In today’s tech landscape, the accessibility of artificial intelligence (AI) has reached unprecedented heights, thanks to the proliferation of Low-Code and No-Code AI tools and platforms. These innovative solutions empower individuals and small to medium-sized businesses to harness the power of AI without diving into the complexities of coding. Below, we’ve compiled a list of top No-Code AI tools, each with its unique features and capabilities.
1. MakeML (https://www.makeml.app/)
MakeML simplifies machine learning model creation for object identification and segmentation without the need for coding. This online resource provides comprehensive training and video tutorials for building AI software and applying Computer Vision to real-world problems. MakeML offers a code-free or low-code development environment, complete with single GPU cloud training and limited dataset import/export capabilities.
2. Obviously AI (https://www.obviously.ai/)
With Obviously AI, making accurate predictions becomes effortless, even for non-coders. This platform allows you to create machine learning algorithms with a single click, modify datasets using a user-friendly interface, and integrate ML models across your organization. It’s suitable for applications like revenue forecasting, supply chain planning, and targeted advertising.
3. SuperAnnotate (https://superannotate.com/)
SuperAnnotate is an end-to-end AI solution for tasks such as annotating, managing, and versioning data. It accelerates AI pipeline scaling and automation, particularly in high-throughput data annotation for video, text, and images. Project management tools and active learning features enhance the annotation process.
4. Teachable Machine (https://teachablemachine.withgoogle.com/)
Teachable Machine empowers you to train a computer to recognize voice, gestures, and images without any coding. This web-based low-code machine learning platform enables the rapid development of versatile machine learning models. You can easily integrate these models into applications, websites, and more, and they work locally on your device, ensuring data privacy.
5. Apple’s Create ML (https://developer.apple.com/documentation/createml)
Create ML by Apple provides a Mac-based approach to training machine learning models. It allows you to train multiple models simultaneously, each with a unique dataset. Create ML’s user-friendly interface includes features like model evaluation, performance tracking, and real-time previews, making it a valuable tool for various applications.
6. PyCaret (https://pycaret.org/)
PyCaret automates machine learning workflows in Python, reducing the need for extensive coding. This modular platform offers functions that perform various machine-learning tasks, from data preprocessing to model deployment. It’s a great choice for those who want to focus on analysis rather than coding.
7. Lobe (https://lobe.ai/)
Lobe makes it easy to train your applications to recognize various inputs, from plants to emotions, without any coding experience. This platform supports local training, eliminating the need for online data storage, and offers cross-platform compatibility.
8. MonkeyLearn (https://monkeylearn.com/)
MonkeyLearn offers powerful AI tools for cleaning, visualizing, and labeling data, making it an ideal choice for text analysis tasks. It allows you to generate data visualizations, create custom machine learning models, and use pre-trained classifiers for tasks like emotion analysis and entity extraction.
9. Akkio (https://akk.io/)
Akkio simplifies AI model building for prediction tasks, such as lead scoring, text classification, and churn reduction. It also provides data cleaning capabilities and an intuitive interface for non-technical users across various domains.
10. Amazon SageMaker (https://aws.amazon.com/sagemaker/)
Amazon SageMaker is a comprehensive cloud-based ML platform that offers no-code and low-code tools for creating, training, and deploying ML models. SageMaker Canvas, with its drag-and-drop interface, streamlines the model development process, making it accessible to non-technical users.
11. DataRobot (https://www.datarobot.com/)
DataRobot streamlines the entire machine learning lifecycle with its no-code platform. It enables data scientists and business users to create and deploy models easily, even without extensive coding skills.
12. Google AutoML (https://cloud.google.com/automl)
Google AutoML allows programmers and data scientists to build machine learning models without hand-coding. It offers pre-trained models and an intuitive drag-and-drop interface for model creation and deployment.
13. NanoNets (https://nanonets.com/)
NanoNets is an AI platform that simplifies model training, particularly for structured data and OCR tasks. It offers efficient document processing and OCR technology, making it a valuable tool for data extraction from documents.
14. IBM Watson Studio (https://www.ibm.com/cloud/watson-studio)
IBM Watson Studio provides a central hub for creating, deploying, and managing AI models. Its no- or low-code features, along with pre-trained models, enable non-technical users to build AI models for various applications.
15. H2O Driverless AI (https://www.h2o.ai/products/h2o-driverless-ai/)
H2O Driverless AI streamlines the machine learning lifecycle, from data preprocessing to model deployment, without requiring extensive coding. It’s a valuable tool for data scientists and business users alike.
16. Domino Data Lab (https://www.dominodatalab.com/)
Domino Data Lab is a cloud-based service that facilitates creating, deploying, and managing machine learning models. It offers low- or no-code options, making data science operations accessible to a wider audience.
17. CrowdStrike Falcon Fusion (https://www.crowdstrike.com/products/falcon/fusion/)
CrowdStrike Falcon Fusion automates security operations and incident response, leveraging AI for threat analysis. Its low- to no-code architecture simplifies the creation and automation of security workflows.
18. RapidMiner (https://rapidminer.com/)
RapidMiner is a comprehensive data science platform that allows for quick data mining and machine learning model creation. Its no/low code approach makes AI accessible to non-technical users.
These No-Code AI tools are revolutionizing how AI is integrated into various industries, democratizing access to AI capabilities and enabling individuals and businesses to leverage the power of machine learning without the need for extensive coding expertise. Explore these tools to find the one that best suits your AI needs and start unlocking the potential of artificial intelligence today.
Article by: Werner Koegelenberg for Tappstr
