Are data scientists doing data science?
Not as much as they should. According to a report by Algorithmia on the State of Enterprise Machine Learning, at least 25% of a data scientist’s time is spent on deploying machine learning models. What’s more, deployment takes anywhere up to 90 days, depending on company size and other factors. In some instances, the market is changing so rapidly that by the time a model is actually deployed, insights are OBE (Overcome By Events). Meaning the data is already outdated and the model won’t be effective.
To keep up, do data scientists need to sleep less or somehow make more hours in their day?
Thankfully no, because there is an alternative: MLOps.
A Data Scientist’s Best Friend
MLOps, or Machine Learning Ops, is to ML what DevOps is to Dev. It provides methods to manage, monitor and maintain the lifecycle of a machine learning model. With MLOps, once a model is built and deployed, it can run, improve and scale independently and accurately, without needing a data scientist to ‘babysit’. That’s why more data scientists and their managers are turning to MLOps as a solution to drive efficiency and performance of their teams.
What Data Scientists Gain From MLOps
The most important work of a data scientist is creating algorithms and AI models that can take the product or business to the next level. What data scientists shouldn’t be spending their time on is managing and maintaining those models. Not when MLOps can do the work instead.
Here are the main benefits of MLOps to the everyday performance of data scientists:
With access to pre-set automated operations, data scientists can offload much of their ML model management tasks and spend far less time on data snags and other routine issues.
By relying on automation for ML model maintenance and management, data scientists benefit from far more accurate and repeatable processes throughout the model lifecycle.
With every action automated and recorded, MLOps enables tracking down to the last detail. If something goes wrong, data scientists can easily analyze the issue and find out who built the model, how they built it, and which data they used. Without MLOps, this level of auditability is impossible.
Here’s the cherry on top. By freeing up so much of their time, energy, and skill, MLOps lets data scientists focus on what they are actually supposed to be doing: data science. This leads to more product improvement, more agility to react to market changes, more experimentation, more development, and – ultimately – more innovation.
Focusing on Innovation
By doing less, data scientists can do more. Less routine tasks, less dealing with missing data, less distractions that lead to long deployment times and reduced efficiency. More algorithm development, more model creation, more innovation.
With MLOps, data science becomes what it really should be, and what it can be. Data scientists don’t need more hours in the day. They need the hours they have to be used more effectively. And this is exactly what MLOps gives them.
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.
Want to learn how you can start focusing on innovation?
Check out the MLOps FAQ blog, and talk to us about how MLOps can help your data team and business.