This article explains how Machine Learning Operations came to be a discipline inside many companies and things to consider when deciding if your organization is ready to form an MLOps team. Machine learning (ML) is a subset of artificial intelligence in which computer systems autonomously learn a task over time. Based on pattern analyses and inference models, ML algorithms allow a computer system to adapt in real time as it is exposed to data and real-world interactions. For many people, ML was, until recently, considered science fiction. But advances in computational power, frictionless access to scalable cloud resources, and the exponential growth of data have fueled an increase in ML-based applications.
Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. How can teams tame complexity and live up to the expectations placed on them? There is no one size fits all when it comes to implementing an MLOps solution on Amazon Web Services (AWS).
Machine Learning is deemed as one of the key driver for the fourth industrial revolution. With more business firms now welcoming machine learning insights into their fabric of software advancements, to overcome the complex process involved in deploying it, DevOps methods are employed on the machine learning models. This emerging term in professional machine learning applications is called as MLOps. GigaSpaces Technologies, a computer software company, provides leading in-memory computing platforms for real-time insight to action and extreme transactional processing. With GigaSpaces, enterprises can operationalize machine learning and transactional processing to gain real-time insights on their data and act upon them at the moment.
The ability to make fast, data-driven decisions has never been more valuable as businesses grapple with the shift toward hyper-personalisation, driven by rapidly changing customer behaviours and expectations. The pandemic has accelerated the imperative for businesses to invest in Artificial Intelligence (AI) and Machine Learning (ML) so they can replace guesswork with data-powered certainty to reorient strategy and optimize operations for success in an uncertain future. Nevertheless, enterprises often struggle to integrate these technologies at scale and monetize the benefits. Stumbling blocks typically include challenges associated with cost, lack of investment protection, undefined business outcomes, lengthy timeframes from development to deployment, lack of expertise, and the complexities of the regulatory landscape. Gartner predicts that by 2022, at least 50% of ML projects will not be fully deployed into production.