For years machine learning (ML) researchers have focused on building outstanding models and figuring out how to squeeze every last drop of performance from them. But many have realized that creating top-performing models doesn’t necessarily equate to having them deliver business value. Often the best models can be very complex and costly to run in production. In fact, the vast majority of models (87%) never make it into production according to VentureBeat .
Machine Learning Operations (MLOps) ensures that the models built by the Data Science team are deployed in production. It is the umbrella term encapsulating the technologies that automate ML models’ management, deployment, monitoring, and alerting. The Data Science team builds it and MLOps ensures production-grade infrastructure is there to run it.
In this article let’s discuss why now, more than ever, companies need to think about MLOps.
Undeployed models are useless. In fact, they are worse than useless; the time and money invested in developing AI-powered solutions are intended to result in tangible business value, and the only way to do that is by deploying them in production. By providing a framework and the tools to deploy your models, MLOps ensures that the business value of a model is realized.
A Data Science team that spends its time building models that are never deployed will quickly become fatigued and demotivated. In contrast, a team constantly deploying new models will see their impact across the business and will be excited and motivated to build on their results. Thanks to this positive feedback loop, they will want to stay at your company for longer and be inspired to experiment and be creative, key motivators in the competitive market for top data science talent. This creativity can lead to innovative solutions that other teams wouldn’t be able to dream of.
Often Data Science teams spend days building models only to have the Ops team tell them the solution is infeasible. This situation is a lose-lose for both teams, and can be a cause of much frustration. However, having a solid MLOps function results in more models in production; this gives the Data Science team more reference experiences about what models can and cannot be deployed. The Data Science team can then use this knowledge to spend more time on projects that have a high chance of being deployed and reduce the time spent on models that will never work in production.
Moreover, without MLOps, the Data Science team would have to deploy and maintain their models manually each month. This work is rote, time-consuming, and doesn’t yield the new models that actually move the business forward. Thankfully, with MLOps, your team can automate away most of this tedious work, resulting in substantial time savings and keep your Data Science team focused on high-value, engaging work.
ML Engineers cannot fully know how well a model performs or what kind of data they will see in production until the model is actually in production. They use various techniques to estimate, and to reduce the risk of developing poor-quality solutions, but the only way they can really know is once the model has been deployed.
It can be an arduous process to retrain a deployed model. However, an efficient MLOps process knows that you will want to retrain your models in a few days/weeks/months anyway. So, it implements fast, repeatable workflows that allow you to account for model drift or completely retrain a model on new data.
Implementing such practices has been a bottleneck for Data Science teams for a long time, and it is only recently that tools have come on the market to do this. These Continuous Integration / Continuous Deployment (CI/CD) processes and Model Approval Workflows are yielding very positive results, and we can expect to see them implemented more and more.
The introduction of the Algorithmic Accountability Bill in New York City and GDPR in Europe has tightened regulation around AI-powered solutions. If you want to use AI to grow your business, you must know the rules and stick to them. A solid MLOps function enables you to automate compliance and ensure all your models work within the law. Say goodbye to wasting valuable hours manually doing it each month.
Training high-quality models is without a doubt still a difficult task, but it has become significantly more straightforward over the last few years. Plus, thanks to tools such as H2O’s AI Cloud , you can automate away most of the work. However, in the future, the businesses that outperform the competition will be those that consistently get their models into production, delivering top-notch predictions. This advantage will only be available to the teams with an exceptional MLOps function.
Data Science and IT teams have a wide variety of tools that they use within their daily jobs – sometimes due to functionality, and often due to preference. As machine learning and software development/deployment tools are ingrained within organizations, businesses need to decide on their tooling choice for MLOps. Choosing a “closed” toolset would lead to only a select number of products that would be compatible, and likely years of being locked in with a particular vendor. Selecting an “open” or portable tool now, would lead to optionality in the future to use the most preferred tool for any of the machine learning lifecycle modules.
We’ve looked at the top, high-level reasons your company needs to think about MLOps now. Huge strides have been made in this area over the last few years, and having a first-class MLOps function is achievable for all companies. Doing so will lead to more models in production delivering consistent value for your business.
If you want to give everyone in your company the power to create and deploy incredible models with minimal effort, you can request a demo of H2O AI Cloud.