In the era of digitization, user-friendly machine learning that does not require data science knowledge can be an invaluable asset for any organization. AutoML comes with the advantage of ease of use, which makes it possible for companies to leverage the use of AI to automate processes and improve efficiency. Companies can take advantage of AutoML to build and deploy ML models without having to invest in data science professionals or lengthy manual processes.

From gathering data and deploying machine learning models, to fraud detection and personalized marketing, the range of applications of an AutoML SaaS platform in boosting productivity and efficiency is quite wide. If you have considered whether an AutoML SaaS platform will add value to your company, here are some of the key reasons to invest in this type of auto AI software.

Why Your Company Needs an AutoML Platform It Bridges Skill Gaps

Efficient systems that enable businesses to compete can help in times when skilled professionals are costly or scarce, as is the case with data scientists. With increasing market demand for data scientists, sourcing for professionals can be difficult. However, when organizations make use of an AutoML SaaS platform, the automated processes make it easier to build and deploy ML models regardless of skill level.

AutoML offers a user-friendly interface that simplifies the machine-learning pipeline enabling your employees to utilize the system even without high-level skills in data science.

Reduced Errors in Applying Algorithms

Machine learning models are highly dependent on data, and the accuracy of this data is key in ensuring better models and systems. By automating processes, auto AI software reduces the risk of inaccuracies and errors that may hinder efficiency and negatively impact the return on investment on ML projects.

Automating the cleaning, sourcing, and processing of data significantly minimizes the risk of human error, resulting in better ML models. AI tools are also more efficient at evaluating ML models and they eliminate the need for manual testing, which can be time-consuming and prone to errors.

Allows you to Scale Machine Learning in Your Company

Machine learning models can be useful in a wide range of operations including analyzing customer trends, evaluating data for decision-making, and more. The use of AutoML enables you to scale the use of AI in your organization by making it easy to build and deploy ML to various departments. With the increased use of AI, problem solving and decision-making can be done efficiently resulting in better productivity.

When to use AutoML Structured data

AutoML is ideal for machine learning projects that make use of structured data. When features are organized into rows and columns, AutoML tools can easily encode and normalize variables to engineer features for machine learning algorithms.

Small to medium datasets

On average, datasets with up to 50 columns and up to 100,000 rows can be compatible with AutoML tools. Larger datasets may require more time which can make the process inefficient.

Examples of tasks that can be automated with AutoML include:

·         Profiling data, including visualizing and calculating descriptive statistics

·         Cleansing and pre-processing data

·         Feature engineering

·         ML model evaluation