What is MLOps Maturity?
MLOps Maturity refers to the level of readiness and efficiency of Data Science, Machine Learning, and Artificial Intelligence teams in managing the entire lifecycle of machine learning models. It involves the integration of people, processes, and technology to streamline the development, deployment, and maintenance of ML models. Achieving MLOps Maturity is crucial for teams to ensure timely success of their ML projects and deliver value to their business stakeholders by making them more engaged and informed regarding ML activities across the organisation. Here are some crucial steps and practices for guidance on increasing your MLOps maturity and actually making the most out of your existing team hires and engaging them towards the right action area.
Why is MLOps Maturity Needed?
DS/ML/AI teams face several challenges in managing the end-to-end lifecycle of ML models, such as data management, model selection, training, testing, deployment, monitoring, and maintenance. These challenges can lead to delays, errors, and inconsistencies in the ML pipeline, resulting in reduced accuracy and performance of the models. MLOps Maturity helps teams overcome these challenges by providing a structured approach to managing the ML lifecycle and ensuring consistency, repeatability, and scalability of the ML pipeline. Achieving MLOps Maturity also brings several benefits, such as faster time-to-market, reduced costs, improved model accuracy, and better collaboration among team members.
Realizing MLOps maturity becomes a crucial step for almost all companies deploying any ML model in production. Although MLOps maturity as a whole should always be considered as an extension to your DevOps maturity level, however it is highly recommended to not just go for traditional DevOps KPIs that are also common to MLOps KPIs in terms of general software development practices.
Here are some key differences between MLOps KPIs and DevOps KPIs:
Model Performance Metrics:
MLOps KPIs often include metrics related to the performance of machine learning models, such as accuracy, precision, recall, F1-score, or area under the receiver operating characteristic curve (AUC-ROC). These metrics assess the model’s ability to make accurate predictions or classifications. DevOps KPIs, on the other hand, typically focus on software quality, deployment frequency, and mean time to recovery (MTTR).
Data Quality and Data Drift:
MLOps KPIs may include metrics related to data quality and data drift. Data quality KPIs assess the completeness, correctness, and consistency of the training and inference data used in machine learning models. Data drift KPIs monitor changes in the statistical properties of the data over time, ensuring that models remain accurate and reliable. DevOps KPIs typically do not explicitly measure data quality or drift.
Model Versioning and Governance:
MLOps KPIs often consider the management of model versions, including metrics related to version control, model lineage, and model governance. These KPIs help track the evolution of models, maintain reproducibility, and ensure compliance with regulatory requirements. In contrast, DevOps KPIs typically focus on version control and deployment of software applications.
Monitoring and Alerting:
MLOps KPIs include metrics related to model monitoring and alerting systems. These KPIs assess the availability and responsiveness of monitoring systems that track model performance and detect anomalies or degradation. DevOps KPIs usually focus on infrastructure monitoring, service availability, and response time.
Business Metrics:
MLOps KPIs may include business-related metrics that tie machine learning model performance to desired outcomes, such as revenue, customer satisfaction, or cost reduction. These metrics help evaluate the impact of machine learning models on business objectives. DevOps KPIs typically focus more on technical metrics and may not directly measure business outcomes.
Hence MLops Maturity gives a totally different view across the organization leading to more effective engagement of DS/ML/AI Teams.
How to Achieve MLOps Maturity? 5 Best Practices:
Collaboration and Communication:
The most crucial skill for any successful ML/DS/AI team member is effective communication of results with the development team and especially communicating with business stakeholders. MLOps requires close collaboration and communication between data scientists, developers, operations, and business stakeholders. Teams should establish clear roles and responsibilities, define workflows, and use tools that promote collaboration and communication, such as project management software, version control systems, and chat apps. For example, Netflix’s MLOps team uses Slack and GitHub to collaborate and share code across teams.
MLOps KPIs monitoring dashboards can automate decision making for AI/ML Teams to focus on models which need attention. And same dashboards can serve the business stakeholders prioritising their attention area and collaborating with AI/ML teams directly. MLOps capabilities and maturity level greatly enhance the communication capabilities of team members specially when sharing their output with business stakeholders and allows them to answer key questions from the business requirement side.
AI/ML teams are able to see their impact and contribute the most out of their knowledge and get a sense of what should be learned or built next. They should also establish best practices for sharing and collaborating, such as code reviews, documentation, and knowledge sharing sessions.
Standardisation and Automation :
MLOps involves several repetitive and time-consuming tasks, such as data preprocessing, model training, and deployment. Teams should automate these tasks using tools such as Docker, Kubernetes, and Jenkins, to reduce errors and improve efficiency. They should also establish standard processes and best practices for data management, model selection, and testing, to ensure consistency and repeatability. For example, Airbnb’s MLOps team uses a standardised machine learning pipeline, called Bighead, to automate the ML lifecycle.
Golden rule – Standardised workflows are easier to automate and automated workflows are easier to standardise. Bringing this into practice can be done by ensuring standardisation of all parts of ML pipeline and immediately automating that pipeline with a clear KPI for calculating how much part needs developer intervention and how much is automated as a skeleton backbone. Which part needs just the config management and which part needs developer time to code. Repeating this activity every release cycle can greatly enhance your MLOps maturity level and enables every team member for Ops.
Continuous Integration and Deployment:
MLOps requires continuous integration and deployment of ML models, to ensure that the models are always up-to-date and performant. Teams should use tools such as GitLab, CircleCI, and AWS CodePipeline, to automate the build, test, and deployment of ML models. They should also establish a feedback loop that captures performance metrics and user feedback, to improve the models over time. For example, Spotify’s MLOps team uses a continuous delivery pipeline, called Luigi, to automate the deployment of ML models.
MLOps CI/CD is a completely different process from traditional software development lifecycle. It involves continuous training that depends on monitoring model KPIs. To enhance the efficiency of AI/ML teams, automating the continuous training by triggering model retraining when it drops below a certain threshold is crucial.
The use of GPU-based hardware for advanced ML/AI models poses a challenge for MLOps in terms of CI/CD. It is crucial to ensure that the automation tools like Jenkins and Terraform are configured correctly to avoid launching the model on a compute without GPU support, resulting in high inference time and huge costs due to large data movement over the network. It is essential to load the model file, which can be several GBs in size, only once on the right compute with GPU support enabled to reduce server costs. Failure to follow these crucial steps can result in significant server billing without achieving the desired outcome. Contact us for assistance if needed.
Monitoring and Logging:
MLOps requires continuous monitoring and logging of ML models, to detect and fix errors and anomalies. Teams should use tools such as Prometheus, Grafana, and ELK Stack, to monitor the performance and health of ML models in real-time. They should also establish a logging system that captures all events and actions in the ML pipeline, to facilitate debugging and auditing. For example, Uber’s MLOps team uses a monitoring and logging system, called Michelangelo, to track the performance and usage of ML models.
Effective MLOps monitoring and logging is a critical component for achieving advanced automation levels. It enables conflict resolution between teams and vendors claiming higher accuracy for their models. Advanced deployment strategies, such as A/B deployment or redirecting serving traffic to the model with the highest accuracy using Thompson sampling, must be incorporated for showcasing MLOps maturity level.
Industry examples of MLOps monitoring and logging tools include:
- TensorBoard: A tool for visualizing TensorFlow models, TensorBoard provides real-time monitoring of training and validation metrics.
- Prometheus: An open-source monitoring system, Prometheus is designed for collecting and storing time-series data. It is widely used for monitoring Kubernetes clusters and other cloud-native applications. Prometheus metrics on Grafana is a popular choice for monitoring dashboards.
- Datadog: A cloud-based monitoring and analytics platform, Datadog provides real-time visibility into the performance of applications, infrastructure, and logs. It supports a wide range of integrations, including popular ML frameworks like TensorFlow and PyTorch.
- ELK Stack: A popular open-source logging stack, ELK (Elasticsearch, Logstash, Kibana) is widely used for collecting, indexing, and analyzing logs from various sources. It provides real-time insights into the health and performance of distributed systems.
As mentioned above that ML/AI models mostly requires GPU or accelerated hardware computation. Integrating GPU monitoring metrices into Grafana dashboards like GPU memory utilization for optimal batch size, GPU clock speed etc becomes crucial and important for ML/AI teams and hence justifying the cost of running, training the model.
Security and Compliance:
MLOps requires strict security and compliance measures, to protect sensitive data and ensure regulatory compliance. Teams should establish security policies and procedures, such as access control, encryption, and vulnerability scanning, to prevent unauthorized access and data breaches. They should also ensure compliance with relevant regulations, such as GDPR and HIPAA, by implementing appropriate safeguards and controls. For example, Capital One’s MLOps team uses an automated security and compliance framework, called Cloud Custodian, to enforce security and compliance policies across their ML infrastructure.
Often ignored yet the most important part which truly defines the MLOps maturity level. MLOps can automate security and compliance checks as a part of CI/CD itself and hence incorporating DevSecOps practices in the workflow. This has the true contribution in calculating true time-to-market of any AI Product and services. Example – A company trying to serve its fintech AI Model reduced time to market by 2 months by automating financial compliances needed for serving such APIs.
MLOps maturity includes services and automation to monitor data corruptions, unauthorised data movement leading to bad model retraining or compliance violations. Example – A hospital needs AI services but cannot allow patient data to be moved out of the Hospitals servers. Bringing in such capabilities becomes crucial for businesses to expand their target client base.
How MLOps Maturity become the bridge between team and business stakeholders?
Sharing and collaborating are essential for achieving and maintaining MLOps Maturity. They should also collaborate on projects and initiatives, to leverage each other’s strengths and resources. MLOps capabilities becomes key informant for business to which side to focus on, what capabilities to developed more for making the most out of their ML models, what skill sets for which they need to hire more, how existing resources are currently being utilised, which includes both the human resources and compute resources, both of which acting as a recurring cost to the company.
Team Managers should continue incorporating success KPIs for establishing clear communication and direction from business stakeholders. This includes but not limited to KPIs like number of businesses served, Model API hits frequency over a time period, Which ML project is consuming how many team members. Which ML Project is costing how much in cloud billing.
Capturing and integrating these KPIs in the workflow help in achieving Readiness of the overall team and enable answering the Crucial questions from business stakeholders like how much time to market? What features or capabilities to incorporate next in the workflow etc.
How to Continue with MLOps Maturity?
Achieving MLOps Maturity is an ongoing process that requires continuous improvement. Teams should regularly assess their MLOps practices and identify areas for improvement, such as performance, efficiency, and security. They should also stay up-to-date with the latest technologies and trends in the ML industry, such as AutoML, Federated Learning, and Explainable AI, and evaluate their potential impact on their MLOps practices. Finally, they should establish a culture of continuous learning and innovation, where team members are encouraged to experiment, learn from their mistakes, and share their knowledge with others.
To achieve MLOps maturity, it is necessary to make domain-specific improvements in the workflow by focusing on the appropriate tool rather than just following the latest trend. The team should prioritize automating all manual tasks involved in the last release cycle and identify areas of the ML pipeline for every release cycle. This approach will eventually lead to long-term benefits by allowing the team to focus on innovation in new areas instead of being stuck with outdated processes. Microsoft Azure has a dedicated guide to model your MLOps maturity level.
Conclusion:
MLOps Maturity is critical for DS/ML/AI teams to manage the end-to-end lifecycle of ML models efficiently and effectively. By following the best practices of collaboration, automation, standardization, continuous integration and deployment, monitoring and logging, and security and compliance, teams can achieve MLOps Maturity and reap the benefits of faster time-to-market, reduced costs, improved accuracy, and better collaboration. To maintain MLOps Maturity, teams should continue to assess their practices, stay up-to-date with industry trends, and foster a culture of continuous learning and innovation. Contact Us for your queries.