Why QuickML?

In machine-learning solutions, there is a gap between the data scientists working on the different stages of machine learning, and the developers working on the models. QuickML focuses on giving data scientists an end-to-end control over the whole pipeline, making iterations much easier for improving accuracy.

Creating a machine-learning model to get better predictions in order to help improve the businesses is an iterative process which involves dealing with many development and production difficulties, such as:

• Data handling
• Resources handling
• Model management strategies
• Model Monitoring
• And other operational challenges

QuickML promises to be the service to tackle all these difficulties, and take advantage of the data that is available over the cloud, with zero operational overheads. QuickML helps in creating these data and machine-learning-based systems effectively by providing a no-code platform for the developers, data analysts, data scientists, and others to get the most out of the data with minimal effort. It serves as a platform for developing, maintaining, and producing machine learning models.

We have categorized the activities of QuickML into two main modules:

• QuickML Machine Learning Pipelines
• QuickML Data Pipelines

QuickML Machine Learning Pipelines

Machine-learning pipelines are the end-to-end execution of workflows for data and machine learning tasks, designed to orchestrate fully trained and accurate machine learning models to help provide predictive intelligence in a wide range of business requirements.

QuickML has a unique no-code pipeline builder platform in which machine learning pipelines are designed and executed. It focuses on easing up the machine learning operations in the development of machine-learning models that are ready for production.

Users can build a machine learning model or improve the quality of the data by using this pipeline builder interface. The interface contains simple drag-and-drop UI for constructing the stages of a pipeline. Each of these stages can be further configured with output preview in the platform, based on the requirements.

QuickML specializes in providing a good variety of basic ML algorithms and Artificial Intelligence features integrated as atomic Stages in the pipeline building flow. A pipeline execution flow can contain various combinations of Data operations, Machine-learning tasks and algorithms that can generate different feature focuses and resilient machine learning models with business data.

QuickML Data Pipelines

Data pipelines are part of the Machine-Learning Model Lifecycle, which often requires various manipulations that have to be applied to the data before passing to any machine-learning training process.

QuickML data pipelines can be treated as independent data pipelines as well part of ML pipelines based on requirements.

Data can be either imported from other zoho services or from external services like AWS S3/GCloud or from the local file system. Once the data is imported into the system, users will have the basic details about the data, such as the data quality.