Businesses in a variety of sectors, including e-commerce, marketing, banking, etc., are gradually beginning to understand the strength and effectiveness that modern technologies like artificial intelligence and machine learning can provide. Even though it should be obvious that adopting robust technologies like these, which were previously only accessible to large organizations, has many benefits, doing so often comes as an afterthought for businesses.
This happens for various reasons. Mastering machine learning is difficult, frustrating, and time-consuming. Traditionally, AI methods are complex and this can cause a disconnect between business owners and developers. Artificial intelligence requires programmers and developers with extensive coding knowledge, and as the need for developers has grown, they are more difficult to find and train than ever before. It is clear that conventional forms of AI are only feasible for some.
Fortunately, no-code and low-code machine learning technologies are available. These will help you to mitigate many of the existing pain points that businesses seeking to leverage these technologies face. In this article, you'll learn more about no-code and low-code ML development and how you can benefit from it. Let's get started.
What Is Low Code and No Code Development?
This low-code no-code (LCNC) concept remains perplexing and is frequently confused with developer tools and API products. Let's get a better sense of the overall picture. Modern software deployment generally follows a six-stage life cycle, with each stage subdivided further. The fundamental idea behind LCNC is that it minimizes one or more stages of the software development lifecycle.
Without writing a single line of code, non-technical people can create applications thanks to no-code development. The level of abstraction in no-code products is so high that users can begin without any programming experience. Wix, Zapier, Webflow, and Airtable are only a few examples of no-code tools. Low-code allows developers to create applications with little or no hand coding as possible. Appian, Mendix, and QuickBase are some examples of low-code platforms.
Both can help you develop faster. No-code is better suited for basic application requirements, whereas low-code can handle more complex requirements. No-code is easier to maintain because there is literally no code to maintain, whereas low-code will require additional effort and time if changes to the code base are required.
Why would you even think about using no or low-code? Ask yourself, why would you reinvent the wheel when there is a tool available to help you build a portion of your application, or even the entire application, faster? Especially if you're a startup, you want to create an MVP or minimum viable product as soon as possible. This is so that you can bring it to market and validate your idea. Why waste time and money building an entire application from scratch only to discover that your idea isn't going to work?
LCNC products usher in a new era in which designing and building software products no longer require formal programming training. In recent years, we've seen new products emerge that focus not only on simple graphical interfaces but also on all aspects of a complex application, including front and back-end logic, database operation and maintenance, and other use cases, providing true end-to-end solutions with no code.
The level and amount of abstraction are the most significant differences between low-code and no-code. No code is about empowering users to create software that they were not previously capable of, whereas low code is about freeing engineers from writing low impact code repeatedly.
No Code and Low Code Machine Learning
It is no secret that machine learning is challenging. It takes time for small and medium-sized businesses to understand this branch of artificial intelligence and even longer to apply it successfully to solving business challenges. Among the reasons for this are a need for more sufficient infrastructure to run ML models, an inability to select the right algorithm, and a data science talent shortage.
Attempting to overcome these challenges one at a time may not be a cost-effective strategy for small and medium-sized businesses. Thankfully, machine learning platforms with low and no code are now becoming more popular, making machine learning much more accessible.
Machine learning platforms with low and no-code allow businesses to implement machine learning without having deep industry expertise. By using no-code and low-code platforms, developers with minimal or no formal software development training can create machine learning applications and reduce the burden on data scientists.
More specifically, they allow, for example, smaller businesses to experiment with machine learning and larger businesses to free up their data scientists to work on more complex projects. There are tools, for instance, that allow users to make data predictions without writing code, tools that transform unstructured data into actionable insights, and tools that allow users to create object detection and segmentation models without writing code.
No-code and low-code machine learning solutions make it simple to create and train ML models, much like how no-code and low-code development platforms are used to develop software applications quickly without coding and with little to no coding, respectively.
You can safely assume that the goal of low-code and no-code machine learning tools is to popularize artificial intelligence and lower the entry bar. These tools have already begun to upend the machine learning industry and persuade more companies to use ML.
Best No Code or Low Code Machine Learning Platforms to Consider
Several tools are designed specifically for machine learning, and the landscape is constantly changing as new platforms emerge. The following are some popular tools, but this is by no means an exhaustive list of all the tools available.
PyCaret is an open-source machine learning Python library that makes it easy to do deep learning in the cloud with minimal coding. It supports TensorFlow, Keras 2 and PyTorch 1.0+ with pre-trained models, as well as custom models created in TensorBoard and saved as JSON files. It has support for image classification, object detection, text classification, and speech recognition. PyCaret is a low-code solution that can replace large numbers of code lines with a few words. It significantly accelerates software development and makes it more approachable to beginners.
Google AutoML is a no-code machine learning project created by Google that seeks to bring artificial intelligence into every part of our lives, allowing developers with only limited machine learning skills to train high-quality models specific to their businesses. The platform's applications range from computer vision and video intelligence to natural language processing and translation. It offers free tools such as AutoML Vision, which uses machine learning to identify objects in images; AutoML Vision Express, which enables users to caption images automatically; and AutoML Vision Transfer, which transfers trained models from one application to another.
H2O AutoML is an open-source platform that enables developers to create machine learning solutions using Apache Hadoop's distributed file system (HDFS) and Apache Hive's SQL interface. This can be done by using the H2O AutoML API or by creating a RESTful web service using H2O Web Services Manager. In addition, to support for HDFS/Hive and Spark/Spark Streaming, this platform also supports Amazon Elastic MapReduce (EMR), Microsoft Azure Machine Learning (MML), IBM Watson Natural Language API, and Google Cloud ML Engine APIs.
MakeML is a Python library that has been designed to help developers build, manage and deploy their machine learning models in an easy way. This platform is intended to develop neural networks for object detection and segmentation. It provides an iOS developer with a macOS app for creating and managing datasets.
Auto-ViML is a machine learning platform that uses minimal code and the user's own data for training. The platform has a drag-and-drop interface and works with any dataset, including text, images, audio, video, and other files.
Obviously AI is a tool developed by Microsoft Research with an aim to help researchers build better machine learning systems without requiring them to become experts in programming languages such as Python or Java. This platform performs complex operations on user-defined CSV data with the help of advanced NLP. Marketers and business owners can use it to forecast revenue flow, improve operational efficiency, create a more efficient supply chain, and run customized automated marketing campaigns.
Create ML is a no-code drag-and-drop Python library that provides functions for creating visualizations of deep neural networks and other machine learning models. It supports automatic inference and inference on multiple GPUs. Currently, CreateML is a macOS app with a variety of pre-trained model templates.
Whether you are a seasoned developer or have never written code, low-code and no-code machine learning platforms offer a simple way to build websites, software products, and applications and generally leverage machine learning capabilities. You can upload your own models or train them using these platforms' various tools. This will assist you in showcasing your creativity to a larger audience. Therefore, select the top low-code or no-code machine learning platform of your choice to build your applications and sites with improved productivity, collaboration, and ROI while building your ML models and datasets at lightning speed.
At Solwey, we understand technology and can leverage the most suitable tools to help your business grow. Reach out if you have any questions about machine learning, and find out how Solwey and our custom-tailored software solutions can cover your needs.