No-Code AI for Enterprise
How every employee can be energized to introduce AI in their field of works without writing any code
Data is an intrinsic and instrumental component for smart decision making. Data could be profoundly leveraged by not only big organizations but anybody to provide a remarkable edge over competitors. This is self-evidence by the relentless reliance of big technology companies on data, novel big data technologies and state-of-the-art machine learning techniques.
Despite the plethora of new breakthroughs from big data, deep learning, machine learning and data mining, trends suggest sluggish usage of the aforementioned AI technologies by small companies and non-data experts. This sluggishness is caused by the scarcity and high cost of data scientists and data engineers.
Data democratization envisions a world whereby every citizen does not only have fast open access to data, but also an incredibly easy access to AI tools that can unravel hidden knowledge from the data without the help of data scientists. Based on this, data democratization is subdivided into Data Access and Machine Learning Democratization.
Data Access
Huge efforts have been made in the past to loosen the access of data within an enterprise. This has led to substantial gains. Specifically, for decades, the storage of data in data warehouses, databases and file transfer systems for clearly delineated projects or tasks has exacerbated the problem of data silo within numerous enterprises. Data silos restrict data access, undermine data sharing and hence hinder organizations from reaping the benefits of data democratization.
With the emergence of highly advanced yet cheap and accessible big data technologies, a significant shift in the way enterprise data is stored has been witnessed. In particular, a staggering number of organizations are moving their data from traditional data warehouse and ERP systems to new storage systems such as datalake, lakehouse and other cloud-based file storage systems. These recently alluded storage systems seamlessly combine and store both structured and unstructured data. Moreover, they provide fast parallel or distributed cluster-based computing functionalities. Such functionalities enable data to be open, quickly searched, aggregated and processed at an incredibly high velocity — thereby dramatically eroding the data access and transparency problems of data democratization.
Nowadays, with most enterprise data in corporate datalake, lakehouse or distributed file systems, every employee can readily access her desired datasets within the organization — if the specified data governance or data security policies are satisfied. This means, the last frontier to effectively accomplish data democratization is to vigorously democratize machine learning.
Machine Learning Democratization
Machine Learning democratization is essential and crucial to re-energize and facilitate small companies to embrace and utilize compelling world-class AI solutions. Machine Learning democratization has the potential to reach and make every citizen a data scientist. It can help every citizen or employee to easily engrain AI when tackling challenging problems — to deliver success of compounding magnitude with no background AI Knowledge.
Machine Learning can be democratized by enabling, empowering and providing these citizens or employees with specialized AI tools that can be used to easily uncover new vivid insights without any coding. This will indisputably accelerate the growth of AI in small and medium sized enterprises.
Simplification of AI and Machine Learning
AI and Machine Learning can be simplified and packaged in a software in such a way that, any user irrespective if she is non-technical or technical can easily assemble and build complex AI pipelines using an intuitive drag and drop user interface. In a nutshell, No-Code AI is a core ingredient to fulfill machine learning democratization. No-Code AI can propel the adoption of AI in an enterprise by non-technical employees and citizen data scientists.
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.
Small and medium sized businesses that adopt No-Code AI would substantially improve their clout with existing customers, defend their market positions, hone current business models and uncover new product differentiation approaches to standout from competitors. Most No-Code AI tools encapsulate complex machine learning pipelines at their backends. They then provide easy to use user interfaces to trigger and yield solid AI results — a move that has clearly fueled the popularity of No-Code AI tools.
As previously mentioned, many organizations are still struggling to incorporate AI due to the lack of data scientists and engineers, and in some cases because of the huge cost associated with building a data team. Yet these same companies in areas as diverse as health care, genomics, sales and industry 4.0 yawn for the introduction and usage of cutting-edge AI to address their problems head-on.
No-code AI systems can provide these companies with a comprehensive solution whereby with few clicks, as well as drags and drops, users can run complex machine learning workflows to solve their industry specific problems.
Robustness of No-Code AI Tools
While No-Code tools are beneficial to enable users with little to no data science experience to run AI models, unfortunately at the moment, some of these tools have limits with regards to the complexity of the machine learning tasks that they can handle. Hence, during No-Code AI tool selection, one has to clearly understand the delineated range of machine learning tasks that the No-Code AI tool is capable of solving.
Many No-Code AI tools can solve simple to medium grade regression and classification tasks. However, when faced with complex enterprise tasks that require multiple data types (e.g., some combination of audio, email, video or social network data) and complex neural network architectures, some of the No-Code AI tools in the market falter and produce dismal results. No-Code AI users can refrain from such pitfalls by carefully choosing a No-Code AI tool that can solve their specific use cases.
Moreover, No-Code AI tools that are expressly designed to handle complex challenging machine learning tasks are increasingly being used by even data scientists — to profoundly improve their efficiencies and accomplish more workload in a far shorter period of time.
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.
No-Code AI Landscape 2021 and Top No-Code AI Products
Below is a list of top No-Code AI tools with the AI verticals where they function best. (In our next No-Code AI post, we would cover in detail these No-Code AI tools. Stay tuned.)
Google Cloud AutoML Vision: Image, Video, Text Recognition, AutoML
DataRobot: General Purpose AI
Evercot AI: Health Care, Sales Opportunity Forecast, AutoML
Create ML: Image Classification
Teachable Machine: Image and Sound Detection, Activity Labelling
Lobe: Image Classification and Activity Labelling
RunwayML: Image Classification and Text Analysis
Clarifai: Data labelling, Object Detection, Image Classification
MonkeyLearn: Document Classification, Text Analysis
NanoNet: Document Classification, Text Analysis
MakeML: Object Detection
ObviouslyAI: Sales Forecast and General Predictions
Levity: Image Classification, Document Classification and Automation
C3 AI Ex Machina: General Purpose AI
Skyl.ai: General Purpose AI
ML Jar: AutoML, General purpose AI
