A successful machine learning (ML) project is more than just developing and implementing models; machine learning is the entire life cycle of data; it consists of complex steps and various skills required to achieve actual results and create business value. The complexity of the machine learning life cycle is one reason why best practices and fully integrated tools are still in their infancy today.
Other reasons include lack of skills, poor model scalability, and lack of automation. Data scientists usually come from all walks of life and do not always follow DevOps and coding best practices. In addition, data scientists and engineers tend to work in silos, which leads to poor collaboration between teams. That’s why they can make use of certain performance metrics. Combine machine learning, DevOps, and data engineering skills to manage the entire machine learning life cycle, and use mlops best practices to achieve business value in line with the company’s data strategy.
The automation level, model, and code of the machine learning data pipeline determine the maturity of the machine learning process. As you age, the speed at which you learn new models will increase. The purpose of the MLOps best practices to automate the implementation of machine learning models in the primary software system or as a service component, that is, to automate all stages of the machine learning workflow without manual intervention. Calendar events, messaging, monitoring events and data changes, model training code, and application code. Automated testing helps to find problems quickly in the early stages. This allows you to quickly correct mistakes and learns from them.
What are data scientists mainly doing today?
Today, most machine learning attempts to implement machine learning models in a production environment look like this: As a data scientist, you start with machine learning use cases and business goals. In this use case, we first collect and check essential data from various data sources to understand and evaluate its quality. Once we have a feel for the data, we will design and develop some features that we find interesting for our problem. Then enter the modeling phase and start doing some experiments. During this phase, we will manually perform different stages of the investigation regularly. For each experiment, we will prepare some data, develop and test some functions. Train and adjust hyperparameters in each model or model architecture that we find particularly promising.
MLOps Success Framework
Because MLOps is a new field, it isn’t easy to understand its meaning and requirements. One of the biggest challenges in implementing MLOps best practices is duplicating DevOps practices in ML. This is mainly due to a fundamental difference:
DevOps deals with code, while ML deals with code and data. When it comes to data, unpredictability is always a significant issue. Since code and data evolve independently and parallel, the resulting islands make machine learning production models slower and often incompatible. In addition, simple CI/CD methods may not be implemented because large amounts of data that are difficult to track and process cannot be reproduced. The CI / CD / TC (continuous learning) method must be selected in production.
Features, model architecture, and search for hyperparameters are constantly evolving. The mlops best practices always strive to provide the best system based on the current state of the art and continuously changing data patterns. On the one hand, this means that you should always be aware of the latest ideas and starting points. This also means experimenting with these ideas to see if they can improve the performance of your machine learning system.