Maturity of Enterprise No-Code AI from the Prism of CRISP-DM
In this post, we discuss the maturity and benefits of Enterprise No-Code AI, when perceived and compared to a popular data science project management paradigm called CRISP-DM.
Introduction
The rapid proliferation of AI in small businesses or large companies is decelerated primarily because at least a data scientist has to be onboarded prior to solving a problem using AI. For example, quite often, employees or domain experts (e.g., sales reps, marketing managers, engineers, physicians, interns etc.) have access to a pool of data amassed by their company. However, to perform prediction or discover emerging new trends from this data, they need a data scientist or a data team — to build data pipelines and implement machine learning algorithms to make such predictions or unlock the aspired new trends.
No-Code AI provides the aforementioned domain experts an excellent opportunity to employ a No-Code AI tool to perform prediction or unravel new trends from data without any AI or Machine learning experience, and no support from a data scientist or a data team.
Hence, if you are an employee or citizen data scientist that is interested in leveraging AI for your enterprise project but have no or little AI knowledge, don’t hesitate. Try No-Code AI. All you need is data. Any data.
Apart from bridging the AI knowledge gap, the rational for exploring (Enterprise) No-Code AI is immense as highlighted in the next sections.
Fundamentals of No-Code AI
What is No-Code AI
Simply put, No-Code AI is the democratization of AI to enable anybody with even no knowledge of machine learning to build and run AI models without writing a single line of code.
What is Low-Code AI
Low-Code AI presents a user with a user interface to build and run machine learning pipelines with a small amount of code. Low-Code AI tools can also be used by data scientists, as well as technical users with basic knowledge of coding.
In particular, Low-Code AI can be explored by data scientists, software engineers and other technical employees, who intend to incorporate AI into their solutions while speeding up their productivities.
Fundamentals of CRISP-DM
CRISP-DM is a structured project management methodology utilized to execute data science projects. It constitutes of six steps. We will shed more lights on these steps later.
AutoML and No-Code AI Tools Maturity
No-Code AI tools are primarily geared for business and non-technical users. An interesting technology that is offered by some No-Code AI tools is AutoML.
What is AutoML
AutoML is the ability of an AI system to solve a machine learning problem by first synthesizing numerous sequences of Machine Learning (ML) models automatically, followed by the selection of the optimal ML model sequence and its corresponding hyper parameters.
Specifically, when an AutoML is triggered to solve a given Machine Learning tasks, multiple machine learning data pre-processing algorithms and ML models are instantiated and allowed to run on the data. Each run consists of a combination of one or more data pre-processing and ML model algorithms in a sequence called machine learning pipeline. In addition, each run (of a machine learning pipeline) yields a different result. The AutoML then chooses the machine learning pipeline with the best performance and outputs it as the result of the pipeline. In a nutshell, AutoML can be used to automate feature engineering, model selection and parameter optimization.
While No-Code AI is highly used by non-technical users, data scientists can immensely profit from the AutoML capabilities of No-Code AI. Many No-Code AI tools provide basic AutoML, which under the hood continuously search for a good combination of a sequence of algorithms within a pipeline, and then chooses the pipeline with the best result. In contrast, there are other more powerful AutoML algorithms such by Google Cloud AutoML and Evercot AI, which utilize sophisticated machine learning approaches such as Neural Search Architecture.
Since some No-Code AI tools provide AutoML capabilities, No-Code AI is thus matured enough to handle challenging Enterprise AI problems spanning from multitudes of areas such as in computer vision, time series prediction and Natural Language Processing (NLP), etc.
Benefits and Maturity of No-Code AI w.r.t. CRISP-DM
Building an AI solution from bottom up is difficult, time consuming and expensive. No-Code AI provides an alternative approach to directly consume AI without building. The benefits of enterprise No-Code AI are startling in terms of time savings and cost reduction. To properly articulate how huge these benefits are, it would be important to commence by briefly explaining the de facto data science project management methodology used when addressing an AI problem.
Time Saving
CRISP-DM is a structured project management methodology utilized to execute data science projects. It constitutes of six steps. They include, Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. An industrial grade data science project following the CRISP-DM paradigm involves the continuous iteration of the aforementioned steps. The Data Preparation step of CRISP-DM is focused on data pre-processing and feature engineering. Quite often, this is the most time-consuming step — yes, most often it consumes more time than the Modelling step.
With regards to the Modelling step of the CRISP-DM, a vast number of fundamental machine learning algorithms for classification, regression, clustering, knowledge extraction, object detection or prediction are available in numerous public libraries. Based on the complexity of the machine learning problem, in some instances, data scientists directly utilize these publicly available libraries. However, there are use cases that require the data scientist to create a new machine learning model from scratch or substantially extend an existing publicly available algorithm.
Succinctly, this traditional iterative process of data preparation and machine learning model creation within the scope of a data science project (CRISP-DM) may take weeks to months. In contrast, with No-Code AI it takes minutes to hours. The difference is thus stunning and hard to be overlooked.
Cost Savings
Most data science projects require a cross functional team of at least a business representative, at least a data scientist, a project manager and at times data engineers. Optionally, the team may also include software engineers and dev-ops engineers. The cost of assembling and maintaining such a data team through the entire CRISP-DM data science project duration is high. For small or midsize businesses that are not willing to bear the huge cost to setup a cross functional data team, and yet aspire to reap the benefits of AI, a No-Code AI tool is a good proposition.
Machine Learning Result Quality
So far, while the comparisons between No-Code AI tools and a data science project (executed using CRISP-DM) have focused on time and cost savings, no mentions about the algorithmic performance between a traditional CRISP-DM data science project and No-Code AI tool was provided. This is an important aspect that has to be examined, in cases where decisions have to be made between a data science project and a No-Code AI tool. Decision makers have to evaluate the No-Code AI tools vigorously based on their strengths and the complexity grade of the machine learning tasks they can handle. It’s difficult to accurately infer which one would perform better without knowing the use case. But as a rule of thumb, our suggestion would be as follows. For simple and less complex enterprise AI tasks, the qualities of the AI results may not differ significantly between traditional CRISP-DM projects and a good No-Code AI tool. However, for very complex AI projects, data scientists are required and highly recommended to have a deeper look at the AI problem. Low-Code AI may also be used in such a scenario.
Easy Enterprise AI Access and Democratization
No-Code AI enables anybody irrespective of their machine learning knowledge to build and run AI without writing codes. Recently, No-Code AI tools have become very mature and can handle challenging enterprise AI tasks. Thus, No-Code AI has the ability to democratize machine learning, energize and empower employees such as sales reps, marketers, engineers, physicians to directly create complex AI models without coding. This can help companies to quickly identify opportunities or answer key questions that are pivotal to their businesses. In addition, No-Code AI can bring AI democratization in areas such as life science where scientists lack advanced AI knowledge. Such scientists can tap No-Code AI to search and explore the complex relationships and interconnectivity of proteins or molecules — to unlock important feature signatures in areas such as genomics and drug discovery respectively.
No-Code AI profoundly lowers the AI barrier of entry and erodes the stringent necessity that a data scientist has to be onboarded prior to solving a task with AI. Provided the AI task is very challenging, data scientists and Low-Code AI can be used to harness the problem.
Conclusion
No-Code AI solutions provide basic ML techniques, as well as advanced machine learning technology (e.g., AutoML) that can successfully take on Enterprise AI problems. These solutions are today very mature for Enterprise AI and will get more sophisticated in the future due to the high pace technological advancement in this area. In addtion, No-Code AI clearly erodes Enterprise AI cost when compared to CRISP-DM. This is imperative especially for small and medium size businesses where AI bugdets are limited. However, for very large and very complex AI projects, we recommend the use of data scientists and CRISP-DM. Or a hybrid approach, which involves the partitioning of a large CRISP-DM project into smaller sub-projects based on this criteria:
Tasks that can be handled or automated by No-Code AI are allocated to a given sub-project, whereas very complex tasks that need manual scrutiny and critical thinking by data scientists are seperated into a different sub-project.
In this way, a trade-off can be reached to develop solid Enterprise AI solutions that save cost and time by using both CRISP-DM, No-Code AI and Low-Code AI. Moreover, this also helps Enterprises to optimize the leverage of their data scientists — by providing them with really exciting, less boring and challenging data science tasks that most data scientists yawn for.
