Catalyst QuickML

Introduction

QuickML is a fully no-code ML pipeline builder service in the Catalyst development platform for creating machine-learning pipelines with end-to-end machine learning solutions. QuickML helps you execute sequences of stages including wide ranges of data processing and machine learning sub tasks needed to efficiently build, test, deploy, and monitor effective ML models for different business requirements.

Catalyst, as a whole, serves as a complete end-to-end development platform that provides you with services, components, and tools to build, code, test, deploy, and monitor web applications, mobile applications, and microservices. Catalyst offers a host of backend services, FaaS components, DevOps tools, powerful AI and ML microservices, and more. The setup and management of the underlying server resources utilized by these applications are handled entirely by Catalyst, thereby completely eliminating the infrastructure maintenance, and costs from your end. Catalyst services can either be used independently, or be integrated with one another to build highly-functional, robust applications and micro services.

You can access QuickML from the highly-integrated Catalyst console, from where you can create a project and get started. You can set up your Catalyst project and tailor it to your requirements, and access all the other services and components of Catalyst from the console as well.

What is a Machine Learning Pipeline?

A machine learning pipeline is a series of instructions in the form of sequential steps, where each step is a particular process that is achieved by placing the respective components to develop, deploy, and monitor the machine-learning model. These sequential steps include end-to-end processes like data collection, data validation, preprocessing, model training, analysis, and deployment.

When it comes to machine learning, iterations are the key to building and achieving an effective model with high accuracy. Breaking down complex solutions into smaller components is important, as these are easier and quicker when it comes to iterations. Having smaller components dedicated to individual roles makes it easier to replace them as well.

The monolithic approach is not practically scalable in the long run. This is because when tweaking a particular area or context, the whole flow needs to be re-processed, as there is no segmentation available. Going through each step of a machine-learning pipeline manually is costly, time-consuming, and often erroneous.

Automated machine-learning pipelines will help data scientists to focus on new models without having to manually keep previously developed models upto date by preprocessing data or running deployment scripts.

In the early days, machine-learning models served as the primary product in the market. However, as we now consider machine learning to be service, the major product in demand is actually the workflows defined by connecting the components to achieve desired machine-learning solutions (i.e., a machine-learning pipeline) is the product in demand.