If you have not heard the term MLOps yet, you can bet you will soon. MLOps or Machine Learning Operations has emerged as the missing link between the functions of Machine Learning, Data Science and Data Engineering. MLOps is the culmination of an operating framework, set of best practices, and architecture principles, that together enable teams to deploy machine learning models in production and maintain them for continuous delivery and integration into operations and products.
The term MLOps is an evolution of the term DevOps, but specifically for Machine Learning software. So in essence, MLOps is to Machine Learning models what DevOps is to high grade software applications. The main difference is that MLOps is built around data infrastructure and orchestrating pipelines for ML models. MLOps in practice are the systems to reliably deploy, version-control, monitor and to tune model features with a high degree of accuracy and performance. At the same time, architecting and building data pipelines that continuously support these models in production.
Over the last 5 years, organizations have invested heavily in hiring talented Data Scientists to work on machine learning model development, using tools and libraries such as Scikit-learn, TensorFlow, PyTorch, Jupyter, and Pandas. These data Scientists are programming models in many languages, including Python, R, SAS, and SQL. Together these technologies are primarily used for developing features, assembling data sets, and running experiments. They enable Data Scientists to successfully develop machine learning models.
The problem here is that most Data Science has been limited to model development, not model deployment in production. ML teams struggle to prioritize models in production, because it is often unclear how they directly share results with engineering and infra teams to build the ML pipelines for iterative model development. As a result, most of these models fail to make it beyond experiments and testing, and into daily business use.
Studies have shown that approximately 85% of corporate AI initiatives fail to move beyond the experimentation and testing phases. Machine learning, commonly known as a subset of AI, effectively struggled the same fate. From the challenges facing machine learning projects emerges many questions:
To address these challenges we consider the obstacles of mindset, business strategy, and technical challenges to build a Machine Learning practice. In addition there are fundamental challenges affecting your success rate in achieving machine learning.
MLOps is an operating model to help data scientists and engineers overcome technical challenges, collaborate on model deployment, product teams incorporate ML into products, and business to have the correct infrastructure to execute their ML strategy. If you can build an MLOps practice, you will have the tooling necessary to advance your Machine learning initiatives with a higher probability of success.
MLOps should be used as a method of business intelligence aiming to eliminate all waste and make Machine learning systems scalable by automatic and producing both high value and consistent insights from ML models. In order to achieve a level of automation, MLOps brings DevOps principles to machine learning. The DevOps concepts of continuous development, integration, versioning, collaboration, and monitoring are applied to ML model development. These principles form the foundation of the MLOps practice to enable a faster development cycle, better quality control, and the ability to respond to changing business requirements.
An optimal MLOps architecture is the generation of automated environments for model development & design, model retraining, model development, model retraining, drift monitoring, automation of pipeline, quality control, and governance of a model into a single platform.
MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle – data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics. The standardizing and streamlining a machine learning project’s lifecycle management.
The rise of MLOps is supported by the emergence of open-source technologies such as Kubeflow and MLFlow. Kubeflow, as a tool dedicated to managing deployments of machine learning (ML) workflows that are deployed on Kubernetes. MLFlow is a platform to manage the end-to-end machine learning lifecycle, tracking experiments, recording and comparing parameters and results.
It is expected that the market for MLOps solutions is expected to reach $4 billion by 2025. We are in early days, but the strategy to adopt MLOps will take shape over the next few years. A well-structured MLOps practice makes it easier to align ML models with business needs, people, as well as regulatory requirements. While deploying ML solutions you will uncover sources of revenue, save time, and reduce operational costs. The efficiency of the workflow within your MLOps will allow you leverage data analytics produced for decision-making, and to build better customer experiences.
Depending on which stage your organization sits in its Machine Learning journey, developing a successful MLOps practice will require an upfront investment in people, technology and operations, then after experimentation and tweaking, a return on investment can be realized. Our tips for building a strong MLOps practice are:
Ultimately, the goal of a good MLOps strategy will be to accelerate the use of Machine Learning in your organization and find a higher success rate in the insights to improve your products and serve your customers.
Stay tuned, as we share an ongoing series of blog posts on MLOps, how to adopt it, common architectures and MLOps technical tutorials.