Data scientists and machine learning (ML) engineers can bank on MLOps to streamline the ML lifecycle by monitoring, managing and deploying highly efficient machine learning models.
One of the most prominent subsets of AI, machine learning prevails as an important part of most software developments in recent times. Machine learning enables systems and software to adapt to the data and learn through those data patterns. However, machine learning is a broad term and can be characterized as a multidisciplinary field, which introduces the specialization of Machine Learning Operations or MLOps. MLOps enables the monitoring and deployment of machine learning models. Due to this, data scientists and ML engineers are keen on using MLOps, which is gaining accelerated popularity. This pervasion of MLOps technology is so much so that its market worth is predicted to hit a record high of 6161.20 million by 2028.
In simple terms, MLOps is an integration of ML systems development and ML systems deployment that streamlines the delivery of ML models. It combines machine learning, DevOps and data engineering to deploy and maintain ML systems in production efficiency. Data scientists and operations engineers use MLOps practices to execute collaboration and communication between them to increase quality and simplify management processes. When MLOps is introduced in deployment, deep learning and ML models enjoy the automation of deployment in large-scale production environments. MLOps is leveraged in most phases of the ML lifecycle, including model creation, data gathering, CI/CD, production, deployment, diagnostics and business metrics. With large enterprises and tech giants like Amazon, Google, Microsoft and IBM relying on MLOps, many other market leaders and lesser-known vendors are beginning to invest in this technology.
MLOps is similar to DevOps, the practice that aims to shorten the lifecycle of systems development by uniting software developers (Dev) and IT operations teams (Ops). MLOps entail the collaboration between data scientists and ML engineers to share the same aims. Data scientists design data sets and proceed toward analysis through the creation of AI models, whereas ML engineers use automated processes to run the datasets through models.
MLOps automates as many processes as possible while also eliminating waste and delivering deeper insights using machine learning. Data scientists and the ML operations teams are presented with tremendous data collected from various sources and software that may not align with the business interest. In such cases, MLOps provides a proper systemization so data scientists can focus on relevant, scalable data catering to business needs.
MLOps enable the management and maintenance of the entire ML lifecycle, testing the models, their updates and more without hindering the business applications.
Using various modeling frameworks, languages and tools can complicate the process for data scientists. Instead, the IT operations team leverages MLOps to deploy models from various languages and frameworks promptly.
MLOps monitor the process to provide model-centric metrics and statistics, detection of data drift and more. It comes with its own compliance that offers access control, audit trials, traceability and regulatory compliance.
MLOps are providing data scientists and data engineers with improved efficiency in business applications and keeping the doors open to exponential progress in the field.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.