Attention, attention, data lovers. Are you tired of spending countless hours writing code to build and deploy machine learning models? Well, say hello to Qlik AutoML! Qlik AutoML automates the process of applying machine learning to real-world problems, making it possible for analytics teams to embrace machine learning without specialized skills. We know it may sound too good to be true but trust us! We are engineers. With the ability to train models to a particular dataset and generate predictions, Qlik AutoML closes the gaps between data and insight, and between insight and action. So, buckle up and get ready to discover how Qlik AutoML is revolutionizing the world of machine learning.
What is Qlik AutoML?
Qlik AutoML is an advanced analytics service offered by Qlik Cloud Data Services. It lets you build and deploy machine learning models with ease, all without having to write a single line of code. That’s right, no coding is required! With Qlik AutoML, you can automate the entire process of applying machine learning to real-world problems, including data preparation, feature extraction, and algorithm selection and optimization – tasks that traditionally require a data scientist.
This service is now fully integrated into Qlik Cloud and is available to all enterprise customers, allowing analytics teams to embrace machine learning without requiring specialized skills. The ability to train models across a range of structured, supervised machine learning problems and seamlessly connect to a wide range of data sources makes Qlik AutoML a powerful tool for generating predictions and understanding outcomes. At B EYE, we’re excited to see that Qlik is continuously upgrading its platform abilities, and we can’t wait to see what’s in store next. If you want to see the full power of Qlik AutoML, see the demo provided bellow.
What are the key benefits of Qlik AutoML?
Did we grab your attention yet? If you are not amazed by Qlik AutoML yet, our next lines about its benefits will definitely change your mind. Qlik AutoML has many benefits for its users and businesses, and we’ll highlight the key ones below. Qlik AutoML tools and processes are highly advantageous for data scientists and machine learning experts. Data scientists can rapidly and effortlessly create baseline models and act on the results, while ML experts can avoid the traditional trial-and-error method and instead concentrate their efforts on customizing complex models.
The following are some of the overarching benefits of automated machine learning that apply to both types of users:
Rapid implementation of machine learning across the organization
AutoML enables non-ML experts to utilize machine learning models and enables ML-experienced developers and data scientists to produce solutions quickly and easily, which are often more straightforward and even outperform manually coded models.
Emphasis on high-impact work
AutoML reduces the time-consuming and repetitive coding required in the machine learning process, including data preprocessing and cleaning, algorithm selection, and optimization and monitoring of model parameters. Furthermore, training computers to identify content can decrease errors and save countless hours of manual curation of tables, text, images, and videos.
Enhancement of business performance
AutoML makes it easier and faster to provide predictive analytics to your analytics team, which can greatly improve business performance. Some examples of its applications include detecting fraud, providing more personalized experiences for consumers, and enhancing inventory management through improved demand forecasting.
How to get the most out of Qlik AutoML?
A great tool gives you great power. That is for sure. But to get the most out of Qlik AutoML, organizations should consider the following best practices:
Ensure that data is of high quality
Clean and well-structured data is essential for accurate machine learning models. Ensure that data is free of duplicates and outliers and that it accurately represents the relationships and patterns you want to analyze.
Start with a well-defined problem
Define the problem you want to solve and the business outcomes you want to achieve before starting a machine learning project. This will help you focus on the most relevant data and avoid wasting time on irrelevant information.
Monitor model performance regularly
Machine learning models need to be updated and retrained over time as new data becomes available. Regular monitoring of model performance will help you identify when it is time to retrain the model and ensure that it continues to provide accurate results.
Use an agile approach
Machine learning is an iterative process, and you may need to make changes to the model and data as you learn more about the problem you are trying to solve. An agile approach will help you quickly respond to new insights and continue to improve the model over time.
In conclusion, we are thrilled to have access to Qlik AutoML and its incredible capabilities to automate the process of applying machine learning to real-world problems. This tool is a game-changer for analytics teams, allowing them to embrace machine learning without specialized skills, and making it possible to generate predictions, understand outcomes, and take action. The benefits of Qlik AutoML are numerous, including rapid implementation of machine learning, high-impact work, and enhancement of business performance. As a long-term partner of Qlik, at B EYE, we are excited to work with Qlik AutoML and can’t wait to see how it will continue to revolutionize the world of machine learning.