The future of decision intelligence is here—and it’s low-code.
While traditional data science tools like Python, R, and Tensorflow can be used to custom-build any Machine Learning (ML) application from scratch, they require substantial investment in technology and talent on a rolling basis as the technical landscape of analytics is ever-changing and continuously evolving. At the other end of the spectrum are downstream analytics tools like Excel are not scalable, while other tools like Tableau and Power BI are business intelligence tools that enable users to explore data using a fixed structure. They cannot match the functionality, customizability, and flexibility in harnessing the powers of Artificial Intelligence(AI) using ML for generating market-winning, real-time insights.
This gaping hole in the business analytics domain has long been an unresolved puzzle – until recently. The industry is now striding ahead towards bringing the power of high-end data science to the fingertips of business users and citizen data scientists, and is simultaneously looking at reducing the data-to-decision lifecycle to a fraction of what it was. This is being achieved by molding traditional application development platforms in data science into their low-code/no-code versions.
FORMCEPT’s flagship product, MECBot, has always been several paces ahead of this challenge by having a highly customizable application development layer with out-of-the-box ML modules that cater to predefined business use-cases like customer retention and loyalty, market segmentation, churn rate optimization, and many more.
In this blog, we discuss the need for democratizing traditional data science, the emergence of low-code ML solutions for businesses, the importance of ML-Ops (a set of best practices in ML to deploy and manage ML systems and components in an agile and standardized manner) in making this happen, and finally, the prerequisites for low-code ML projects to be successful.
The Need for Democratizing Traditional Data Science
Relying Heavily on Traditional AI-ML Leads to the Accumulation of Huge Technical Debt
To leverage traditional data science and analytics successfully, a team of data scientists, analysts, and engineers need to be hired and retained by businesses, which in turn leads to a sharp rise in the fixed costs of businesses. Furthermore, in traditional AI-ML, every time businesses need to manually repeat the same set of steps in the data-to-dollar lifecycle, i.e., whenever a solution needs to be created from the ground up for a new business problem.
This leads to tiresome and costly delays for businesses in bringing their data science projects to fruition. Besides, making the data ML-ready takes an immense amount of time, and by the time production and deployment of the model finally takes place, either the insights become obsolete, or the project is abandoned prematurely by businesses due to long turnaround time and lack of visibility into the potential value to be gained from it. All of this leads to mounting technical debt for enterprises that can quickly spin out of control.
Recently, an interesting study by Alegion and Dimensional Research found that only about 22% of AI model training projects are on track and did not get prematurely stalled. Meanwhile, the same study showed that about a third of AI/ML projects get stalled at the proof of concept phase.
Image Courtesy: Alegion and Dimensional Research | “What data scientists tell us about AI model training today.” | Source URL: Link
Only 13% of data science projects make it to production. A 2018 Kaggle survey found that 41% of data science projects are focused on compiling and cleaning data, 20% on selecting and configuring a model, and the remaining 9% on putting the model into production and deploying it. The average data science project takes several months, or in some cases, even more, to deliver results that actually aid in business decision-making. Moreover, according to a study, only 4% of enterprises have succeeded in deploying ML models to production.
What Causes Technical Debt to Spin Out of Control When Using Traditional AI-ML?
Data Science Talent is Expensive, Scarce, and Hard to Retain
Data science is easily one of the most talent-hungry industries in the world. 83% of executives say AI is a strategic priority for their businesses, but high-quality data science expertise is scarce. A study by Deloitte reports that 31% of companies rank “Lack of Skills” among the top 3 challenges for their AI initiatives.
According to a Quanthub report, there are three times more data science job postings than job searches, even though the median annual salary of data scientists in the U.S. is ~$98k. Thus, hiring a team of competent data science professionals, retaining them, and training them continuously to keep them abreast with the latest advances in technology makes a dent in the budgets of enterprises, and causes the success of AI-ML projects to be more elusive than ever.
Image Courtesy: Quanthub | Source URL: https://quanthub.com/data-scientist-shortage-2020/
Data Representation Challenges: Underfitting and Overfitting
Developing ML models requires data to be cleaned, unified, and pre-processed into continuous pipelines. After all, the ML model or the code that is written is just the tip of the iceberg – a small part of the larger ML framework, as shown in the figure below.
For a model to work, the data fed to it must be a good and balanced representation of the actual population. If the data is too generalized, or too specific, or has too many outliers, the model will be biased and ineffective. Furthermore, if the data is cluttered with noise or not well validated, the model will descend to chaos right at the start (during the training phase).
Two specific representation problems arise during the development of an ML model:
- Overfitting: When the data matches the model parameters perfectly and has no outliers or anomalies. Such data have over-representation bias which leads to high-risk decisions.
- Underfitting: When the data doesn’t match the model parameters at all and is completely scattered without any patterns that the ML model can work with. Such data have under-representation bias which leads to low-return decisions.
Further, for ML models to function as truly independent of people and infrastructure, businesses should be able to deploy models to any environment without the need to re-work their data pipelines every time.
The Underlying Space, Time, and Power Limitations of Legacy Infrastructure
Most legacy infrastructure is limited by their “latency,” or their inherent space, time, and power constraints. The limitations of the underlying data infrastructure can be captured by asking three simple questions:
- How much data can the system store at a given point in time? (Space constraints)
- How fast can the system process this data using the model algorithms? (Time constraints)
- How many layers of complexity in the data can the system manage at once? (Power constraints)
Ensuring near real-time analysis of large volumes of disparate data on such infrastructure requires creating a radically different data pipeline that can –
- Overcome the limitations of space by reducing dimensional complexity in data.
- Adjust the usage and availability of storage based on dynamic requirements.
- Overcome the limitations of time by optimizing the usage of processing power to model usability at any given point of time, thereby decreasing the time-to-process, and ultimately, the time-to-market.
Low-Code ML Platforms: An Overview
The premise of a low-code/no-code decision intelligence platform is to combine the high-end functionality of traditional data science with the ease of use and flexibility of drag-and-drop application development platforms.
According to Gartner, low-code application development will be responsible for more than 65% of application development activity by the year 2024. This is particularly significant for intelligent decision-making applications for businesses, the lion’s share of which comes from powerful advances in ML models.
Difference Between Low-Code and No-Code
Along with “low-code,” a related buzzword that is doing the rounds in the tech space right now is “no-code.” While some prefer to use the two terms interchangeably, it is useful to distinguish between them. No-code ML application development solutions are drag-and-drop platforms to create ML models that prevent users from making modifications to the pre-defined code structure of the product or application. Low-code ML model development platforms, on the other hand, not only come with pre-built, plug-and-play ML components but also allow the user to access and make modifications to the backend code, and hence, enable deeper customization.
Next-gen ML Innovation for Enterprise Analytics
Where Fast, Visual Low-Code Application Development Meets the Robustness and Agility of ML-Ops
The iterative nature inherent to building ML models means that they need to be trained and validated multiple times before pushing to production. Manually performing all these deployment steps is both time-consuming and labor-intensive. ML-Ops come in handy in making this happen – i.e. making deployments possible across any on-cloud, on-premise, or hybrid environment irrespective of the underlying infrastructure. ML-Ops constitute a set of best practices in ML to deploy and manage ML systems and components in an agile and standardized manner.
ML codes by themselves mean nothing if they do not enable data-driven decision-making for a larger goal or problem of your business, such as improving customer retention, increasing sales conversion rates, reducing wastage, mitigating risks, and so on. The success of plug-and-play ML application development depends on successful ML-Ops integration and a number of related factors such as:
- The drag-and-drop ML development environment should be scalable and production-ready from day one.
- It should be suitable for collaboration and re-use by design.
- Production and deployment workloads should be auto-scalable where possible. The environment should also enable complex model deployment scenarios such as A/B deployments, shadow rollouts, and phased rollouts, and rolling out post-deployment changes based on live recommendations.
- Users should have the ability to auto-monitor the health of different models and the underlying resource utilization.
- The solution should perform elastic scaling of the computing and IT resources like CPU, GPU, and memory resources to reduce costs and up-time.
- The integrated and end-to-end ML-Ops practices should provide the user with the ability to observe (also known as observable AI) the model behavior and recommend actions based on insights in a self-explainable manner (also known as explainable AI).
Businesses That Can Make the Most of Low-Code ML
Like most data science applications, there is no one-size-fits-all when it comes to low-code ML. Here are a few crucial indicators for businesses to determine whether opting for the same would be beneficial for them.
Auto-deployment of ML-ready Data Pipelines
At the outset, for drag-and-drop ML solutions to work without hiccups, businesses need to have in place pre-processed, cleaned, and ML-ready data pipelines which can be fed to the ML models and be auto-deployed using any ML libraries, frameworks, or AutoML tools of their choice.
Automated Version Control and Governance
Since the low-code ML framework essentially works as a CI/CD (Continuous Integration and Continuous Delivery) environment, version control is an essential prerequisite to enable automated re-use of the existing data pipeline. This means that any modifications in the source code should be reflected in the CI/CD framework in near real-time by re-using the underlying pipeline to create the new and modified production-ready code.
Combined with ML-Ops, businesses with the ability to implement audit, compliance, access control, and spontaneous testing and validation will be in a much better shape to execute low-code ML in a secure and risk-free manner. Modification logs, access logs, and admin privileges like version roll-back can prevent unwanted modifications and malicious tampering of the data and the models.
Use Case Discovery and ML Model Marketplace
A visual, drag-and-drop platform for ML application development will be of little use unless the user has a way to discover best-fit models based on the use cases, the available data, and the specific decision-making requirements. Hence, instead of the platform being a blank slate, it should contain libraries of reference models for specific use case scenarios and recommendations for the most suitable model in a given scenario. An ML model marketplace with pre-defined templates and modules with a catalog of anticipated outcomes would help business users quickly identify the model to go ahead with, given the demands of the problem at hand.
To operationalize ML models and extract valuable insights from them to aid in executive decision-making, data scientists need to work in tandem with a host of other teams such as operations, marketing, customer care, sales, finance, and product/service teams. This leads to major organizational challenges with regard to internal and external communication, cross-team collaboration, and functional coordination. We at FORMCEPT are committed to helping you mitigate such organizational and technical challenges during model development and delivery, without compromising on agility, simplicity, and speed of execution.
How MECBot Can Help
Our Award-Winning Product Makes Low-Code ML Work for You in an End-to-end and Hassle-free Way
No matter what your technology, talent, or infrastructure constraints are, we can bring the powers of next-gen ML models to your fingertips and make your mission-critical business decisions market-winning.
If you already have an ML-ready data pipeline, the Plug-and-Play ML module of our award-winning product, MECBot, will help you to unleash the power of ML for unprecedented business intelligence and real-time insights within hours, not days or months. If you don’t have an ML-ready data pipeline or have infrastructure constraints that need to be overcome, MECBot’s Data Preparation module will do it for you within less than half the benchmark time required by traditional solutions.
MECBot is our flagship data excellence platform for all enterprise AI needs that acts like a Google-like assistant for business users and data teams to help them make sense of their data and address their queries as and when they arise. Insights on MECBot are available as a self-service, without having to depend on data scientists every time, and all insights are accessible on-demand through free form search and ad hoc querying.
Powered by innovations in AI, Machine Learning, Natural Language Processing (NLP), and Deep Learning, MECBot puts your business first by adopting the Business Domain Entity-Model approach without any dependence on the underlying databases or the structure of the data. With MECBot, a business model can be directly created by CXOs, Data Scientists, or Data Analysts, or all of them collaboratively. MECBot directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model.
With all the excitement around low-code ML solutions, this blog was intended to share our take on what it takes to make it work. In the next blog, we will take a deeper dive into MECBot as a one-of-a-kind, low-code application development platform for ML, and how it can transform the way you solve the pressing problems of your business forever.