Research firm IDC predicted that global spending on cognitive and artificial intelligence systems would reach $ 57.6 billion in 2021. The firm also predicted that 75% of commercial enterprise applications would use AI. While we’re certainly seeing more widespread adoption, many organizations are having difficulty translating investments in AI into real business value. In addition, it’s widely estimated that most AI/ML projects will fail. That fact can make it even harder to get buy-in from key executives on these investments. This is where MLOps – Machine Learning Operations – can play a key role.
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ML issues
While it’s true that machine learning has tremendous potential to offer, it’s also true that getting to those possibilities can be expensive and time-consuming. So, while interest in implementing ML is high, actual production implementation remains low. The main hurdle of bringing solutions into production isn’t the quality of the models, but rather the lack of infrastructure in place to allow companies to do so.
Lifecycles are not all the same. For machine learning, the development lifecycle is profoundly different from that of traditional software development. Over the last 20 years, people have, for the most part, figured out what it takes for traditional software to go from development to production. They understand the compute, middleware, networking, storage and other elements needed to ensure the app is running well.
But for all they’ve learned, they have not understood that you can’t equate the software development lifecycle (SDLC) for the machine learning development lifecycle (MLLC). ML is a significant paradigm shift. Infrastructure allocations are unique; the languages and frameworks are different.
It can take just a few weeks to create machine learning models, but the process of getting these models into production can take anywhere from six to nine months due to siloed processes, disconnects between teams and manually translating and scripting ML models into existing applications.
Another problem is that once they’ve made it into production, it’s hard to monitor and govern machine learning models. There’s no guarantee ML models created in the lab will run      the way they’re intended in production. And there are several different factors that could be behind that.
How MLOps helps
There’s a lot that can go wrong as you deploy machine learning models in production. When IT/DevOps attempts to operationalize machine learning models, these teams need to manually script and automate the different processes. These models are often being updated, and each time the models are updated, the entire process is repeated.
As you develop an increasing number of models – and various iterations of these models – keeping track of them becomes a huge issue. One of the big issues is that often, the tools they’re using don’t address the problem of different codebases and frameworks being disjointed amongst each other. That can lead to problems, which results in wasting time and resources, among other issues. Most teams today also struggle with tracking and versioning as they update their models.
Machine learning operations (MLOps) essentially applies DevOps principles to ML delivery, helping to bridge the divides between data science and operations to manage the production ML lifecycles. That enables faster time to market for ML-based solutions, more rapid rate of experimentation, and assurance of quality and reliability.
You could possibly produce one or two ML models in a year using traditional SDLC models, with extreme effort and inefficiency. But with MLOps, you can scale, so you can address multiple problems. You can use these models to help better target prospective clients, find more relevant customers or find and improve inefficiencies. You’re able to roll out improvements much faster, ultimately improving productivity and profit.
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Succeeding at MLOps
Now, MLOps is not a cure for all ills. It’s still necessary to lay the right foundation and know the best practices for it to work. To succeed with MLOps, you need to focus on two primary duties. The first is understanding the different roles. You need to ensure you have the right, diverse set of skills and employees in place; don’t treat data scientists and machine learning engineers as one and the same. Both are necessary, but you need a mix.
Another tip: don’t try to do it all yourself. MLOps is also labor-intensive, requiring large teams of ML engineers. It’s important to think through what you need and look at the tools that are available to help you simplify the approach and streamline the number of dedicated people needed.
A solid plan for success
About half of all enterprise AI projects are doomed to failure, according to industry estimates. Company culture plays a role in the success or failure of these initiatives, as well as the mistaken idea that one lifecycle is like another. But not having the right tools in place is a major reason. MLOps is now available to help you succeed with your AI/ML projects, so consider using it as part of your plan to benefit from the advantages they can deliver.
