How Prominent Brands Implement MLOps: Use Cases to Check Out
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While creating your machine learning model you might notice that the process is very complicated. Terabytes upon terabytes of convoluted data, constant maintenance, random bugs — the list goes on.
But it doesn’t have to be this hard. In recent years, the ML industry developed a set of principles that make the development process much easier and faster.
By combining the best practices of traditional software development and machine learning model development, Machine Learning Operations (MLOps) practices were born. Today, we want to discuss what they are and how they can be beneficial based on real-world MLOps examples.
Understanding MLOps
MLOps is a principle behind maintaining and deploying models in the machine learning niche. The improvements it brings to the ML workflow and its lifecycle management are widely praised for high efficiency and reliability.
There are four key principles in MLOps:
- Version Control: By tracking changes in your ML model, you can reproduce the results of experiments or roll back to previous versions in case a faulty version goes into the production environment;
- Automation: MLOps offers automation of tedious processes and the data pipeline to free your data scientists from constant maintenance of your ML model;
- Continuity: MLOps allows for continuous integration, delivery, and monitoring of your trained models;
- Model Governance: MLOps manages all aspects of ML systems for improved efficiency.
Thanks to MLOps, you can work on multiple models at once, giving your team the ability to expand the scope of their data science projects.
Furthermore, MLOps connects data science and operations, integrating AI and ML capabilities into real-world applications. This complexity and diversity positively influence market growth.
According to Business Research, the MLOps market size was 1.1 billion USD in 2022 and will reach 9 billion USD by 2029.
Forecasts like this are based on the demand surge that started during COVID and the latest trends in the MLOps market, such as cloud-based platforms and services.
The main driving factors for market growth are the following:
- Increase in complexity of machine learning models;
- Diversity of ML models;
- Rising need for collaboration and alignment among stakeholders.
ML model deployment involves various stakeholders and requires more sophisticated management methods and tools. MLOps is here to address these requirements.
It enables organizations to standardize their ML workflows across different teams and projects. It also provides a common platform and language for stakeholders. So, we’re pretty sure you can expect to see more analogs of MLOps pop up in different fields of AI models’ development.
MLOps Examples that Drastically Change the Game for Businesses
As you can see from the benefits, MLOPs have a lot of potential to offer to any business. But are there any MLOps use cases that work in real life? We have gathered examples of successful companies that chose MLOps implementation for their workflows.
Starbucks as a Data-Driven Company
Starbucks launched its mobile app in 2011 intending to create a digital-based loyalty program. However, it was quickly expanded to have information about menus, store locations, and opening hours.
Soon enough, Starbucks realized that having an app could give them a significant competitive advantage. All they needed to do was analyze data to maximize customer lifetime value.
The development of such a sizable project might seem like a head-over-heel idea. The sheer amount of data processing couldn’t be operated on without an easy and quick way to scale an ML model. Luckily for Starbucks, MLOps practices provide just that.
In 2019, they introduced a new AI-driven platform based on MLOps practices named “Deep Brew”. The main goal behind this initiative was to “elevate every aspect of the business and the in-store and customer experience”, as described by Starbucks’ chief technology officer Gerri Martin-Flickinger.
Deep Brew is a platform that allows personalizing the customer experience and optimizing store human resources allocation and inventory management.
Deep Brew has proven to be a huge success for Starbucks. This platform helped the brand to make business decisions that resulted in significant growth. Since its launch in 2019, Starbucks’ net revenue grew to almost 36.8 billion USD with an 11.46% increase each year.
The standard set by Starbucks with this MLOps example pushed the industry to evolve and switch to becoming data-driven to stay competitive.
I’m Lovin’ It: McDonald’s and Machine McLearning Operations
Another great MLOps example is McDonald’s. Since its initial opening in 1940, McDonald’s has grown into the world’s second-largest restaurant company by revenue. The brand has drastically changed over the years, adding more and more innovations. One such innovation was the concept of a drive-thru.
Drive-thru was so successful that in America, approximately 70% of all purchases were made from cars. This figure made McDonald’s look into ways of making this innovation as efficient and convenient as possible.
In 2018, the company added a machine learning solution based on MLOps practices to drive-thrus at select locations. This solution considers purchases made by other customers when updating the offers presented on ordering displays. Soon enough, they expanded the functionality and now it covers the experience not only at the drive-thrus but inside their stores as well. Use of the MLOps examples can be found here:
- Store demand and forecasting;
- Product recommendations;
- Drive-thru automation;
- Customer lifetime value;
- Sentiment analysis of product reviews.
After the initial tests, McDonald’s decided to roll out this MLOps solution to more US and international locations in 2019. Now McDonald’s aims to scale this technology further to improve the personalization of customer experience.
How Walmart Changed its Resource Management
Walmart is one of the biggest retail corporations in America. As of the start of 2024, Walmart has over 10 thousand stores and clubs in 24 countries. You can imagine how hard it is to manage the resources of a corporation that has so many locations. So, how do they do it?
Walmart utilizes machine learning operations to enhance the efficiency and scalability of its machine-learning models. It provides Walmart with the ability to make data-driven decisions. This has a positive outcome on operational efficiency and customer satisfaction.
Netflix and Real-Time Model Monitoring
Netflix has provided streaming services since 2007. As its user base increased, alongside did the catalog of movies and TV shows. The recommendation feature was born in the process, which was enhanced with AI soon enough. However, the number of viewers and offered content has continued to increase, requiring more efforts from AI engineers and data scientists.
It was decided to utilize real-time model monitoring to ensure recommendation quality using MLOps. Its practices help detect whether the user played a recommended video and compare the data to the predicted outcome. If there are any discrepancies in performance metrics, the system alerts the machine learning team to investigate and fine-tune the model.
Continue reading our original article to find out how Ocado, Spotify, Lush, and Revolut applied MLOps to optimize their operations.
