[[MLOps Level 1]] represents a step up from the foundational practices observed at [[MLOps Level 0]], focusing on beginning the integration of automation and monitoring into the machine learning (ML) lifecycle. This level is characterized by initial attempts to streamline processes, introduce some degree of automation, and improve collaboration among teams. The aim at this stage is to reduce manual labor, improve consistency in model deployments, and start managing machine learning systems more systematically. ### Characteristics of MLOps Level 1: **1. Partial Automation:** - At Level 1, organizations begin to automate some aspects of the ML pipeline, particularly repetitive tasks such as data preprocessing and initial model training setups. This helps in reducing human error and increasing the efficiency of the workflows. **2. Basic CI/CD Integration:** - Continuous Integration (CI) processes may be implemented to automatically test and integrate new model code with existing repositories. Continuous Deployment (CD) might still be manual or semi-automated, focusing on ensuring that models can be smoothly transitioned from a development environment to production. **3. Version Control:** - More rigorous use of version control systems for code, models, and sometimes even data sets is established. This practice helps in tracking changes, maintaining consistency across environments, and aiding in reproducibility. **4. Basic Monitoring and Logging:** - Basic monitoring systems are put in place to track model performance and operational metrics. Logging mechanisms are used to record model predictions and behaviors, which are essential for debugging and understanding model performance over time. **5. Collaboration Tools:** - Tools that enhance collaboration across data scientists, ML engineers, and operations teams are adopted. This may include shared workspaces, integrated development environments (IDEs), and communication platforms that support a more unified approach to ML development and deployment. ### Common Practices at MLOps Level 1: **Model Validation and Testing:** - Automated testing frameworks are often adopted at this stage to validate model accuracy and robustness before deployment. These tests might include unit tests for individual components and integration tests to ensure that the entire pipeline works as expected. **Experiment Tracking:** - Tools to track experiments, manage parameters, and record results are used to compare different models systematically and make informed decisions about which models to promote to production. **Manual Model Deployment:** - While some automation may be introduced, model deployment might still largely be a manual process, involving final checks and approvals before models are updated or replaced in the production environment. **Limited Use of Advanced Features:** - Features like advanced hyperparameter tuning, automated model retraining, and sophisticated anomaly detection in model performance are generally not fully implemented at this level. ### Benefits of MLOps Level 1: - **Improved Efficiency:** Partial automation and better tooling reduce the time and effort required to move from data handling to model deployment. - **Enhanced Collaboration:** Integrated tools and processes foster better communication and understanding among teams, leading to more cohesive development efforts. - **Reduced Errors:** Automation of certain pipeline components reduces the chances of human error, particularly in data preprocessing and initial model training. ### Challenges at Level 1: - **Scalability Issues:** While improvements are made, processes at Level 1 might still not be scalable enough to handle very large datasets or complex model deployments efficiently. - **Partial Automation Gaps:** The incomplete integration of automation can lead to bottlenecks where manual processes remain, potentially slowing down deployments or leading to inconsistencies. Progressing beyond Level 1 typically involves addressing these challenges by further automating the deployment processes, enhancing monitoring capabilities, and integrating more robust data and model management practices. As organizations move to higher levels of MLOps maturity, they aim to achieve fully automated pipelines, sophisticated monitoring, and proactive management of the entire machine learning lifecycle. See [[MLOps Level 2]] # References ```dataview Table title as Title, authors as Authors where contains(subject, "MLOps") or contains(subject, "Level 1") sort title, authors, modified, desc ```