ModelOps is a holistic strategy to move models through the analytics life cycle quickly and iteratively so they may be deployed faster and generate desired business value, whereas, MLOps is a set of approaches for delivering and maintaining machine learning models in production in a consistent and timely manner. ModelOps is essentially a superset of MLOps with enterprise features. Data science teams benefit from MLOps technologies, but there's still a gap between the teams designing and using AI and IT executives responsible for overseeing it. So, ModelOps comes into play, justifying its potential to be so game-changing. ML only provides value once models reach production.

The rise of artificial intelligence has become omnipresent in recent years, state-of-the-art models are open-sourced on a daily basis and companies are fighting for the best data scientists and machine learning engineers, all with one goal in mind: creating tremendous value by leveraging the power of AI. Sounds great, but reality is harsh as generally only a small percentage of models make it to production and stay there. In this blog post, we'll explain how companies can unlock the business value of AI by adopting MLOps practices. Based on years of experience in applying machine learning at ML6, we share a set of best practices that have proven to work for us when it comes to MLOps. When it comes to a definition for MLOps, we believe Google's definition is spot on: "MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops)."

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). However, for most organizations embarking on this transformational journey, the results remain to be seen. And for those who are already underway, scaling their results across their organizations is completely uncharted waters. Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.

Many organisations are dipping their toes into machine learning and artificial intelligence (AI). Some are already reaping some of the rewards of artificial intelligence through increased productivity and revenue. However, for most organisations embarking on this transformational journey, the results are yet to be seen and for those who are already underway, scaling their results appears as completely uncharted waters. According to a survey by NewVantage Partners, only 15% of leading enterprises have deployed AI capabilities into production at any scale. Most of these leading organisations have significant AI investments, but their path to tangible business benefits is challenging, to say the least. There are a number of reasons for this that we find to be reoccurring practically everywhere.