Machine learning operations (MLOps) is a new field that has been created to help companies with machine learning.
It’s what happens behind the scenes when you’re trying to use machine learning for your business.
In this article, we’ll explore what is MLOps and what it entails for businesses.
We’ll also explain the challenges faced on a day-to-day basis, what they do in an ideal world, and what skills are needed to succeed in this field.
What Is MLOps?
MLOps is a term to describe the process of managing and operating machine learning models in a business setting.
It involves the activities required to get a machine learning model up and running. This includes everything from data collection to training to deployment.
Basically, this means working with teams of data scientists, engineers, and product managers.
You’ll need to have strong technical skills as well as good communication and problem-solving abilities.
Why Is MLOps Important for Businesses?
Machine learning is becoming an increasingly important part of the business landscape.
Machine learning can improve decision-making, automate tasks, and gain a competitive edge.
Though, machine learning is not easy to put into place. It’s a complex process that requires careful planning and execution.
This is where MLOps comes in. By having a dedicated team focused on your machine learning models, you can ensure that they provide value to your business.
What Are Some Machine Learning Challenges?
Nowadays, machine learning has become a buzzword. Companies are rushing to install it without understanding what they’re getting into. This can lead to poor implementation and lots of wasted time and money.
This is true for smaller businesses that lack the resources needed to support their initiatives properly.
Some common challenges include:
- Choosing the right type of model (classification vs regression)
- Identifying what problem you actually need machine learning for in your organization
- Having a mismatch between data scientist skills and business needs (too much math vs not enough coding ability, etc.).
Understanding these challenges will help you create the best MLOps strategy.
What Skills Do You Need?
To be successful in MLOps, you need a mix of technical and non-technical skills.
Technical Skills Include:
- Data engineering: Collecting, cleaning, and transforming data so that it’s ready for machine learning models
- Machine learning: Building and training models using different algorithms
- Python/R programming language: Used for coding and scripting tasks
Non-Technical Skills Include:
- Business analysis: Understanding what problem you’re trying to solve with machine learning and what data is available to do so
- Project management: Planning and executing projects on time and within budget
- Communication: Working with teams across different departments in a clear and concise manner. Communication is key for any MLOps strategy.
As you can see, there are several skills you need to master to create an effective MLOps strategy.
How Do Businesses Benefit Financially From MLOps?
The benefits of MLOps can be broken down into two categories: short-term and long-term.
Short-term benefits include things like:
- Faster time to market
- Increased efficiency
- Improved accuracy
Long-term benefits are more strategic in nature and can include things like:
- Competitive advantage
- New product lines
- Reduced costs.
In order to realize these benefits, it’s important that businesses have a clear goal for their machine learning initiatives and an ML strategy for achieving them.
Learn More About Machine Learning
Learning what is MLOPs isn’t difficult when you understand how machine learning works. By reading this article, you should know the basics of developing a great MLOps strategy.
Do you want to learn more about machine learning? If so, check out more of our online articles.