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Organizations are always struggling to get their machine learning models up and running and getting them into production to support their businesses. Data scientists create machine learning models, but are generally unaware of the production aspects of implementing or qualifying these models. They usually refrain from touching production in case something goes wrong. It is also usually not your responsibility to perform DevOps tasks such as model deployment. Traditionally, these DevOps functions and the work of data scientists have been isolated. Let’s look at all of this in the background and look at five challenges that machine learning models pose in manufacturing.
- Periodic Redeployment of Machine Learning Models
Machine learning models need to be implemented over and over as they degrade over time. This is in contradiction to the software engineering principles that software developers practice. In their case, code once implemented is good forever and only when code is improved is it necessary to be redeployed. But machine learning models can lose value over time. This must be observed throughout the life of the model and requires careful monitoring.
- All About the Monitoring
Unlike software engineering code, monitoring machine learning models may require additional effort. Because these models are trained on the data and then implemented, the data must also be accurate and free from unsafe anomalies. Tracking should be created for the incoming feature vectors in order to detect deviations, distortions or anomalies in the data. With this in mind, it is also important to monitor and alert the incoming data.
- Machine Learning Models Span Three Verticals
When it comes to software engineering code, development teams only have to worry about the language in which the code is implemented. These developers build an infrastructure based on this code, and then the systems with this code are ready for a lifetime deployment. This does not apply to the implementation of the machine learning model. In addition to programming languages, you need to consider factors such as libraries and frameworks. In order to be able to support these three verticals, a suitable infrastructure or a platform-like tool is required. The development of machine learning models takes place in a very heterogeneous environment. Data scientists use a variety of machine learning frameworks and languages that can use libraries that depend on the underlying hardware, such as Nvidia’s CUDA, and other types of dependencies that can create backend challenges.
In the case of machine learning, data scientists would find great support in a common platform that can work with all kinds of frameworks and programming languages as they could focus on domain knowledge rather than being limited to a limited number of frameworks. A faster way to deploy models developed in production would give data scientists more freedom, as they could deploy models and different versions more frequently, which would help to quickly grasp the root causes of actual production problems with the model.
Machine learning models must also go through regulatory compliance before going into production. The predictions may differ and you need to review the history to make sure the machine learning model is behaving correctly. This is generally the case in the banking or finance industries, where the model’s predictions need to be quickly and easily tracked to demonstrate compliance to regulators and to be able to explain why the machine learning model made a particular price prediction. To be able to find a prediction made by a model in the past, tracking needs to be built in to easily find the model and the dataset it was trained on.
- Managing Similar Kinds of Models
Nowadays it is difficult to find a tool on the market that makes it possible to provide several models at the same time and show a comparison of how they behave on the same data, currently it is very complex, tedious and difficult to achieve. of a single machine learning model is manual, the effort to implement and compare multiple models is multiplied and almost impossible. The ability to see the monitoring metrics for multiple models in your production data is a powerful way to choose the right models. Good business decisions can be made which model is behaving correctly and faulty models can be removed quickly and easily.
Victory is Within Reach
Implementing machine learning models has its challenges, but it’s not impossible. In addition to the tips above, using a new model development lifecycle will streamline the model development and production process. The teams involved make effective decisions promptly and help the teams to minimize production risks.