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Making Better Decisions with No-Code AutoML

Making informed decisions is essential for businesses and organizations to remain competitive and relevant in today’s data-driven environment. Although machine learning has grown to be an effective tool for making predictions from data, but traditional development of machine learning frequently necessitates a high level of coding knowledge and proficiency. Machine learning is made more accessible to a wider audience with no-code AutoML. 

We will explore No-code AutoML and its potential to transform decision-making in this article.

 No-Code AutoML is a system that enables people and organizations to create machine learning models without requiring a lot of coding or data science knowledge. Building, training, and implementing machine learning models are made easier, and as a result, a wider range of users—including business analysts, subject matter experts, and decision-makers who do not have a background in data science or programming—can use it. 

Here’s a closer look at No-Code AutoML’s main features:

  •  Simplified Machine Learning Workflow
  •  User-Friendly Interfaces
  • Automated Feature Engineering
  •  Model Selection and Hyperparameter Tuning
  • Built-in Data Handling
  • Integration with Business Applications
  • Democratization of Machine Learning
  • Reduced Dependence on Data Scientist

Making Better Decisions with No-Code AutoML

Problem Definition:

Clearly define the problem you want to solve with machine learning. Ensure that the problem statement aligns with your business or research goals.

Data quality

Despite the automation, data quality is still very important. Make sure the data you use is accurate, pertinent, and reflects the issue at hand. Clearly handle outliers and missing values.

Feature Engineering

Take advantage of any feature engineering tools that the no-code AutoML platform offers. Comprehend the domain in order to select or create appropriate features.

Model selection

Identify the advantages and disadvantages of various machine learning models. Model selection explanations are provided by certain no-code AutoML platforms. Investigate several model types if you can to determine which one works best.

Data Splitting

Make sure the data is appropriately divided into training, validation, and test sets. Reliable model evaluation depends critically on the quality of the validation and test sets.


Utilize the platform’s interpretability features to learn how the model generates predictions. This is particularly crucial if you consider explainability and transparency to be essential factors when making decisions.

Bias and Fairness

Be aware of potential biases in your data and models. Some no-code AutoML tools offer features to detect and mitigate bias. Pay attention to fairness and ethics in decision-making.

Validation and Testing

Carefully validate the model on a holdout test dataset to ensure it generalizes well. 

By following these best practices and being mindful of your role in the decision-making process, you’ll be able to effectively utilize no-code AutoML to make better decisions grounded on data-driven insights.

Green AutoML: Driving Sustainability in Machine Learning


Join NIKO No Code AutoML  model building platform today and experience our simplified process. We now provide a free 14-day trial, making it easier than ever to get started.

Simply reach out to us, and we’ll get you all set up.

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NIKO is a streamlined tool for creating AI models quickly and easily, without writing a single line of code. Here is an introduction video about NIKO.


With NIKO you can build, manage and automate machine learning models and predictions.

You can read more detail here.

With NIKO, it’s absolutely straightforward – you merely need to drag and drop it into the system.

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