In my previous post, I have discussed the three key components to build an end-to-end MLOps solution, which are data and feature engineering pipelines, ML model training, and retraining pipeline ML model serving pipelines. You can find the article here: Learn the core of MLOPS -- Building ML Pipelines. At the end of my last post, I briefly talked about the fact that the complexities of MLOps solutions can vary significantly from one to another, depending on the nature of the ML project, and more importantly, variations of the underlying infrastructure required. Therefore in today's post, I will explain how the different levels of Infrastructure required, determine the complexities of MLOps solutions, as well as categorize MLOPS solutions into different levels. More importantly, in my view, categorizing MLOps into different levels makes it easier for organizations of any size to adopt MLOps.

A technique for creating policies, norms, and best practices for machine learning models is known as "machine learning operations" or "MLOps." MLOps aims to guarantee the whole lifecycle of ML development -- from conception to deployment -- is meticulously documented and managed for the best results instead of investing a lot of time and resources in it without a strategy. MLOps aims to codify best practices to improve the quality and security of ML models while making machine learning development more scalable for ML operators and developers. MLOps provides developers, data scientists, and operations teams with a framework for cooperating and, as a result, producing the most potent ML models. Some refer to MLOps as "DevOps for machine learning" since it successfully applies DevOps methods to a more specialized field of technological development.

AIOps and MLOps are both essential components of an AI-powered business. Many companies have used these terms interchangeably in recent years, but there is a difference between them. Understanding that difference can help you understand what role AI will play in your organization and how it will change your business practices. Artificial intelligence for IT operations, also known as AIOps, is a category of tools and strategies that allows organizations to take advantage of big data and machine learning. AIOps uses artificial intelligence to automate and optimize tasks in enterprise IT infrastructure.

Machine learning is going to be huge. It's already so powerful that it has a use case for pretty much every industry that exists. Just think about how it makes fluid automation a reality and cuts workloads in half. How it accelerates medical research and insights to save more lives. And how capable it is of predictive maintenance in areas like the energy sector, where disasters and major financial losses can be avoided. But for ML to work, it requires a unique approach.

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).

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.

A few years ago, it was extremely uncommon to retrain a machine learning model with new observations systematically. This was mostly because the model retraining tasks were laborious and cumbersome, but machine learning has come a long way in a short time. Things have changed with the adoption of more sophisticated MLOps solutions. Now, the common practice of retaining a machine learning model is somewhat reversed. Models are being trained more often and after very short intervals.