## 4 MLOps Pipeline Design
### MLOps Pipeline Architecture  
Model deployment, which involves running, scaling, maintaining, and integrating models into applications, remains a significant challenge across different industries. This process is crucial to transforming ML models into operational solutions that can drive real-world applications and outcomes. Successful deployment of models requires a robust infrastructure that can handle the computational demands of machine learning algorithms, as well as continuous monitoring and updates to ensure models remain effective over time. The MLOps pipeline architecture is a structure that automates the development, testing, deployment, and monitoring processes of machine learning projects. This architecture includes basic components such as data preparation, model training, model evaluation, model deployment, and continuous monitoring of the model's performance. Similarly, DevOps represents a significant transformation in software development over the last decade. The DevOps approach aims to deliver software products and features faster and more reliably. The DevOps process generally includes planning, development, testing, packaging, deployment, configuration, and monitoring steps. The DevOps architecture is given in Figure 5.  
When the architecture in Figure 5 is examined, it is seen that it represents a constantly renewed and active working process. The depicted architecture in Figure 5 epitomizes the structured progression of the DevOps pipeline, elucidating the systematic sequence of operations from conception to deployment. It commences with the 'Plan' phase, wherein strategic initiatives are formulated, encompassing requirement elicitation and delineation of developmental trajectory. This is succeeded by the 'Create' phase, where the amalgamation of coding practices comes to fruition through collaborative and iterative development. After the creation phase is 'Verify', a critical juncture wherein the developed features undergo rigorous automated validation to ensure adherence to predefined quality benchmarks and functional integrity. Upon successful verification, the 'Package' phase ensues, entailing the encapsulation of validated features into deployable entities, primed for integration into the user environment. The 'Release' phase marks the deployment of the application into the production milieu, orchestrated to minimize manual intervention and foster seamless operational continuity. This is closely followed by the 'Configure' phase, which involves the meticulous tuning of the application within its deployment ecosystem to ensure optimal performance parameters are met. Crucially, the 'Monitor' phase encapsulates the ongoing  
Figure 5: DevOps pipeline architecture. surveillance of application performance post-deployment, serving as a feedback conduit to the 'Plan' phase, thereby instituting a cyclical paradigm of continuous enhancement and evolution. This feedback mechanism is pivotal, facilitating empirical insights that drive the iterative refinement of the subsequent developmental iterations. Different technologies can be used at each step of the DevOps architecture. DevOps can use tools like JIRA for planning, Git and IDEs for code generation, Docker for packaging, Jenkins for deployment, and Prometheus/Grafana for monitoring. Flexibility here may vary depending on the developer's current hardware requirements. Just as DevOps has introduced new approaches to software development, MLOps has brought a similar transformation to ML operations. Although progress has been made in integrating ML into different applications and technological products, a solid structure is needed to ensure fast and reliable delivery of ML products. Therefore, ML needs to be operationalized. Figure 6 shows the MLOps architecture inspired by DevOps. The adaptation of DevOps processes to the MLOps process aims to meet the unique needs of ML using open-source technologies. This process covers the life cycle stages of ML models such as data preparation, model training, model versioning, deployment, and monitoring.