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Multiple options make machine learning available to professional data scientists, citizen data analysts and software developers.
Low-code platforms improve the speed and quality of developing applications, integrations, and data visualisations. Instead of building forms and workflows in code, low-code platforms provide drag-and-drop interfaces to design screens, workflows, and data visualisations used in web and mobile applications. Low-code integration tools support data integrations, data prep, API orchestrations, and connections to common SaaS platforms. If you’re designing dashboards and reports, there are many low-code options to connect to data sources and create data visualisations.
If you can do it in code, there’s probably a low-code or no-code technology that can help accelerate the development process and simplify ongoing maintenance. Of course, you’ll have to evaluate whether platforms meet functional requirements, cost, compliance, and other factors, but low-code platforms offer options that live in the gray area between building yourself or buying a software-as-a-service (SaaS) solution.
But are low-code options just about developing applications, integrations, and visualisations better and faster? What about low-code platforms that accelerate and simplify using more advanced or emerging capabilities?
I searched and prototyped for low-code and no-code platforms that would enable technology teams to spike and experiment with machine learning capabilities. I focused mainly on low-code application development platforms and sought machine learning capabilities that enhanced the end-user experience.
Here are a few things I learned on this journey.
Are you a data scientist looking for low-code capabilities to try out new machine learning algorithms and support modelops faster and easier than coding in Python? Maybe you are a data engineer focusing on dataops and wanting to connect data to machine learning models while discovering and validating new data sources.
Data science and modelops platforms such as Alteryx, Dataiku, DataRobot, H20.ai, KNIME, RapidMiner, SageMaker, SAS, and many others aim to simplify and accelerate the work performed by data scientists and other data professionals. They have comprehensive machine learning capabilities, but they are more accessible to professionals with data science and data engineering skill sets.
Here’s what Rosaria Silipo, PhD, principal data scientist and head of evangelism at KNIME told me about low-code machine learning and AI platforms. “AI low-code platforms represent a valid alternative to classic AI script-based platforms. By removing the coding barrier, low-code solutions reduce the learning time required for the tool and leave more time available for experimenting with new ideas, paradigms, strategies, optimisation, and data.”
There are multiple platform options, especially for software developers who want to leverage machine learning capabilities in applications and integrations:
These low-code examples target developers and data scientists with coding skills and help them accelerate experimenting with different machine learning algorithms. MLops platforms target developers, data scientists, and operations engineers. Effectively the devops for machine learning, MLops platforms aim to simplify managing machine learning model infrastructure, deployment, and ops management.
An emerging group of no-code machine learning platforms is geared for business analysts. These platforms make it easy to upload or connect to cloud data sources and experiment with machine learning algorithms.
I spoke with Assaf Egozi, cofounder and CEO at Noogata, about why no-code machine learning platforms for business analysts can be game changers even for large enterprises with experienced data science teams. He told me, “Most data consumers within an organisation simply do not have the required skills to develop algorithms from scratch or even to apply autoML tools effectively—and we shouldn’t expect them to. Rather, we should supply these data consumers—the citizen data analysts—with a simple way to integrate advanced analytics into their business processes.”
Andrew Clark, CTO and cofounder at Monitaur, agreed. “Making machine learning more approachable to businesses is exciting. There are not enough trained data scientists or engineers with expertise in the productisation of models to meet business demand. Low-code platforms offer a bridge.”
Although low code democratises and accelerates machine learning experimentation, it still requires disciplined practices, alignment to data governance policies, and reviews for bias. Clark added, “Companies must view low code as tools in their path to benefiting from AI/ML. They should not take shortcuts, considering the business visibility, control, and management of models required to make trusted decisions for the business.”
Now let’s focus on the low-code platforms that provide machine learning capabilities to software developers. These platforms select the machine learning algorithms based on their programming models and the types of low-code capabilities they expose.
This is not a comprehensive list. One list of low-code and no-code machine learning platforms also names Create ML, MakeML, MonkeyLearn Studio, Obviously AI, Teachable Machine, and other options. Also, take a look at no-code machine learning platforms in 2021 and no-code machine learning platforms. The possibilities grow as more low-code platforms develop or partner for machine learning capabilities.
Low-code platforms will continue to differentiate their feature sets, so I expect more will add machine learning capabilities needed for the user experiences they enable. That means more text and image processing to support workflows, trend analysis for portfolio management platforms, and clustering for CRM and marketing workflows.
But when it comes to large-scale supervised and unsupervised learning, deep learning, and modelops, using and integrating with a specialised data science and modelops platform is more likely needed. More low-code technology suppliers may partner to support integrations or provide on-ramps to enable machine learning capabilities on AWS, Azure, GCP, and other public clouds.
What will continue to be important is for low-code technologies to make it easier for developers to create and support applications, integrations, and visualisations. Now, raise the bar and expect more intelligent automation and machine learning capabilities, whether low-code platforms invest in their own AI capabilities or provide integrations with third-party data science platforms.
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