Developing machine learning (ML) models are a taunting task for data scientists, however, managing these models in production can be even harder. In order to have successful results, data scientists need to recognize the model drift, retrain the model with updated data sets, improve performance, and maintain the underlying technology platforms. Hence, developing production-ready models are something difficult and long to achieve.
New challenges always appear once ML models are deployed to production and used within the business processes. With more organizations adopting ML, there is a need to be aware of model management and operations. This is where MLOps – Machine Learning Operations – comes into play to make model management and operations easier and faster.
What is MLOps
MLOps is the use of machine learning models by DevOps teams. MLOps aims to add discipline to the development and deployment of machine learning models by defining processes to make ML development more reliable and productive. MLOps is a set of best practices for businesses to run AI successfully.
However, the development of ML models is relatively new and requires extensive experimentation and optimization. Data scientists must strive to prove the accuracy of their models.
Similar to software development, ML models require frequent maintenance and enhancements, by maintaining the code, platforms, and infrastructure as well as model drift. When new data becomes available, models tend to drift, and the predictions and recommendations provided by machine learning models deflect from expected outcomes.
What are the benefits of MLOps?
Organizations start investing in MLOps as it can boost their productivity as well as create trusted and efficient models.
One of the most important benefits of MLOps is that it helps bring ML workflows to production by decreasing tension between data science teams and operation teams. MLOps systems create good dynamic, adaptable pipelines which then improve DevOps systems and can adapt to changing KPI-driven models.
Moreover, MLOps allow automatic and streamlined changes by consistently measuring and ordering the behavior of the model when it is running. MLOps can also help in regulatory capacities as it is able to reproduce models accordingly to the original standards. Thus, the systems continue to work by the original requirements despite the change in pipelines and models.
MLOps can detect anomalies and defect in machine learning development quickly, hence, helping IT teams to understand quickly what needs to be fixed and improved. This leads to less failure or slowing down of the systems. MLOps support models as they adapt to their own evolution and drifts in data, hence developing dynamic systems.
Therefore, MLOps is boosting the credibility, reliability, and productivity of machine learning development, leading machine learning to be a respected asset of software development.
The future of MLOps
New MLOps capabilities are released as more enterprises are getting value and maturing their data science programs. For instance, SAS introduced a feature contribution index that will help data scientists assess models without a target variable.
MLOps is able to bring scientific methodology and collaboration to data science teams. Indeed, MLOps could run multiple experimental models in parallel in order to challenge the production’s accuracy or even to help improve the speed to markets and data quality.
Yet, let’s not forget that MLOps is an evolving discipline and it is inclined to disappointments. MLOps bolster the exercises of individuals who create ML models, send and deal with the framework. From arranging many pipelines of models to scaling ML applications to guaranteeing ML well-being, MLOps is the key to safe and efficient ML tasks.
In time, MLOps will make collaboration and integration easier, as well as allowing the data scientists to use their time to do other things and develop more models. With MLOps, the retraining, testing, and arrangement are automated, by taking the entirety of the new information accumulated and retraining the models. That is what that data scientists are performing manually.
Conclusion
Although it still has a long way to go, MLOps can help unify disparate teams taking on challenging, innovative projects. A successful MLOps strategy can improve the efficiency, accuracy, and productivity of organizations in the long run.
