Artificial intelligence (AI) can transform every business, from giving customers the products and services they really want, to streamlining internal processes.
However, from investing in infrastructure to training and hiring the skilled workforce required to bring it all together, the barriers to entry can seem intimidating.
10 best examples of low-code and no-code AI
adobe stock
This is why the emergence of a new generation of no-code/low-code AI tools and platforms is so exciting. Today, just about anyone who knows where to look can jump in and start building applications that leverage machine learning in innovative ways. From designing web services and customer-facing apps to coordinating sales and marketing campaigns, getting started with AI has never been easier.
What is low-code/no-code AI?
These terms are used to refer to tools that allow anyone to create AI applications without the need to get their hands dirty and write technical code. AI can help everyone in almost every job, from doctors and lawyers to marketers, teachers, and project managers. Many of these people don’t have the technical skills needed to write code or the free time to learn it.
No-code/low-code solutions typically work in one of two ways: A wizard where the user selects the elements they want to include in the application through a drag-and-drop interface and combines them using a visual interface, or where the user answers questions and selects options from drop-down menus.
If you already know how to code, it’s possible to tweak and tweak the results to create an application that behaves in a more specific way. So a basic knowledge of computer code structure and syntax is always helpful.
Here’s an overview of some of the tools on the market aimed at opening up the AI revolution to everyone. Some of these are designed for people with no experience at all, others already have an ML background but want to reduce the tedious and routine elements involved in data preparation and algorithm design Some are most useful to humans.
Amazon SageMaker
Amazon has extensive experience building and deploying ML models for consumer use cases, and SageMaker aims to put that expertise in the hands of everyone. Using SageMaker Jumpstart is easy. This allows users to choose templates for the most common types of ML apps that companies are likely to benefit from.
Akio
The service promises that you can start deploying AI in 10 minutes without any coding or data science skills. It is focused on enabling the creation of AI-powered workflows and enabling rapid deployment and evaluation of workflows. We also have a powerful suite of integrations, including industry-standard data platforms like Snowflake and marketing tools like Hubspot and Salesforce.
Apple CreateML
Apple’s solution provides simple drag-and-drop functionality that makes it easy to create iOS applications that include recommendation, classification, image recognition, and text processing. You can collect data using your iPhone’s camera and microphone. If you have a Mac computer with a GPU, you can take advantage of that power to speed up and enhance your training process.
data robot
It is another cloud-based platform that automates data preparation and provides tools for building and deploying algorithms for industrial use cases ranging from banking and retail to healthcare, manufacturing and public sector. It has its own model. One interesting feature is the focus on explainable AI. It aims to increase confidence in AI-generated insights and decisions by making its methods comprehensible to humans.
Google AutoML
Google’s first no-code AI solution isn’t for complete beginners. Some understanding of machine learning is recommended. However, users can start with a simple graphical interface and jump right into experimenting with computer vision and natural language processing capabilities. Everything runs on his Google Cloud, so anyone who has used other Google productivity tools will feel familiar.
Google teachable machine
Teachable Machine is perhaps more beginner-friendly than AutoML, with simple and straightforward tutorials that guide you through the process of training algorithms to classify and classify data, one of the most rudimentary use cases of ML and AI. I’m here. It’s probably most useful as teaching material to get you started with the basics before diving into one of the other platforms aimed at creating production applications.
microsoft robe
A simple tool for training image recognition algorithms. Microsoft developed Lobe to help users understand the basics with a platform that automatically selects the model that is most likely to succeed depending on the user’s workload. No coding experience required. If your users go beyond that, they can move to Azure AI, Microsoft’s more advanced ML framework.
nano net
It is an AI platform specifically designed to automate and accelerate the process of extracting structured or semi-structured data from documents. If you’re spending time and money on costly and time-consuming processes that involve importing data from forms, text documents, and more, this is exactly what you’re looking for. Thanks to our ML implementation, we can learn from our mistakes and find the information we need more accurately.
Obviously AI
Another platform intended to make it easy for anyone to plug in their data – whatever form they happen to have – so they can immediately reap the benefits of AI-powered analytics. It is intended to Templates for time series analysis (predicting the value of a variable at a given point in time based on known past performance), churn prediction, risk scoring, fraud detection, and identifying cross-selling opportunities Offers.
PyCaret
This is a library for the programming language Python, so it requires a little more technical knowledge than the other tools listed here. However, it is categorized as low-code as it provides a large number of pre-configured functions and wrappers that greatly simplify the tasks of data preparation, analysis and model training.
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