Automated Machine Learning vs Low Code Platforms

Developers should learn AutoML tools when they need to quickly prototype machine learning models without deep expertise in data science, or to streamline repetitive tasks in model development for faster deployment meets developers should learn low code platforms to accelerate prototyping, automate repetitive tasks, and enable collaboration with business stakeholders who lack coding expertise. Here's our take.

Automated Machine Learning

Nice Pick

Developers should learn AutoML tools when they need to quickly prototype machine learning models without deep expertise in data science, or to streamline repetitive tasks in model development for faster deployment

Pros

• It is particularly useful in business contexts where rapid experimentation and scalability are critical, such as automating customer segmentation or predictive maintenance
• Related to: machine-learning, data-preprocessing

Cons

• Specific tradeoffs depend on your use case

Low Code Platforms

Developers should learn low code platforms to accelerate prototyping, automate repetitive tasks, and enable collaboration with business stakeholders who lack coding expertise

Pros

• They are particularly useful for building internal tools, business process applications, and MVPs (Minimum Viable Products) where speed and agility are prioritized over custom code
• Related to: business-process-automation, drag-and-drop-interfaces

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Automated Machine Learning is a tool while Low Code Platforms is a platform. We picked Automated Machine Learning based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Automated Machine Learning wins

Based on overall popularity. Automated Machine Learning is more widely used, but Low Code Platforms excels in its own space.