What is Machine Learning Operations (MLOps) and how can it be implemented?
Wed 31 May 2023
Artificial intelligence and machine learning technologies have opened up a myriad of opportunities for businesses in various sectors. However, these advanced technologies can be challenging to deploy and manage without streamlined processes. As enterprises strive to integrate these solutions more effectively, Machine Learning Operations (MLOps) has emerged as a game changer.
Coined by machine learning engineer Cristiano Breuel, MLOps is a set of practices combining machine learning, DevOps, and data engineering. Its primary goal is to streamline the deployment and maintenance of machine learning systems, making them more efficient and reliable.
The Power of Collaboration in MLOps
The main value of embracing MLOps is the focus on communication and collaboration between data scientists and operations experts. When successful, this approach can increase development speed at the same time as removing human errors.
As AI usage in enterprises remains at a relatively early stage, MLOps is more experimental than more established software development approaches like DevOps or DevSecOps.
An integral part of MLOps involves building hybrid teams that extend beyond the traditional software engineer talent pool. By including data scientists or machine learning experts who are more comfortable and experienced in both experimentation and model development can make the end product more likely to function as it is intended.
The MLOps process begins with data gathering, followed by data analysis and preparation. Once these preliminary data steps are complete, the process transitions to model training, validation, serving, monitoring, and eventually, model retraining.
Harnessing True Scalability
Managing a large number of AI solutions is a daunting task. MLOps simplifies this process, enabling the continuous monitoring, control, and delivery of multiple models.
Reproducibility presents another significant challenge when working with AI tools. Implementing these streamlined processes addresses this issue, thanks to the enhanced collaboration between stakeholders, which minimises miscommunication and conflicts.
Regulatory compliance concerning AI deployment is also a growing concern for enterprises. MLOps can simplify and expedite regulatory compliance due to the transparency inherent in the process. MLOps platforms foster an environment conducive to real-time communication, allowing staff from diverse backgrounds to interact on an equal footing.
Real-world applications of MLOps
Several leading companies have successfully adopted MLOps, demonstrating its value in diverse sectors. These case studies elucidate how MLOps can streamline the operationalisation of machine learning models.
Online-only supermarket, Ocado, utilised MLOps for numerous applications, including personalising product recommendations, predicting product demand, and managing warehouse robots. A notable use-case is their fraud detection system in which the company leveraged Google Colaboratory, Cloud Dataflow, and Cloud Composer.
The global cosmetics retailer, LUSH, employed these processes for on-device operations to recognise unpackaged products in their stores. Using Google Cloud Storage and Cloud Dataflow, they created an app that recognises products in real-time when a device is pointed towards them.
UK-based financial technology company, Revolut, used machine learning operations for its transaction monitoring system. Apache Beam, DataFlow, and CatBoost are among the tools used to develop and deploy their machine learning models that help combat fraudulent card transactions.
Uber implemented MLOps extensively across its services for driver-rider pairing, ETA predictions, financial planning, and more. They operationalised their machine learning models through an internal ML-as-a-service platform called Michelangelo, which manages data, trains, evaluates, deploys models, and makes predictions.
Netflix deploys thousands of machine learning models for various applications, from catalogue composition to streaming quality optimisation. To enhance user experience through personalisation, they use machine learning for homepage personalisation, show recommendations, and artwork display for each movie title.
Looking Ahead to the Future
The growing complexity of machine learning models and the increasing need for real-time decision-making capabilities necessitate an ever-evolving MLOps framework.
The future of these frameworks is promising, and its evolution will play a critical role in shaping the broader landscape of AI and machine learning. As these advanced technologies continue to revolutionise industries, MLOps will continue to be a key enabler of sustainable and efficient AI deployment and management.
As a result, these processes will undoubtedly become even more integral to businesses navigating the ever-evolving landscape of machine learning.
Embracing MLOps not only paves the way for technological advancement but also promotes responsible AI use and, ultimately, a more innovative and inclusive future.
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