No Code AI Platforms: Build & Deploy Models (2024 Review)

Artificial intelligence often feels like it’s locked behind a wall of complex code, accessible only to data scientists and machine learning engineers. But what if you could build, train, and deploy AI models without writing a single line of code? That’s the promise of no-code AI platforms.

These platforms empower business users, analysts, and even developers with limited AI experience to leverage the power of machine learning. They provide user-friendly interfaces, pre-built algorithms, and automated workflows, enabling you to solve business problems, automate tasks, and gain valuable insights from your data. This review dives deep into the leading no-code AI platforms available in 2024, exploring their features, pricing, and suitability for different use cases.

This review is for business users and product managers who need to understand which tool is best for specific use cases. We delve past marketing hype to provide specific insights.

What to look for in No-Code AI Platform

Before diving into specific platforms, let’s define the key features to look for

• User-Friendly Interface: Intuitive drag-and-drop interfaces that simplify the model building process.
• Data Integration: Seamless connections to various data sources, including databases, spreadsheets, cloud storage, and APIs.
• Automated Machine Learning (AutoML): Automated feature engineering, algorithm selection, and hyperparameter tuning.
• Pre-built Models: A library of pre-trained models for common tasks, such as image recognition, natural language processing, and time series forecasting.
• Deployment Options: Flexible deployment options, including cloud, on-premise, and edge devices.
• Scalability: Ability to handle large datasets and increasing user demands.
• Collaboration Tools: Features that enable teams to collaborate on model building and deployment.
• Explainability: Tools that help users understand why a model makes a particular prediction.
• Security: Robust security measures to protect data and models.
• Support and Documentation: Comprehensive documentation and responsive support to assist users.

Review: Leading No-Code AI Platforms

Now, let’s examine some of the top no-code AI platforms in the market:

Obviously.AI

Obviously.AI is designed for business users to make predictions easily in almost any domain. It especially caters to those with limited or no coding/AI experience. It’s a compelling option for those seeking a quick and accessible entry point into AI-powered data analysis and forecasting.

Key Features of Obviously.AI

• User-Friendly Interface: The platform boasts a very clean, drag-and-drop interface, making it easy to upload data, select target variables, and generate predictions. It guides users through each step of the process.
• Automated Machine Learning (AutoML): Obviously.AI automates the entire machine learning pipeline, from data preprocessing to model selection. The platform automatically identifies the best algorithm for your dataset, eliminating the need for manual tuning.
• Integration with Data Sources: It can connect to various data sources like CSV files, Google Sheets, and other popular platforms, making data ingestion a seamless process.
• Predictive Storytelling: This platform generates interactive, easy-to-understand reports with visualizations and insights that help users communicate findings to stakeholders.
• API Access: Provides API integration for embedding predictions into other applications and workflows. This can be useful to include predictions into existing workflows where custom dashboards are required.

Pricing Breakdown

Obviously.AI offers several pricing tiers to accommodate different user needs:

• Free Trial: A free trial plan allows users to assess the platform’s capabilities with limited access to features.
• Starter Plan: Typically around $99/month, this plan is designed for individual users and small teams. It usually includes a set number of predictions and reports per month.
• Pro Plan: Priced around $499/month, the Pro Plan is tailored for more advanced users needing higher prediction volumes and more comprehensive features like API access and custom model training.
• Enterprise Plan: For large organizations, custom pricing is available, offering bespoke solutions with unlimited predictions, dedicated support, and priority feature requests.

Pros of Obviously.AI

• Easy to learn and use, even for non-technical users.
• Automated model selection and training reduce complexity.
• Integrated predictive storytelling helps communicate insights clearly.
• Fast deployment of machine learning models.

Cons of Obviously.AI

• Limited control over model customization.
• Less suited for complex machine learning tasks requiring advanced configuration.
• The reliance on automated processes may not be ideal for data scientists needing granular control.

CreateML (Apple)

CreateML, developed by Apple, is a no-code AI platform tightly integrated within the Apple ecosystem, primarily aimed at iOS, macOS, watchOS, and tvOS app development. It allows developers, even those with limited machine learning experience, to build and train models directly on their Macs using a graphical interface. CreateML particularly shines in tasks related to computer vision, natural language processing, and sound analysis.

Key Features of CreateML

• Drag-and-Drop Interface: Provides a simple, user-friendly environment to load, prepare, and label data without writing code.
• Integration with Core ML: Seamlessly integrates with Core ML, Apple’s machine learning framework, making it easy to deploy models to Apple devices.
• Real-Time Training Preview: Offers real-time feedback during model training, allowing developers to monitor progress and adjust parameters.
• Supports Various Data Types: Handles image, text, tabular, and audio data efficiently, catering to a wide range of applications.
• On-Device Training: Capable of training models directly on Apple devices, leveraging the device’s processing power and ensuring data privacy.

Pricing Breakdown

CreateML is offered as a *free* tool as part of Xcode, Apple’s integrated development environment (IDE). No additional costs are involved, making it particularly attractive for developers already in the Apple ecosystem.

Pros of CreateML

• Free to use for Apple developers.
• Tight integration with Apple’s Core ML framework.
• User-friendly interface with real-time feedback.
• Supports on-device training for enhanced privacy.

Cons of CreateML

• Limited to Apple platforms, restricting deployment outside of the Apple ecosystem.
• Less comprehensive feature set compared to commercial No-Code / AutoML platforms.
• May lack advanced customization options for highly complex AI tasks.

Microsoft Azure Machine Learning Designer

Microsoft Azure Machine Learning Designer is a drag-and-drop interface within the Azure Machine Learning Studio, aimed at users with varying levels of machine learning expertise. Part of the broader Azure AI ecosystem, it provides a visual environment for constructing machine learning pipelines without coding. Azure Machine Learning Designer caters to a wide range of applications with its comprehensive tools and integration capabilities.

Key Features of Azure Machine Learning Designer

• Drag-and-Drop Interface: An intuitive interface simplifies the creation of machine learning workflows. Users can quickly assemble pipelines by dragging and connecting modules.
• Pre-Built Modules: Offers a library of pre-built modules for various tasks, including data preprocessing, feature engineering, model training, and evaluation.
• Custom Code Integration: Allows users to integrate custom code (Python and R) into their pipelines for advanced customization.
• Automated Machine Learning (AutoML): Provides automated features that handle algorithm selection and hyperparameter tuning to optimize model performance.
• Integration with Azure Services: Seamlessly integrates with other Azure services such as Azure Data Lake Storage, Azure Synapse Analytics, and Azure Kubernetes Service (AKS).
• Collaboration Tools: Supports team collaboration with version control, shared workspaces, and role-based access control.

Pricing Breakdown

Pricing for Azure Machine Learning Designer is based on consumption. Costs depend on the compute resources used for training, deploying, and managing models. Key pricing components include:

• Compute Instance: Charges for the compute resources used to run the designer interface and execute pipelines. The cost varies based on instance size and region.
• Model Training: Payment for the compute time used during model training, which depends on the CPU, memory, and training duration.
• Model Deployment: Charges for hosting deployed models as web services, based on the chosen compute type (e.g., AKS, Azure Container Instances).
• Data Storage: Costs for storing data in Azure Data Lake Storage or other Azure storage services.

Microsoft offers a free tier with limited resources for experimenting with Azure Machine Learning services, but production deployments will incur costs. Users should leverage the Azure pricing calculator to estimate costs based on their specific needs.

Pros of Azure Machine Learning Designer

• User-friendly drag-and-drop interface.
• Extensive library of pre-built modules.
• Integration with custom code for advanced users.
• Seamless integration with other Azure services.
• Support for collaborative development.

Cons of Azure Machine Learning Designer

• Pricing can be complex and depend on resource consumption.
• Steeper learning curve for users unfamiliar with the Azure ecosystem.
• Reliance on Azure services may limit flexibility for organizations using other cloud providers.

Google Cloud AutoML

Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited machine learning expertise to train high-quality models tailored to their specific needs. As part of the Google Cloud Platform, AutoML provides a user-friendly interface and robust automated capabilities, reducing the complexity of building and deploying AI solutions.

Key Features of Google Cloud AutoML

• Automated Machine Learning (AutoML): Automates key steps in the machine learning pipeline, including data preprocessing, feature engineering, and model selection.
• Pre-trained Models: Offers pre-trained models for common tasks like image recognition (AutoML Vision), natural language processing (AutoML Natural Language), and tabular data analysis (AutoML Tables).
• Custom Model Training: Allows users to train custom models with their own datasets using the AutoML framework.
• Integration with Google Cloud Services: Seamlessly integrates with other Google Cloud services, such as BigQuery, Cloud Storage, and Cloud Functions.
• Deployment Options: Provides flexible deployment options, including cloud-based APIs, edge devices, and integration with mobile applications.
• Model Evaluation: Offers comprehensive tools for evaluating model performance, including accuracy metrics, confusion matrices, and feature importance analysis.

Pricing Breakdown

Google Cloud AutoML follows a pay-as-you-go pricing model, with costs varying based on usage. Key pricing components include:

• Training Costs: Charges for the compute time used during model training, depending on the chosen service (AutoML Vision, AutoML Natural Language, AutoML Tables) and the size of the dataset. Each service has different pricing structures.
• Prediction Costs: Fees based on the number of prediction requests made to the deployed models.
• Data Storage: Costs for storing data in Google Cloud Storage.
• Model Management: Charges for managing and maintaining deployed models.

Google Cloud offers a free tier with limited resources, allowing users to experiment with AutoML services. Production deployments will incur costs, and users should consult the Google Cloud pricing calculator for detailed estimates.

Pros of Google Cloud AutoML

• Automates complex machine learning tasks.
• Offers pre-trained models for common applications.
• Seamless integration with other Google Cloud services.
• Flexible deployment options.
• Scalable infrastructure for handling large datasets.

Cons of Google Cloud AutoML

• Costs can be unpredictable and dependent on usage.
• Requires familiarity with the Google Cloud ecosystem.
• Less control over model architecture and training process compared to custom solutions.

DataRobot

DataRobot is an enterprise AI platform that specializes in automated machine learning (AutoML). It aims to democratize AI by enabling users, regardless of their coding expertise, to build, deploy, and manage machine learning models with ease. DataRobot is known for its comprehensive feature set, robust automation capabilities, and focus on enterprise governance and security.

Key Features of DataRobot

• Automated Machine Learning (AutoML): Automatically explores hundreds of algorithms, preprocessing steps, and feature engineering techniques to identify the best models for a given dataset.
• Visual AI: Enables users to build models using images and video data without coding.
• Time Series AI: Provides specialized tools for forecasting time series data, including automated feature engineering and model selection.
• MLOps: Offers a comprehensive MLOps platform for deploying, monitoring, and managing machine learning models at scale.
• Explainable AI: Provides insights into model behavior, including feature importance, prediction explanations, and fairness metrics.
• Data Integration: Supports connections to a wide range of data sources, including databases, cloud storage, and streaming platforms.
• Collaboration Tools: Enables teams to collaborate on model building and deployment with version control, access control, and shared workspaces.

Pricing Breakdown

DataRobot’s pricing is custom and enterprise-focused. Contacting their sales team directly is required to tailor a plan suitable for the organization’s requirements.

• Custom Enterprise Agreement: DataRobot offers custom plans based on the specific needs of the organization, including the number of users, the volume of data, and the required features.
• Value-Based Pricing: The pricing model is often based on the value that DataRobot brings to the organization, considering factors such as improved decision-making, increased efficiency, and new revenue opportunities.
• Support and Training: Custom pricing often includes comprehensive support, training, and consulting services to ensure successful deployment and adoption.

Pros of DataRobot

• Comprehensive AutoML capabilities.
• Specialized tools for visual AI and time series forecasting.
• Robust MLOps platform for model management.
• Explainable AI features for understanding model behavior.
• Enterprise-grade security and governance.

Cons of DataRobot

• Custom pricing can be expensive for small organizations.
• Can be overwhelming for users with limited machine learning experience.
• Requires significant investment in training and support to realize full potential.

When and who should use No-Code AI tools: a verdict

No-code AI platforms are most suited for the following:

• Business Analysts: Empowering analysts to build predictive models and uncover insights without relying on data scientists.
• Small to Medium-Sized Businesses: Providing access to AI capabilities without the overhead of hiring specialized AI engineers.
• Rapid Prototyping: Enabling quick experimentation and validation of AI solutions before investing in full-scale development.
• Educational Purposes: Facilitating the learning and understanding of AI concepts through hands-on experience.

However, no-code AI platforms may *not* be ideal for:

• Complex, Highly Customized AI Solutions: Where fine-grained control over model architecture and training is required.
• Large-Scale Deployments Requiring Extreme Performance Optimization: Specific hardware or model customization are beneficial
• Organizations with Existing Teams of Data Scientists: Since these possess the expertise to build custom models using code.

Alternatives to no-code

Several tools outside of the no-code paradigm can accomplish similar tasks. These are generally suited for situations where more control or lower cost is required, often at the expense of usability or development time.

• Low-code AI platforms. Some platforms offer an in-between by combining a visual interface with the ability to also write some Python or R to fully customize the AI model.
• Automated script generation: Some AI tools can build the scripts for you, making it as simple as copying and pasting them into your local IDE. For example, you can use Jasper.ai generate code using natural language prompts. (affiliate link)

Final Verdict

The no-code AI movement is democratizing artificial intelligence, bringing its power to a wider audience. Platforms like Obviously.AI provide the quickest and easiest onramp, while something like Azure’s Machine Learning Designer provides robust control to more advanced and enterprise users. While no-code AI may not replace data scientists for the most complex tasks, it empowers business users and analysts to harness the power of AI to solve real-world problems.

Ready to explore the power of AI for your business? Check out Jasper.ai now. (affiliate link)