A Beginner’s Guide to MLOps and the Importance of It
Some people say that ‘Data Science’, ‘Machine Learning’, and ‘Artificial Intelligence’ are nothing more than buzzwords, but I beg to differ.
The Machine Learning (ML) and Artificial Intelligence (AI) industry is MASSIVE, but more importantly, it is GROWING. What started off as a $12 billion industry in 2017 is projected to grow to $57.6 billion by 2021, according to International Data Corporation. That’s an increase of 480% in four years!
Why is ML and AI growing so fast? Because of its potential.
According to McKinsey, organizations that have successfully put ML and AI into production reaped a profit margin increase of 3–15%. Some are even predicting that AI and ML applications will enable profits to increase by 38% by 2035.
That being said, according to the same McKinsey report, up to 88% of corporate AI initiatives are struggling to move beyond testing and deployment stages.
This isn’t surprising, considering how little resources there are on model deployment, model management, and MLOps compared to the hundreds of resources on ML modeling.
Therefore, this article serves as an introduction to MLOps. You’ll learn what MLOps is, what it involves, and why it’s important.
What is MLOps?
MLOps is short for Machine Learning Operations, and it encompasses all tasks required to manage the ML lifecycle, including model deployment, data governance, and monitoring of metrics.
The goal of MLOps is to increase automation and improve the quality of ML production while meeting business and regulatory requirements.
You can think of MLOps as a brother of DevOps, a well-known practice in developing large-scale software systems. DevOps encompasses the ideas of shortening development cycles, increasing deployment speed, and providing dependable updates and releases. Since a machine learning system is a subset of a software system, similar practices of DevOps can help to build and operate ML systems.
Why is MLOps important?
MLOps is increasing in importance because it addresses a number of problems that organizations face when productionizing machine learning applications (hence why 88% of organizations are struggling to move beyond testing). These problems include the following:
- Deployment and automation: MLOps management platforms allow you to automate model containerization and eliminate one-off work. These platforms also provide simple frameworks for model deployment, making it easier to test models in a production environment and subsequently deploy them.
- Reproducibility of models and predictions: MLOps management platforms give you the ability to reproduce model behavior in production without disrupting existing traffic.
- Diagnostics: By leveraging MLOps management platforms, you can indefinitely monitor your ML models and run diagnostics to make sure that they are performing at an optimal level. These platforms automatically identify and alert you of anomalies and model drift.
- Governance and regulatory compliance: By applying MLOps practices, you’ll ensure that you’re compliant and conducting proper governance practices.
- Scalability: By applying MLOps practices, your ML lifecycle will be more efficient, increasing your capacity, and ultimately allowing you to scale your ML models.
- Collaboration: Lastly, the use of an MLOps platform provides the ability to link all stages of an ML application in a single platform, making it easier to collaborate with other users, whether it be a data scientist, an operations analyst, or a business analyst.
Why is MLOps so Hard to Practice?
If MLOps is the solution to a number of problems, why don’t all companies adopt MLOps practices? Well… it’s not that simple.
It’s very common that data scientists don’t have a programming background or engineering experience, which means that they haven’t had exposure to DevOps practices. Also, it’s very common that engineers and data scientists are siloed from each other in a given business, as their objectives and functions are usually distinguished. Overall, machine learning production in enterprises is still relatively new and developing.
That being said, there are a number of solutions, like Datatron, Datarobot, and ParallelM, that are being built in order to address these problems.
Three Levels of MLOps
Google has defined three types of MLOps with each level increasing in the level of automation, as well as a number of other benefits. However, each level also increases in difficulty to implement:
Level 0: Manual Process
This is the most basic level where the process of building and deploying ML models is entirely manual. This means that each step in the ML lifecycle is executed manually, as well as the transition between each step. This type of practice is very common for companies that are new to adopt machine learning in their business practices. This process is usually sufficient when models are rarely changed after its initial use.
Level 1: ML Pipeline Automation
At this level, the ML pipeline is automated — this includes all steps from data extraction and data preparation all the way to model validation. Not only are the steps automated, but the transition between each step as well. By doing so, it allows you to get continuous delivery of model prediction services. Unlike level 0 where you only deploy a trained model, you’re deploying a whole training pipeline in level 1.
Level 2: Continuous Integration (CI) and Continuous Delivery (CD) Automation
Automating continuous integration and continuous delivery allows for a quick and reliable update of the pipelines in production, which ultimately allows data scientists to explore ideas around feature engineering, model architecture, and hyperparameters much faster.
Where Can I Learn More About MLOps?
While there aren’t too many resources on the Internet that talk about MLOps, there are a couple of good links that you can check out:
- Datatron’s website, which covers all of the benefits of an ML Management Software.
- Google’s Guide to MLOps, Continuous Delivery, and Automation
- Microsoft’s Guide to Deploying and Serving Models
Thanks for Reading!
After reading this, you should know what MLOps is, the problems it addresses, why it’s so difficult to implement, and the three levels of MLOps.