meet corporate demand since AI involves sophisticated competencies in mathematics, statistics, and programming.

Even small and medium-sized businesses can easily use AI technologies thanks to visual low-code and no-code platforms. Through the use of third-party APIs, they enable organizations to create many types of custom applications and include various AI and machine learning (ML) components into them.

Let’s examine some of the key benefits of low-code AI compared to the conventional method of developing AI-focused solutions in order to comprehend why low-code platforms are unquestionably the future of developing workplace AI solutions.

In essence, low-code platforms let businesses build fully operational AI-powered products without having to pay expensive software engineers, AI experts, or other technical expertise. The cost of integrating AI into routine corporate activities is significantly reduced by using low-code platforms.

Because of the low-code platforms’ straightforward user interface, more people within an organization may participate in the creation and execution of a unique AI-powered solution and offer their perspectives, which frequently leads to a more useful and effective final product.

Speed is another key benefit of low-code platforms. Due to how quickly AI speeds up the creation and deployment of corporate apps, this enables businesses to be more adaptable and test out several iterations or methods of a given product.


Cloud-based Platforms Low/No Code For AI


Google’s Vertex AI provides end-to-end integration on the Google Cloud Platform by being natively linked with data platforms including BigQuery, Dataproc, and Spark. Along with integrating with open-source frameworks like TensorFlow, PyTorch, and scikit-learn and supporting all ML frameworks, Vertex AI leverages customized containers for training and prediction. The models that are created by developers can be trained and developed using AutoML, and they are all kept in a single model repository.

Furthermore, Vertex AI unifies the AutoML and AI Platform APIs, client libraries, and user interfaces. While AI Platform training allows you to execute bespoke training code, AutoML allows you to train models on picture, tabular, text, and video datasets without writing any code. Both bespoke training and autoML training are possibilities accessible with Vertex AI. Vertex AI allows you to deploy models, save models, and ask for predictions regardless of the training method you select.

Microsoft’s project is called Bonsai Brain which aims to model and develop an AI component that can be used in a variety of autonomous tasks and applications. The Bonsai brain has received training and practice in managing unforeseen circumstances and keeping operations running smoothly. The huge reduction in downtime brought on by increased production efficiency is its main selling point. For automation tasks, larger neural networks must be created, however, Bonsai’s brain works without trained or simulated neural networks.



Any engineer or business user can develop ML predictions using point-and-click choices and automatically merge their data to create individual or batch forecasts using the Amazon Sagemaker Canvas.  Due to the excessive demand for cloud-focused talents and the belief that the solution will help with the existing skill-gap issues, Amazon intends to increase the no-code/low-code portfolio. With no-code/low-code, the business also hoped to appeal to non-technical users.

By removing the requirement for users to write any code, AWS hopes to simplify the development of AI using SageMaker Canvas. The industry leader in cloud computing claims that the tool doesn’t also require substantial expertise in machine learning techniques.

A SageMaker Canvas user must first supply a training dataset in order to create an AI model. Employees have the option of importing data from their company’s internal systems or uploading the training dataset as a spreadsheet. SageMaker Canvas can access data from Amazon S3 records, on-premises databases, and other cloud sources including the Amazon Redshift data warehouse, and other sources.  Users can choose to combine the many training datasets they import into a single file for their AI applications. Important data preparation processes are automated using SageMaker Canvas. The tool assists in locating problems like empty spreadsheet fields.