MLOps Maturity Model &#8211; A benchmark for effective ML models in production
MLOps maturity models are used to interpret the models that are taken up for production and analyze their dependency of reliability for the model in use and its ability for efficient scaling. Maturity models can be used as an evaluation metric or as a score to assess the MLOps model in production and its dynamic ability to adapt to the production environment. In this article, let us try to understand how the MLOps maturity models play a key role in the production environments and their ability to adapt to model production. Table of Contents Introduction to MLOpsWhat is meant by MLOps Maturity model?Operational hierarchy of MLOps maturity modelAdvantages of  MLOps maturity modelsSummary Introduction to MLOps MLOps abbreviates for Machine Learning Operations and is a core functionality of Machine Learning which focuses on streamlining the models developed into production. MLOps also facilitates the models in development to be easily assessed for various performance parameters and monitor them accordingly. As shown in the above image the entire MLOps approach can be broken down into three sets of operations. They are as follows. i) Machine Learning (ML) is the stage that involves data acquisition, data preparation, and initial modeling. This phase of MLOps aims to develop a model for the problem statement. ii) Development (DEV) is the stage of MLOps that focuses on testing the models for deployment and integrating the model into the pipelines. CI/CD pipeline creation and integration are taken up in this phase. iii) Operations (OPS) is the stage that monitors the machine after passing through the development phase. The model is pushed into production and continuously monitored for its performance and various parameters. Are you looking for a complete repository of Python libraries used in data science, check out here. Many organizations use MLOps for its operational efficiency and mainly as it ensures continuous monitoring of the models in production. MLOps also facilitates horizontal scalability of the model easily and efficient handling of pipelines is possible through MLOps. With this understanding of MLOps let us now try to look into what is MLOPS maturity models. What is meant by MLOps Maturity model? The MLOps maturity model in the MLOps cycle is the model used between the Development and Operation phases. The MLOps maturity models focus to handle the principles and mandatory practices of MLOps models efficiently. When we consider the CI/CD pipeline creation in the development phase the maturity models show their dynamic ability to adapt to the pipeline parameters. The maturity models also have the ability to seamlessly adapt to the changes in modeling and show continuous improvement in the creation and operation of a model that is in production. MLOps maturity also benefits the organization to identify the significant difference in the gap between the current model environment and the actual environment required for the model that is developed. So using the scores obtained from the maturity models the organizations can suitably improve the models and develop them in a manner suitable for the environment. So as the score of the maturity model increases the quality of the model developed increases along with increased efficiency and reliability of the model in production. Operational hierarchy of MLOps maturity model The MLOps maturity model operates entirely on 5 levels with different responsibilities and functionalities for each layer of operation. All the levels of MLOPS maturity models operate on 3 principles of the MLOps cycle. So let us try to understand the operational hierarchy of the MLOps maturity models and how at each level of operation the principle of MLOPS is handled. Level 0: No MLOps Let us try to understand how this level of maturity model handles the three main principles. The first principle is model creation where the data is gathered manually and suitably preprocessed. Once the data is efficient a dummy-like model is developed to evaluate certain predictions. The second principle is the model release where the model scoring script is manually scripted after certain experiments and it is mainly used to validate the data available. The third principle is application integration where the models are released manually and are heavily dependent on data scientist interpretations from the model developed. So on a whole, this level basically involves data gathering and model development, but monitoring of the model is not taken up. Level 1: DevOps no MLOps Let us try to understand this level with respect to three standard principles. The first principle is model creation where the pipelines will have the ability to gather the data automatically and the model parameters will be tracked and monitored a fewer number of times only. The second principle is the model release where the models in the pipeline are evaluated and the scores are scripted and passed on to the team of software engineers. The third principle is application integration where the models developed will be taken up for various testing like integration and unit testing and suitably evaluated according to software testing principles. Level 2: Automated Training Let us try to understand this level with respect to three standard principles. The first principle is model creation which is responsible for gathering data automatically from the pipeline. Here the models developed are monitored and validated continuously. The second principle is the model release where the models are released manually and the model’s parameters are evaluated continuously with certain test parameters. The third principle is application integration where the model developed will be taken up for various testing like integration and unit testing and suitably evaluated according to software testing principles. Level 3: Automated Model Deployment Let us try to understand this level with respect to three standard principles. The first principle is model creation which is responsible for effective modeling and managing the models created. Here both the training model code and the resulting model parameters are efficiently handled. The second principle is the model release where the model’s performance is scripted based on the outcomes of tests and is entirely managed by the CI/CD pipelines. The third principle is application integration where the models are monitored and deployed in the form of an application and the model deployed will be monitored on the basis of software testing principles. Level 4: Full MLOps Automated retraining Let us try to understand this level with respect to three standard principles. The first principle is model creation which is responsible for triggering the model for retraining with respect to the feedback received after continuously monitoring the model that is present in production. The second principle is the model release where the model metrics are scripted after monitoring in the pipeline is received and the model is retrained accordingly. The third principle is application integration where the model is only evaluated and monitored continuously through unit tests and software testing principles. So this is how the MLOps maturity model operates on 5 different layers and tries to mature the models developed into production sensibly. MLOps maturity models basically yield certain parameters after processing through the 5 layers and those parameters can be considered as feedback of the model present in the production. Thereafter according to the feedback received the models can be retrained accordingly to obtain optimal performance from the model and this process is continuously monitored to ensure model reliability in production. Advantages of MLOps maturity models As mentioned earlier in this article MLOps maturity models focus on obtaining an efficient and optimal model in production that continuously is improved, integrated and developed in the lifecycle to obtain results as required in the environment. Now let us look into some of the major advantages of MLOps maturity models. i) Optimal decision-making can be ensured by using the MLOps maturity models that are continuously monitored and retrained according to the feedback and environments. So from the models in production optimal decision-making is possible. ii) Ensured agility which enables the maturity models to be deployed on various platforms and across models to validate the maturity parameters and obtain an optimal model that can be used in production. iii) Better predictive analysis can be expected from the maturity models as the maturity models are continuously monitored and retrained according to the environment requirements. So better and right predictions can be obtained by the maturity models. iv) Continuous monitoring of maturity models help us have a sensible and completely reliable model in production. So continuous operation and continued reliability of the model in production are assured through maturity models Summary MLOps mainly focus on streamlining the model into production and the maturity models of MLOps facilitate reliable and stable models to be made available in production. Maturity models provide certain parameters that can be validated with the models developed and used accordingly to retrain the model in production and obtain a more robust model. Maturity modeling also benefits in obtaining efficient models with long-term reliability as it has the ability to dynamically adapt according to the environment requirements. References Microsoft white paper on maturity models
The MLOps maturity model is a key component of the MLOps.This article aims to explain the MLOps maturity model and its importance in the production environment.
MLOps maturity modelling helps us to obtain robust and reliable models that can be used in production. The maturity models is continuously monitored in the cycle.
["Machine Learning Algorithms", "MLOps"]
["Machine Learning Algorithms", "data science", "machine learning", "AI", "ML", "MLOps", "CI/CD", "Scala", "Python", "Aim", "R"]
["AI", "machine learning", "ML", "data science", "MLOps", "Aim", "Python", "R", "Scala", "CI/CD"]
Darshan is a Master's degree holder in Data Science and Machine Learning and an everyday learner of the latest trends in Data Science and Machine Learning. He is always interested to learn new things with keen interest and implementing the same and curating rich content for Data Science, Machine Learning,NLP and AI