Top 17+ Low-Code AI Model Development In 2024

Artificial intelligence is changing many aspects of our existence, but creating AI models has often been complex and requires extensive coding skills. Now, low-code platforms are making it much easier.

Low-code AI model development lets you build AI models with simple, easy-to-use tools without needing to know much coding. This makes it faster and simpler to create AI projects, even if you’re not a tech expert.

In this article, we’ll explain how low-code platforms work, why they’re useful for AI, and how you can use them to create your own AI models. We’ll cover the basics, show you real-life examples, and give you some tips to get started.

What Is Low-Code AI Model Development?

Low-code AI model development means creating AI models using simple tools that don’t require much coding. These tools make it easy for people to build and use AI models without needing to know a lot about programming.

How It Works

1. Simple Tools: Instead of writing complicated code, you use drag-and-drop features to build your model by moving and arranging visual elements.
2. Ready-Made Models: The platform provides pre-made AI models and templates for tasks like analyzing images or making predictions.
3. Data Handling: You can easily connect your data to the platform and use its tools to clean and prepare your data, which is important for accurate results.
4. Training and Testing: The platform helps you train your model and check its performance without requiring a lot of extra work.
5. Deployment: Using your model is straightforward. The platform makes it easy to add it to your applications with minimal extra coding.

Benefits of Low-Code AI Model Development

• Easy to Use: Allows people with little tech experience to create and use AI models.
• Fast Results: Speeds up the process of building and using models so you can make changes quickly.
• Cost-Effective: Reduces the need for extensive coding and specialized skills, saving you money.
• Flexible: This makes it easy to update and change your models as needed.

In short, low-code AI model development simplifies the creation and use of AI models by reducing the need for coding skills. This allows more people to use AI without needing to be tech experts.

Top 17+ Low-Code AI Model Development In 2024

Low-code AI platforms make it easier to build and use AI without needing to be an expert in coding. Here’s a look at some of the best platforms you can use in 2024:

1. DataRobot
   • What It Does: Provides a simple drag-and-drop interface to create AI models.
   • Where to Use It: Healthcare diagnostics, fraud detection, predicting customer churn.
   • Why It’s Great: Offers industry-specific templates, connects easily with your data, and supports quick deployment and monitoring.
2. H2O.ai
   • What It Does: A powerful tool for creating AI models with minimal coding.
   • Where to Use It: Financial forecasting, retail sales predictions, analyzing customer sentiment.
   • Why It’s Great: Features a wide range of algorithms, is scalable, and works well with cloud services like AWS and Azure. Read More about Azure
3. Microsoft Azure Machine Learning
   • What It Does: Combines low-code and no-code tools for building AI models.
   • Where to Use It: Business analytics, inventory management, predictive maintenance.
   • Why It’s GrIt has: It has easy drag-and-drop tools, integrates smoothly with Microsoft products, and offers strong security and scalability.
4. Google Cloud AutoML
   • What It Does: Provides tools for creating custom AI models with ease.
   • Where to Use It: Image recognition, text analysis, predicting customer behavior.
   • Why It’s Great: Quick deployment, strong analytics, and works well with other Google Cloud services.
5. IBM Watson Studio
   • What It Does: A collaborative platform with visual tools for developing AI models.
   • Where to Use It: Healthcare data analysis, financial risk assessment, market research.
   • Why It’s Great: Supports various data sources, offers strong governance features, and integrates with IBM Cloud.
6. KNIME Analytics Platform
   • What It Does: Uses a visual workflow designer for data science and machine learning.
   • Where to Use It: Data preprocessing, building model prototypes, predictive analytics.
   • Why It’s Great: Open-source, supports many plugins and allows for custom coding in Python, R, and Java.
7. C3 AI Suite
   • What It Does: Designed for large-scale AI and data analytics.
   • Where to Use It: Supply chain optimization, predicting maintenance needs, managing energy resources.
   • Why It’s Great: Handles big data well, integrates with enterprise systems, and includes pre-built industry models.
8. Akkio
   • What It Does: Simplifies AI model creation with a drag-and-drop interface.
   • Where to Use It: Scoring leads, customer segmentation, sales forecasting.
   • Why It’s Great: Integrates with Salesforce and Slack, user-friendly for non-techies, and offers fast deployment.
9. RapidMiner
   • What It Does: Provides a visual workflow designer for machine learning tasks.
   • Where to Use It: Academic research, business intelligence, customer analytics.
   • Why It’s Great: Offers over 1,500 algorithms and functions, customizable workflows, and a supportive community.
10. Appen AI
    • What It Does: Combines AI tools with human expertise for data annotation and training.
    • Where to Use It: Natural language processing, computer vision, speech recognition.
    • Why It’s Great: Provides scalable data services, access to many annotators, and pre-built models for quick use.
11. Lobe.ai
    • What It Does: A Microsoft platform for creating deep learning models with visual tools.
    • Where to Use It: Image classification, object detection, text analysis.
    • Why It’s Great: No coding required, integrates well with Microsoft products, and makes it easy to get started with AI.
12. Clarifai
    • What It Does: Specializes in visual recognition with tools for easy model building.
    • Where to Use It: Facial recognition, content moderation, object detection.
    • Why It’s Great: Excellent for image and video analysis, has good API support, and offers flexible deployment options.
13. Teachable Machine by Google
    • What It Does: A web tool for quickly training AI models with a simple interface.
    • Where to Use It: Rapid prototypes, educational projects, creative experiments.
    • Why It’s Great: Very user-friendly, no setup needed, and great for trying out AI ideas.
14. MonkeyLearn
    • What It Does: Focuses on text analysis with tools for natural language processing.
    • Where to Use It: Analyzing customer feedback, sentiment analysis, and topic classification.
    • Why It’s Great: Integrates with Zapier, has pre-built templates, and supports custom training without coding.
15. Obviously.ai
    • What It Does: Allows businesses uto create AI models with minimal effort quicklyffort.
    • Where to Use It: Sales forecasting, customer churn prediction, marketing data analysis.
    • Why It’s Great: Fast model building, uses natural language queries and works well with common data tools.
16. BigML
    • What It Does: A cloud platform for predictive modeling with an easy-to-use interface.
    • Where to Use It: Fraud detection, market trend forecasting, supply chain optimization.
    • Why It’s Great: Simple interface, a variety of machine learning options, and helpful resources for beginners.
17. Aible
    • What It Does: Provides actionable business insights through a user-friendly platform.
    • Where to Use It: Demand forecasting, sales optimization, resource management.
    • Why It’s Great: Quick setup, aligns AI models with business goals, and focuses on results that impact the bottom line.
18. RapidAPI for Machine Learning
    • What It Does: A marketplace for easy access to machine learning models via APIs.
    • Where to Use It: Adding AI features to applications, testing mode and ls, fast deployment.
    • Why It’s Great: Extensive library of pre-built models, supports multiple programming languages, and flexible pricing.

These platforms offer diverse tools and capabilities to fit different needs, whether you’re experimenting with AI or seeking to implement complex solutions. Exploring these options can help you find the right fit for your AI projects in 2024.

Key Components of Low-Code AI Model Development

Low-code AI platforms make it easier for anyone, even those without coding experience, to create and use AI models. They provide a simple and easy-to-use setup that speeds up development and makes AI more accessible. Here are the main parts that make these platforms work effectively:

1. Drag-and-Drop Interface

These platforms usually have a drag-and-drop interface, which means you can build AI models by dragging different building blocks onto a screen. It’s like using a digital toolkit where you pick and place different parts to create a workflow or application. This makes the process easy and reduces mistakes since there’s no need to write code manually.

2. Readymade templates and Components

Low-code AI platforms offer various pre-built templates and components for common tasks like recognizing images, analyzing text, or making predictions. You can start with these templates and adjust them to suit your needs, which saves a lot of time. Some platforms also come with pre-trained models that you can fine-tune quickly to get accurate results.

3. Easy Data Integration

Getting data is a crucial part of building AI models. Low-code platforms make it simple to link to several data sources, such as databases, cloud storage, or IoT devices.. This helps users easily gather, clean, and prepare data, ensuring they work with high-quality information for training their models.

4. Automated Machine Learning (AutoML) Tools

Many low-code AI platforms come with AutoML tools that handle complex parts of the AI model-building process, like selecting the best model, adjusting settings, and preparing data. These tools make it easier and quicker for anyone to build effective AI models, even without deep technical knowledge.

5. Tools for Scaling and Deployment

Low-code AI platforms include tools that make it simple to deploy AI models across different environments, such as cloud services, company servers, or devices at the edge, like smartphones. These tools often have one-click deployment options, allowing models to scale up quickly. They also provide ways to monitor model performance and ensure accuracy over time.

6. Collaboration and Security Features

To support teamwork, these platforms have features that allow multiple users to work together, share feedback, and control access to projects. They also provide security controls to protect sensitive data and ensure compliance with privacy regulations.

7. Compatibility with Existing Systems

These platforms are designed to integrate smoothly using existing business technologies, such as customer relationship management (CRM) software, enterprise resource planning (ERP) tools, and analytics platforms. This compatibility ensures that AI models can be easily added to current workflows, maximizing the benefits of AI without needing to change existing systems.

These components help organizations start AI projects faster, cut costs, and make AI tools available to more people, boosting innovation and smarter decision-making across the company.

Best Practices for Getting Started with Low-Code AI Model Development

Low-code AI platforms are great for making AI models easier, but following these steps will help you get the best results:

1. Set Clear Goals

• What to Do: Decide what you want to achieve with AI. Are you solving a specific problem or improving something?
• Why It Matters: Clear goals help you choose the right tools and focus on what matters, making sure your AI projects are useful and effective.

2. Understand Your Data

• What to Do: Check your data to make sure it’s clean and relevant. Ensure you have enough data for your project.
• Why It Matters: Good data leads to better results. More data can lead to problems with your AI models.

3. Choose the Right Platform

• What to Do: Pick a low-code AI platform that suits your needs. Look for ease of use, integration with other tools, and features.
• Why It Matters: Different platforms offer different things. The right one will help you get the job done more effectively.

4. Start Simple

• What to Do: Begin with easy models to learn how the platform works. As you get more comfortable, you can try more complex models.
• Why It Matters: Starting with simple models helps you learn the basics without getting overwhelmed, preparing you for more advanced projects.

5. Use Templates

• What to Do: Use the pre-built templates and models available on the platform to speed up your work.
• Why It Matters: Templates save time and provide a good starting point. They also show you how to approach different tasks.

6. Test and Improve

• What to Do: Regularly test your models and make improvements based on their performance.
• Why It Matters: Testing and improving help ensure your models work well and let you fix any issues early on.

7. Join the Community

• What to Do: Get involved in forums or groups related to your platform. Share what you’ve learned and learn from others.
• Why It Matters: Being part of the community gives you useful tips and support. It’s a great way to solve problems and learn more.

8. Monitor Performance

• What to Do: After launching your models, monitor their performance using feedback and data.
• Why It Matters: Ongoing monitoring helps ensure the effectiveness of your models and allows you to make changes if necessary.

9. Respect Data Privacy

• What to Do: Follow data privacy rules and make sure your models comply with laws and standards.
• Why It Matters: Protecting data and staying compliant is crucial for avoiding legal issues and building trust.

10. Train Your Team

• What to Do: Provide training for anyone who will use the AI models. Make sure they understand how to use the platform and read the results.
• Why It Matters: Training ensures everyone can use the platform effectively, leading to better results and fewer mistakes.

By following these simple steps, you’ll be able to make the most of low-code AI platforms and easily achieve your project goals.

Final Words

Looking into the Top 17+ Low-Code AI Model Development In 2024 can really boost your projects by making advanced AI tools easier to use. To get started, clearly define what you want to achieve and understand your data. Pick a platform that fits your needs, start with simple models, and use templates to save time. 

Keep testing and improving your models, join community discussions for extra help, and make sure you follow data privacy rules. By following these steps, you’ll be able to make the most of low-code AI and reach your goals effectively in 2024.