Machine learning is going to be huge.
It’s already so powerful that it has a use case for pretty much every industry that exists.
Just think about how it makes fluid automation a reality and cuts workloads in half. How it accelerates medical research and insights to save more lives. And how capable it is of predictive maintenance in areas like the energy sector, where disasters and major financial losses can be avoided.
But for ML to work, it requires a unique approach. No single individual and execute it alone.
ML requires a modern approach, guided by a system working to effectively design, deploy and manage ML models. Everything requires special attention paid to the way that data scientists work together with operations teams to create and release these models.
It requires Machine Learning Operations (MLOps).
What Are MLOps?
MLOps, or Machine Learning Operations, is a concept rooted in AI (Artificial Intelligence) and is centered around ML (Machine Learning).
Essentially, MLOps is a set of practices and principles that help data scientists and operations teams to collaborate more effectively during the development, deployment and management of ML models.
While ML is certainly a revolutionary technology on its own, without some form of structure or systematization in place, it can end up being a fruitless and costly endeavour.
That’s why MLOps exists. And although still relatively new, starting in 2015, the market for MLOps solutions will to reach upwards of $4 billion by 2025.
Quick Overview of ML
While more people are becoming familiar with the concept of ML, it wasn’t until the recent advancements in AI, compute power and our overall technological infrastructures that it became more popularised.
Now, having grown at an exponential level, these autonomous systems that are capable of learning are fast becoming an essential part of operations.
So what is ML?
Machine learning has its roots in AI. It essentially gives systems and processes the ability to learn and improve through experience. This is without the need to undergo manual or explicit programming.
Essentially, it’s the development of applications that can access, learn from and use data. Those applications are programmed to both learn from experience and improve their accuracy over time.
Much like we as humans do.
(For more on Machine Learning)
How Does It Work?
Making proper use of machine learning isn’t easy.
Deploying ML in an effective way means that you can’t simply leave data scientists on their own to figure things out.
It’s also not as simple as just running some numbers and leaving it to do its thing.
To put ML models into production, companies need to remove themselves from legacy thinking. With siloed departments and misunderstanding acting as a huge obstacle to production, a strategic approach needs to be taken to get the absolute best out of this technology.
Simply put, a data scientist can’t do it on their own.
It requires a fluid integration between a variety of disciplines. And because MLOps requires both data and code in order to output predictions, it requires data engineering and operations teams to collaborate in a highly agile way.
The aim is for these teams to ultimately streamline automation, generate high-accuracy insights, reduce waste and innovate at scale.
Why It Matters
MLOps allows your data scientists the freedom and flexibility to solve problems.
It helps remove many of the barriers preventing them from deploying accurate models and extracting insights expediently.
Similar to DevOps in that it helps integrate a seamless and functional system between operations processes and development cycles, MLOps helps drive fluid automation and precise insights at an accelerated rate.
So what exactly are some of the benefits?
Because of its diverse capabilities, ML has the ability to solve many complex and even mundane problems.
According to Geniusee, the main benefits of investing in MLOps solutions are:
- Faster innovation through powerful ML lifecycle management.
- The ability to create reproducible workflows and models.
- Easy deployment of high-accuracy models.
- Best practice management of the entire ML life cycle.
With MLOps, collaboration between data processing teams, analytics professionals and IT allows for quicker model development and deployment. The ML lifecycle gets monitored and managed by a modern framework that allows for faster innovation.
This approach also allows you to create machine learning pipelines that make it easier to create, design and deploy reproducible workflows for models. On top of that, traceability is improved through the detailed tracking of code and other metrics.
Want To Make Your ML Work? Then Consider MLOps
Making your machine learning deployment successful means looking at the bigger picture. The idea of ML is great. It works wonders and will come to impact industries to a large extent.
However, it’s critical to understand that ML requires more than simply running numbers or leaving your data scientists to fend for themselves.
The responsibility falls on the company looking to undertake this transformation. It’s their duty to inform their operations teams of new technologies and developments.
Your operations and data teams ultimately wield the power to make ML work. Make it easier for them and ML will become easier for you.
Want all of the benefits that come with MLOps and effective ML models? Let us tell you more.
