Novel applications of machine learning have been made possible by the emergence of Low-Code and No-Code AI tools and platforms. These tools enable the creation of web services and customer-facing apps with minimal coding expertise. Noteworthy tools include MakeML for machine-learning models, Obviously AI for accurate predictions, and SuperAnnotate for high-throughput data annotation.
### Top Low/No Code AI Tools (September 2023)
#### Making AI Accessible
Applications that leverage machine learning are being developed thanks to the rise of Low-Code and No-Code AI tools. These tools allow for the creation of web services and customer-facing apps to coordinate sales and marketing efforts better. Minimal coding expertise is all that’s needed to make use of these solutions.
#### Value of Low/No Code AI Tools
1. **MakeML**
– Create machine-learning models for object identification and segmentation without hand-coding.
– It simplifies the process of creating and managing large datasets.
2. **Obviously AI**
– Make accurate predictions in minutes without coding.
– Access state-of-the-art algorithms for tasks like revenue forecasting and targeted advertising.
3. **SuperAnnotate**
– Create AI-powered SuperData with end-to-end tasks such as annotating, managing, and versioning data.
– Scale and automate AI pipeline three to five times faster.
4. **Teachable Machine**
– Teach a computer to recognize and respond without writing any code.
– Create robust machine learning models for integration into various applications.
5. **Apple’s Create ML**
– Train multiple models simultaneously and efficiently on your Mac using Create ML.
6. **PyCaret**
– Automate machine-learning workflows in Python with a low-code platform, allowing more focus on analysis rather than code-writing.
7. **Lobe**
– Teach your apps to recognize various elements without coding.
– A cross-platform and cost-effective solution.
8. **MonkeyLearn**
– Use no-code text analysis studio for detailed data visualization and analysis.
9. **Akkio**
– Easily create predictive models for better decision-making without any coding.
10. **Amazon SageMaker**
– Create, train, and deploy machine learning models with low-code tools.
11. **Data Robot**
– Streamline the entire lifecycle of machine learning model development, deployment, and management with a no-code interface.
12. **Google AutoML**
– Create and release machine learning models without hand-coded solutions.
13. **Nanonets**
– Train models with only a fraction of the data and no prior experience in machine learning.
14. **IBM Watson Studio**
– Create, release, and manage AI models in the cloud with low- to no-code features.
15. **H2O Driverless AI**
– Streamline the machine learning lifecycle without writing code.
#### Conclusion
These low/no code AI tools offer practical, efficient, and user-friendly solutions for various AI-related tasks. They provide an opportunity to integrate AI into various functions without the need for extensive coding knowledge, making AI accessible to a wider audience.
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