For AI-oriented businesses and startups who rely on machine learning models, there’s a way to take efficiency to the next level. It’s MLOps.
MLOps can help data scientists offload routine tasks and focus more on data science. But the benefits of MLOps goes beyond day-to-day ML processes. It can actually have a profound impact on business performance as a whole. By bridging the gap between ML and Ops, MLOps takes the automation of machine learning processes one step further, adding significant value to the business.
How? Let’s look at three ways AI-focused businesses can benefit from MLOps and increase their value at the same time:
1. Productivity
Automation is the great time saver, and the same goes for MLOps. By taking the load off of data scientists, MLOps gives them the freedom to focus on data science. This leads to improved time to market, which enables the development and release of more product versions, and a better customer experience, all of which adds value to the business. MLOps allows for much faster deployment of ML models too, yet with increased quality and consistency. This only improves the product, and also creates value.
Time is money. A key benefit of MLOps is that it saves time and puts it back in the hands of the data scientists, so they can be more productive. The result? More business value.
2. Cost efficiency
MLOps enables automation and monitoring of all ML processes and activities. This supports repeatability and auditability, for a far more efficient workflow in which important details don’t fall through the cracks and precious resources are not going to waste.
Machine learning activities typically carry a lot of technical debt. If a data scientist leaves the company, then a lot of knowledge and insight about the ML model goes with them. Significant resources must be devoted to training another individual to quickly catch up and take over. Employee turnover is a pressing issue for ML-based businesses, but MLOps takes the sting out of it, by centralizing and automating all the ML processes so they are fully trackable and auditable. Bottom line? MLOps helps businesses optimize their manpower, thereby reducing waste, and boosting cost efficiency.
3. Innovation
There is a direct line that leads from productivity to innovation. By offloading everything possible to MLOps, the data science team can sharpen their focus and devote precious time and energy to innovating – developing new algorithms and models, realizing product enhancements and features, thinking and working out-of-the-box to get ahead of the competition with bigger, better offerings. For any AI-driven startup, that’s the single most important stepping stone in the path to becoming a unicorn or going public.
Automating the ML lifecycle brings you biz value
Advancing data science is the key to creating business value. Don’t let your data scientists spend too much time managing infrastructure or maintaining models. Rather, turn to MLOps to free them from operational tasks, and allow them to be fully productive, and efficient with their resources, but most of all, enable them to innovate, innovate and innovate some more.
Getting Started
AllCloud is one of the few AWS partners to hold the Machine Learning Competency, and has expertise that cover the full lifecycle of ML models, from consulting, planning, and deployment, to improving existing ML models and full CI/CD pipeline deployments for models and training data. AllCloud’s integration of MLOps and managed services includes full monitoring and security services orchestration, providing an additional layer of sophistication to ML workloads and ensuring our customers use ML in the most efficient and cost-effective way possible.