You’re probably already aware of machine learning and its usefulness in today’s applications. Artificial intelligence (AI) and machine learning (ML) have made it easier for developers to come up with smart software that can predict outcomes accurately, or automate several types of tasks that would otherwise be done by humans. As much as it is important to implement machine learning into an application, it is even more important for businesses to run it smoothly.
For this purpose, there are a set of best practices used by companies, which are known as ‘machine learning operations’, or MLOps.
According to a report by Deloitte, the market for MLOps solutions is projected to grow from $350 million in 2019 to $4 billion by 2025. MLOps has been developed to make it easier for teams to collaborate with each other towards a common output or goal, and it holds a lot of benefits within it.
In this article, we’ll talk about:
This chapter is part of our Comprehensive Guide to DataOps.
What is MLOps?
MLOps refers to machine learning operations, and it involves a set of best practices or methods that can be used by software companies to handle and implement machine learning properly. The need for MLOps arose when companies started to manage higher amounts of data on large-scale models, something which they didn’t need to do before.
As machine learning is still evolving and companies are coming around to it, there’s a need for better communication between the data science and operations teams, so that the end product is accurate, scalable, tested, and released in time.
MLOps can also be understood with the help of the software development lifecycle (SDLC). A regular lifecycle starts with requirements and moves on to design, development, testing, and deployment before finally ending on maintenance.
However, when you consider it from the MLOps point of view:
- Your SDLC starts with the definition of KPIs and objectives by the business development team.
- This info is passed on to the data engineering team for acquiring data and preparing it according to the model to be developed.
- Next, the data science team comes into play and develops the model according to the given data.
- Lastly, the DevOps swoops in and manages deployment and monitoring.
MLOps is being implemented and practiced by some of the leading companies in the world, including Uber, Netflix, DoorDash, Revolut, and several other big names. Not only that, but it can also be easily implemented by startups and small companies, and it can help them achieve faster deployment and release times.
If you’re looking for a comprehensive MLOps definition, it would be something like this:
“Machine learning operations, or MLOps, is a principle in engineering and software development that encompasses the development and deployment of machine learning systems, in an attempt to make the delivery of high-performance models smooth and rapid. The more the amount of data you are dealing with, the more you will need MLOps for your organization.”
Why is MLOps Useful?
MLOps can be used to streamline the delivery of data models, and it helps different teams collaborate and communicate more smoothly. Apart from this, it also addresses several challenges that companies face when they are working on developing an efficient and robust data model.
- MLOps helps curb the shortage of machine learning engineers who can develop and deploy web applications. This shortage is caused due to a lack of data scientists who have this skill set. Therefore, these data engineers can help bridge the gap between data science and DevOps.
- MLOps is an efficient way of reflecting any changes in the model in real-time. For instance, if the business development team decides to change any of the KPIs or objectives later on, it won’t be a problem for the corresponding teams to ensure that the model doesn’t fail. The model will be made to continuously learn and update itself according to the changing data.
- There has been a major gap between different teams involved in the development and deployment of data models, which only causes projects to be delayed or slowed down. However, by implementing MLOps, various teams will be in constant communication with each other, and the back and forth exchange would ensure that the development of the model doesn’t suffer.
- MLOps is also useful for risk assessment and enabling development teams to stick to their initial business objectives or KPIs. It has been often observed that they tend to deviate from the actual purpose of the model and the end product isn’t as accurate or useful as expected. Therefore, MLOps can be used for constant monitoring and feedback, and it can be used to determine the risk of failure from time to time.
DevOps vs MLOps
By now, you have a clear idea of how MLOps impacts organizations and helps them build stronger and more accurate models. However, there may be a question somewhere in the back of your mind about the difference between MLOps and DevOps. You may be thinking about whether MLOps is just DevOps for machine learning applications.
On the surface, you might find both of them to be similar. DevOps is a set of best practices that help accelerate the software development lifecycle and maintain the quality and delivery of software. On the other hand, MLOps refers to a set of best practices that can be used to implement machine learning perfectly, so that an accurate data model is developed.
If you compare the two based on the cycle or workflow that they follow, you would find that the MLOps pipeline has a few extra steps, which pertain to data acquisition and modeling. Moreover, each step of the cycle is also more extensive when it comes to MLOps.
Both the disciplines also vary in the context of development. In DevOps, you will be using code to develop an application or interface. This code is encapsulated into a package that can be executed and deployed. The cycle keeps going on automatically until you reach the end product.
On the other hand, MLOps involves the development of a machine learning model. In this landscape, development refers to training and building the model. The end product of this exercise is a serialized object that inputs data and outputs inferences based on its training.
Another difference between the two disciplines can be observed with respect to version control, which refers to monitoring changes in the code and packages in a DevOps environment. However, it is much more extensive in an MLOps pipeline, as it requires continuous experimentation, monitoring of components and metrics, and much more.
Here’s a table that summarizes the key differences between MLOps and DevOps.
DevOps | MLOps | |
|---|---|---|
Cycle | Software development lifecycle (SDLC)
| SDLC with data and modeling steps |
Development | Generic application or interface | Building of data model |
Package | Executable file | Serialized file |
Validation | Unit testing | Model performance/Error rate |
Team roles | Software and DevOps engineers | Data scientists and ML engineers |
MLOps Best Practices
Since MLOps involve practices that streamline and optimize the delivery of machine learning models, there are some industry-standard best practices that every data scientist or ML engineer should know about.
1. Naming Conventions
When you are implementing machine learning, there are thousands of small variables that are in play, meaning that you can easily confuse several of them if you don’t name them properly. Therefore, you should have a clear and comprehensive naming convention for your project before you start.
2. Checking Code for Quality
Code quality depends on several factors, but it hinges on three things: your code fulfills the intended purpose, it doesn’t have any errors or bugs, and it can be easily understood, maintained, and extended. Since you are working with large amounts of data, your code should always be clean and readable.
3. Keeping Track of Experiments
If you want to develop the best ML system, you should let it evolve with the ever-changing ideas and principles. Even if your ML model runs smoothly, you can always experiment with new methods and concepts that may increase its accuracy or even its efficiency. Whatever you do, make sure to track your experiments and their outcomes.
4. Data Validation
When data is moved from the acquisition to the modeling stage, a host of issues can arise. For instance, the data may have different training and statistical properties, or the training data is full of errors. This can be catastrophic if your model is trained on invalid data, which is why you should always check for data validity, correct formats, etc.
5. Resource Utilization
As mentioned above, you should always experiment with new ideas and concepts in your ML system. However, remember that it takes not only time, but also costs money. There is a significant usage of system resources when the model is being trained, and more so when it is deployed. Therefore, you should always keep track of the budget and resource utilization before moving on with any experiment.
Examples of MLOps
Like we mentioned above, several companies have successfully implemented MLOps in their operations, and they can drive better and more accurate results. Let’s take a closer look at three of them, and how they’ve leveraged MLOps to improve their operations.
Uber
Uber developed a scalable machine learning model for several applications in its business infrastructure. These included the estimation of delivery time, predicting the driver demand for specific locations, and also customer support. The company managed to use machine learning in the right way, and also developed better coordination between teams.
Booking.com
Booking.com is another popular name that has used MLOps for their 150+ machine learning models that are in production. They made use of an iterative and hypothesis-driven process and integrated it into their business operations to fetch better results for customers and also streamline their processes.
Cevo
Cevo constructed an automated ML pipeline for its Australian financial sector client who wanted to deploy and maintain numerous ML models to detect and avoid fraud. By applying MLOps concepts to the project, they claimed that their client has been able to decrease the time to train and deploy ML models from months to days. For example, a model capable of detecting new kinds of fraud every month was created in just 3 hours.
MLOps and Satori
Satori, the DataSecOps platform, helps you streamline secure access to sensitive data. This is done by applying universal security policies to all your data stores, enabling fine-grained access controls by non-data-engineers, and applying simple access workflows.
Conclusion
To summarize it all, machine learning can be quite challenging, but it can be done right if you make use of MLOps to facilitate communication between the teams involved in the development and deployment process. Not only is it comprehensive and streamlined, but it can also be cost-effective and help companies save time as they are developing new ML systems.
