Machine learning operations
This article describes three Azure architectures for machine learning operations that have end-to-end continuous integration and continuous delivery (CI/CD) pipelines and retraining pipelines. The architectures are for these AI applications:
- Classical machine learning
- Computer vision (CV)
- Natural language processing
These architectures are the product of the MLOps v2 project. They incorporate best practices that solution architects identified in the process of developing various machine learning solutions. The result is deployable, repeatable, and maintainable patterns. All three architectures use the Azure Machine Learning service.
For an implementation with sample deployment templates for MLOps v2, see Azure MLOps v2 solution accelerator.
Potential use cases
Classical machine learning: Time-series forecasting, regression, and classification on tabular structured data are the most common use cases in this category. Examples include:
Binary and multi-label classification.
Linear, polynomial, ridge, lasso, quantile, and Bayesian regression.
ARIMA, autoregressive, SARIMA, VAR, SES, LSTM.
CV: The MLOps framework in this article focuses mostly on the CV use cases of segmentation and image classification.
Natural language processing: You can use this MLOps framework to implement:
Named entity recognition:
Text classification
Text generation
Sentiment analysis
Translation
Question answering
Summarization
Sentence detection
Language detection
Part-of-speech tagging
AI simulations, deep reinforcement learning, and other forms of AI aren't described in this article.
Architecture
The MLOps v2 architectural pattern has four main modular components, or phases, of the MLOps lifecycle:
- Data estate
- Administration and setup
- Model development, or the inner loop phase
- Model deployment, or the outer loop phase
The preceding components, the connections between them, and the typical personas involved are standard across all MLOps v2 scenario architectures. Variations in the details of each component depend on the scenario.
The base architecture for MLOps v2 for Machine Learning is the classical machine learning scenario for tabular data. The CV and NLP architectures build on and modify this base architecture.
MLOps v2 covers the following architectures that are described in this article:
- Classical machine learning architecture
- Machine Learning CV architecture
- Machine Learning natural language processing architecture
Classical machine learning architecture
Download a Visio file of this architecture.
Workflow for the classical machine learning architecture
Data estate
This component illustrates the data estate of the organization and potential data sources and targets for a data science project. Data engineers are the primary owners of this component of the MLOps v2 lifecycle. The Azure data platforms in this diagram aren't exhaustive or prescriptive. A green check mark indicates the data sources and targets that represent recommended best practices that are based on the customer use case.
Administration and setup
This component is the first step in the MLOps v2 accelerator deployment. It consists of all tasks related to the creation and management of resources and roles that are associated with the project. For example, the infrastructure team might:
- Create project source code repositories.
- Use Bicep or Terraform to create Machine Learning workspaces.
- Create or modify datasets and compute resources for model development and deployment.
- Define project team users, their roles, and access controls to other resources.
- Create CI/CD pipelines.
- Create monitoring components to collect and create alerts for model and infrastructure metrics.
The primary persona associated with this phase is the infrastructure team, but an organization might also have data engineers, machine learning engineers, or data scientists.
Model development (inner loop phase)
The inner loop phase consists of an iterative data science workflow that acts within a dedicated and secure Machine Learning workspace. The preceding diagram shows a typical workflow. The process starts with data ingestion, moves through exploratory data analysis, experimentation, model development and evaluation, and then registers a model for production use. This modular component as implemented in the MLOps v2 accelerator is agnostic and adaptable to the process that your data science team uses to develop models.
Personas associated with this phase include data scientists and machine learning engineers.
Machine Learning registries
After the data science team develops a model that they can deploy to production, they register the model in the Machine Learning workspace registry. CI pipelines that are triggered, either automatically by model registration or by gated human-in-the-loop approval, promote the model and any other model dependencies to the model deployment phase.
Personas associated with this stage are typically machine learning engineers.
Model deployment (outer loop phase)
The model deployment, or outer loop phase, consists of preproduction staging and testing, production deployment, and monitoring of the model, data, and infrastructure. When the model meets the criteria of the organization and use case, CD pipelines promote the model and related assets through production, monitoring, and potential retraining.
Personas associated with this phase are primarily machine learning engineers.
Staging and test
The staging and test phase varies according to customer practices. This phase typically includes operations such as retraining and testing the model candidate on production data, test deployments for endpoint performance, data quality checks, unit testing, and responsible AI checks for model and data bias. This phase takes place in one or more dedicated and secure Machine Learning workspaces.
Production deployment
After a model passes the staging and test phase, machine learning engineers can use human-in-the-loop gated approval to promote it to production. Model deployment options include a managed batch endpoint for batch scenarios or either a managed online endpoint or Kubernetes deployment that uses Azure Arc for online, near real-time scenarios. Production typically takes place in one or more dedicated and secure Machine Learning workspaces.
Monitoring
Machine learning engineers monitor components in staging, testing, and production to collect metrics related to changes in performance of the model, data, and infrastructure. They can use those metrics to take action. Model and data monitoring can include checking for model and data drift, model performance on new data, and responsible AI problems. Infrastructure monitoring might identify slow endpoint response, inadequate compute capacity, or network problems.
Data and model monitoring: events and actions
Based on model and data criteria, such as metric thresholds or schedules, automated triggers and notifications can implement appropriate actions to take. For example, a trigger might retrain a model to use new production data and then loopback the model to staging and testing for a preproduction evaluation. Or a model or data problem might trigger an action that requires a loopback to the model development phase where data scientists can investigate the problem and potentially develop a new model.
Infrastructure monitoring: events and actions
Automated triggers and notifications can implement appropriate actions to take based on infrastructure criteria, such as an endpoint response lag or insufficient compute for the deployment. Automatic triggers and notifications might trigger a loopback to the setup and administration phase where the infrastructure team can investigate the problem and potentially reconfigure the compute and network resources.
Machine Learning CV architecture
Download a Visio file of this architecture.
Workflow for the CV architecture
The Machine Learning CV architecture is based on the classical machine learning architecture, but it has modifications that are specific to supervised CV scenarios.
Data estate
This component demonstrates the data estate of the organization and potential data sources and targets for a data science project. Data engineers are the primary owners of this component in the MLOps v2 lifecycle. The Azure data platforms in this diagram aren't exhaustive or prescriptive. Images for CV scenarios can come from various data sources. For efficiency when developing and deploying CV models with Machine Learning, we recommend Azure Blob Storage and Azure Data Lake Storage.
Administration and setup
This component is the first step in the MLOps v2 accelerator deployment. It consists of all tasks related to the creation and management of resources and roles associated with the project. For CV scenarios, administration and setup of the MLOps v2 environment is largely the same as for classical machine learning but includes an extra step. The infrastructure team uses the labeling feature of Machine Learning or another tool to create image labeling and annotation projects.
Model development (inner loop phase)
The inner loop phase consists of an iterative data science workflow performed within a dedicated and secure Machine Learning workspace. The primary difference between this workflow and the classical machine learning scenario is that image labeling and annotation is a key component of this development loop.
Machine Learning registries
After the data science team develops a model that they can deploy to production, they register the model in the Machine Learning workspace registry. CI pipelines that are triggered automatically by model registration or by gated human-in-the-loop approval promote the model and any other model dependencies to the model deployment phase.
Model deployment (outer loop phase)
The model deployment or outer loop phase consists of preproduction staging and testing, production deployment, and monitoring of the model, data, and infrastructure. When the model meets the criteria of the organization and use case, CD pipelines promote the model and related assets through production, monitoring, and potential retraining.
Staging and test
The staging and test phase varies according to customer practices. This phase typically includes operations such as test deployments for endpoint performance, data quality checks, unit testing, and responsible AI checks for model and data bias. For CV scenarios, machine learning engineers don't need to retrain the model candidate on production data because of resource and time constraints. The data science team can instead use production data for model development. The candidate model registered from the development loop is evaluated for production. This phase takes place in one or more dedicated and secure Machine Learning workspaces.
Production deployment
After a model passes the staging and test phase, machine learning engineers can use human-in-the-loop gated approval to promote it to production. Model deployment options include a managed batch endpoint for batch scenarios or either a managed online endpoint or Kubernetes deployment that uses Azure Arc for online, near real-time scenarios. Production typically takes place in one or more dedicated and secure Machine Learning workspaces.
Monitoring
Machine learning engineers monitor components in staging, testing, and production to collect metrics related to changes in performance of the model, data, and infrastructure. They can use those metrics to take action. Model and data monitoring can include checking for model performance on new images. Infrastructure monitoring might identify slow endpoint response, inadequate compute capacity, or network problems.
Data and model monitoring: events and actions
The data and model monitoring and event and action phases of MLOps for natural language processing are the key differences from classical machine learning. Automated retraining is typically not done in CV scenarios when model performance degradation on new images is detected. In this case, a human-in-the-loop process is necessary to review and annotate new text data for the model that performs poorly. The next action often goes back to the model development loop to update the model with the new images.
Infrastructure monitoring: events and actions
Automated triggers and notifications can implement appropriate actions to take based on infrastructure criteria, such as an endpoint response lag or insufficient compute for the deployment. Automatic triggers and notifications might trigger a loopback to the setup and administration phase where the infrastructure team can investigate the problem and potentially reconfigure environment, compute, and network resources.
Machine Learning natural language processing architecture
Download a Visio file of this architecture.
Workflow for the natural language processing architecture
The Machine Learning natural language processing architecture is based on the classical machine learning architecture, but it has some modifications that are specific to NLP scenarios.
Data estate
This component demonstrates the organization data estate and potential data sources and targets for a data science project. Data engineers are the primary owners of this component in the MLOps v2 lifecycle. The Azure data platforms in this diagram aren't exhaustive or prescriptive. A green check mark indicates sources and targets that represent recommended best practices that are based on the customer use case.
Administration and setup
This component is the first step in the MLOps v2 accelerator deployment. It consists of all tasks related to the creation and management of resources and roles associated with the project. For natural language processing scenarios, administration and setup of the MLOps v2 environment is largely the same as for classical machine learning, but with an extra step: create image labeling and annotation projects by using the labeling feature of Machine Learning or another tool.
Model development (inner loop phase)
The inner loop phase consists of an iterative data science workflow performed within a dedicated and secure Machine Learning workspace. The typical NLP model development loop differs from the classical machine learning scenario in that the typical development steps for this scenario include annotators for sentences and tokenization, normalization, and embeddings for text data.
Machine Learning registries
After the data science team develops a model that they can deploy to production, they register the model in the Machine Learning workspace registry. CI pipelines that are triggered automatically by model registration or by gated human-in-the-loop approval promote the model and any other model dependencies to the model deployment phase.
Model deployment (outer loop phase)
The model deployment or outer loop phase consists of preproduction staging and testing, production deployment, and monitoring of the model, data, and infrastructure. When the model meets the criteria of the organization and use case, CD pipelines promote the model and related assets through production, monitoring, and potential retraining.
Staging and test
The staging and test phase varies according to customer practices. This phase typically includes operations such as retraining and testing the model candidate on production data, test deployments for endpoint performance, data quality checks, unit testing, and responsible AI checks for model and data bias. This phase takes place in one or more dedicated and secure Machine Learning workspaces.
Production deployment
After a model passes the staging and test phase, machine learning engineers can use human-in-the-loop gated approval to promote it to production. Model deployment options include a managed batch endpoint for batch scenarios or either a managed online endpoint or Kubernetes deployment that uses Azure Arc for online, near real-time scenarios. Production typically takes place in one or more dedicated and secure Machine Learning workspaces.
Monitoring
Machine learning engineers monitor components in staging, testing, and production to collect metrics related to changes in performance of the model, data, and infrastructure. They can use those metrics to take action. Model and data monitoring can include checking for model and data drift, model performance on new text data, and responsible AI problems. Infrastructure monitoring might identify problems, such as slow endpoint response, inadequate compute capacity, and network problems.
Data and model monitoring: events and actions
As with the CV architecture, the data and model monitoring and event and action phases of MLOps for natural language processing are the key differences from classical machine learning. Automated retraining isn't typically done in natural language processing scenarios when model performance degradation on new text is detected. In this case, a human-in-the-loop process is necessary to review and annotate new text data for the model that performs poorly. Often the next action is to go back to the model development loop to update the model with the new text data.
Infrastructure monitoring: events and actions
Automated triggers and notifications can implement appropriate actions to take based on infrastructure criteria, such as an endpoint response lag or insufficient compute for the deployment. Automatic triggers and notifications might trigger a loopback to the setup and administration phase where the infrastructure team can investigate the problem and potentially reconfigure compute and network resources.
Components
Machine Learning is a cloud service that you can use to train, score, deploy, and manage machine learning models at scale.
Azure Pipelines is a build-and-test system that's based on Azure DevOps and is used for build and release pipelines. Azure Pipelines splits these pipelines into logical steps called tasks.
GitHub is a code-hosting platform for version control, collaboration, and CI/CD workflows.
Azure Arc is a platform that uses Azure Resource Manager to manage Azure resources and on-premises resources. The resources can include virtual machines, Kubernetes clusters, and databases.
Kubernetes is an open-source system that you can use to automate the deployment, scaling, and management of containerized applications.
Azure Data Lake Storage is a Hadoop-compatible file system. It has an integrated hierarchical namespace and the massive scale and economy of Blob Storage.
Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics.
Azure Event Hubs is a service that ingests data streams that client applications generate. It then ingests and stores streaming data, which preserves the sequence of events received. Customers can connect to the hub endpoints to retrieve messages for processing. This architecture uses Data Lake Storage integration.
Other considerations
The preceding MLOps v2 architectural pattern has several critical components, including role-based access control (RBAC) that aligns with business stakeholders, efficient package management, and robust monitoring mechanisms. These components collectively contribute to the successful implementation and management of machine learning workflows.
Persona-based RBAC
It's crucial that you manage access to machine learning data and resources. RBAC provides a robust framework to help you manage who can perform specific actions and access specific areas within your solution. Design your identity segmentation strategy to align with the lifecycle of machine learning models in Machine Learning and the personas included in the process. Each persona has a specific set of responsibilities that are reflected in their RBAC roles and group membership.
Example personas
To support appropriate segmentation in a machine learning workload, consider the following common personas that inform the identity-based RBAC group design.
Data scientist and machine learning engineer
Data scientists and machine learning engineers perform various machine learning and data science activities across the software development life cycle of a project. Their duties include exploratory data analysis and data preprocessing. Data scientists and machine learning engineers are responsible for training, evaluating, and deploying models. These roles' responsibilities also include break-fix activities for machine learning models, packages, and data. These duties are out of scope for the platform's technical support team.
Type: Person
Project specific: Yes
Data analyst
Data analysts provide the necessary input for data science activities, such as running SQL queries for business intelligence. This role's responsibilities include working with data, performing data analysis, and supporting model development and model deployment.
Type: Person
Project specific: Yes
Model tester
Model testers conduct tests in testing and staging environments. This role provides functional segregation from the CI/CD processes.
Type: Person
Project specific: Yes
Business stakeholders
Business stakeholders are associated with the project, such as a marketing manager.
Type: Person
Project specific: Yes
Project lead or data science lead
The data science lead is a project administration role for the Machine Learning workspace. This role also does break-fix activities for the machine learning models and packages.
Type: Person
Project specific: Yes
Project or product owner (Business owner)
Business stakeholders are responsible for the Machine Learning workspace according to data ownership.
Type: Person
Project specific: Yes
Platform technical support
Platform technical support is the technical support staff responsible for break-fix activities across the platform. This role covers infrastructure or service but not the machine learning models, packages, or data. These components remain under the data scientist or machine learning engineer role and are the project lead's responsibility.
Type: Person
Project specific: No
Model end user
Model end users are the end consumers of the machine learning model.
Type: Person or Process
Project specific: Yes
CI/CD processes
CI/CD processes release or roll back changes across platform environments.
Type: Process
Project specific: No
Machine Learning workspace
Machine Learning workspaces use managed identities to interact with other parts of Azure. This persona represents the various services that make up a Machine Learning implementation. These services interact with other parts of the platform, such as the development workspace that connects with the development data store.
Type: Process
Project specific: No
Monitoring processes
Monitoring processes are compute processes that monitor and alert based on platform activities.
Type: Process
Project specific: No
Data governance processes
Data governance processes scan the machine learning project and data stores for data governance.
Type: Process
Project specific: No
Microsoft Entra group membership
When you implement RBAC, Microsoft Entra groups provide a flexible and scalable way to manage access permissions across different personas. You can use Microsoft Entra groups to manage users that need the same access and permissions to resources, such as potentially restricted apps and services. Instead of adding special permissions to individual users, you create a group that applies the special permissions to every member of that group.
In this architectural pattern, you can couple these groups with a Machine Learning workspace setup, such as a project, team, or department. You can associate users with specific groups to define fine-grained access policies. The policies grant or restrict permissions to various Machine Learning workspaces based on job functions, project requirements, or other criteria. For example, you can have a group that grants all data scientists access to a development workspace for a specific use case.
Identity RBAC
Consider how you can use the following built-in Azure RBAC roles to apply RBAC to production and preproduction environments. For the architecture in this article, the production environments include staging, testing, and production environments. The preproduction environments include development environments. The following RBAC roles are based on the personas described earlier in this article.
Standard roles
- R = Reader
- C = Contributor
- O = Owner
Component specific roles
AcrPush = Azure Container Registry Push
LAR = Log Analytics Reader
MR = Monitoring Reader
KVA = Key Vault Administrator
KVR = Key Vault Reader
These Azure RBAC role abbreviations correspond with the following tables.
Production environment
| Persona | Machine Learning workspace | Azure Key Vault | Container Registry | Azure Storage account | Azure DevOps | Azure Artifacts | Log Analytics workspace | Azure Monitor |
|---|---|---|---|---|---|---|---|---|
| Data scientist | R | LAR | MR | |||||
| Data analyst | ||||||||
| Model tester | ||||||||
| Business stakeholders | MR | |||||||
| Project lead (Data science lead) | R | R, KVR | R | LAR | MR | |||
| Project/product owner | MR | |||||||
| Platform technical support | O | O, KVA | DOPCA | O | O | O | ||
| Model end user | ||||||||
| CI/CD processes | O | O, KVA | AcrPush | DOPCA | O | O | O | |
| Machine Learning workspace | R | C | C | |||||
| Monitoring processes | R | LAR | MR | |||||
| Data governance processes | R | R | R | R | R |
Preproduction environment
| Persona | Machine Learning workspace | Key Vault | Container Registry | Storage account | Azure DevOps | Azure Artifacts | Log Analytics workspace | Azure Monitor |
|---|---|---|---|---|---|---|---|---|
| Data scientist | ADS | R, KVA | C | C | C | C | LAC | MC |
| Data analyst | R | C | LAR | MC | ||||
| Model tester | R | R, KVR | R | R | R | R | LAR | MR |
| Business stakeholders | R | R | R | R | R | |||
| Project lead (Data science lead) | C | C, KVA | C | C | C | C | LAC | MC |
| Project/product owner | R | R | MR | |||||
| Platform technical support | O | O, KVA | O | O | DOPCA | O | O | O |
| Model end user | ||||||||
| CI/CD processes | O | O, KVA | AcrPush | O | DOPCA | O | O | O |
| Machine Learning workspace | R, KVR | C | C | |||||
| Monitoring processes | R | R | R | R | R | R | LAC | |
| Data governance processes | R | R | R |
Note
Every persona retains access for the project's duration except platform technical support, which has temporary or just-in-time Microsoft Entra Privileged Identity Management (PIM) access.
RBAC plays a vital role in securing and streamlining MLOps workflows. RBAC restricts access based on assigned roles and prevents unauthorized users from accessing sensitive data, which mitigates security risks. Sensitive data includes training data or models and critical infrastructure, such as production pipelines. You can use RBAC to ensure compliance with data privacy regulations. RBAC also provides a clear record of access and permissions, which simplifies auditing, makes it easy to identify security gaps, and tracks user activity.
Package management
Dependencies on various packages, libraries, and binaries are common throughout the MLOps lifecycle. These dependencies, often community-developed and rapidly evolving, necessitate subject matter expert knowledge for proper use and understanding. You must ensure that the appropriate people have secure access to diverse assets, such as packages and libraries, but you must also prevent vulnerabilities. Data scientists encounter this problem when they assemble specialized building blocks for machine learning solutions. Traditional software management approaches are costly and inefficient. Other approaches provide more value.
To manage these dependencies, you can use a secure, self-serve, package management process based on the Quarantine pattern. You can design this process to allow data scientists to self-serve from a curated list of packages and ensure that the packages are secure and compliant with organizational standards.
This approach includes safe-listing three industry standard machine learning package repositories: Microsoft Artifact Registry, Python Package Index (PyPI), and Conda. Safe-listing enables self-serve from individual Machine Learning workspaces. Then use an automated testing process during the deployment to scan the resulting solution containers. Failures elegantly exit the deployment process and remove the container. The following diagram and process flow demonstrates this process:
Process flow
Data scientists that work in a Machine Learning workspace that has a network configuration can self-serve machine learning packages on-demand from the machine learning package repositories. An exception process is required for everything else by using the private storage pattern, which is seeded and maintained by using a centralized function.
Machine Learning delivers machine learning solutions as Docker containers. As these solutions are developed, they're uploaded to Container Registry. Microsoft Defender for Containers generates vulnerability assessments for the container image.
Solution deployment occurs through a CI/CD process. Microsoft Defender for DevOps is used across the stack to provide security posture management and threat protection.
The solution container is deployed only if it passes each of the security processes. If the solution container fails a security process, the deployment fails with error notifications and full audit trails. The solution container is discarded.
The previous process flow provides a secure, self-serve, package management process for data scientists and ensures that the packages are secure and compliant with organizational standards. To balance innovation and security, you can grant data scientists self-service access to common machine learning packages, libraries, and binaries in preproduction environments. Require exceptions for less common packages. This strategy ensures that data scientists can remain productive during development, which prevents a major bottleneck during delivery.
To streamline your release processes, containerize environments for use in production environments. Containerized environments reduce toil and ensure continued security through vulnerability scanning. This process flow provides a repeatable approach that you can use across use cases to the time of delivery. It reduces the overall cost to build and deploy machine learning solutions within your enterprise.
Monitoring
In MLOps, monitoring is crucial for maintaining the health and performance of machine learning systems and ensuring that models remain effective and aligned with business goals. Monitoring supports governance, security, and cost controls during the inner loop phase. And it provides observability into the performance, model degradation, and usage when deploying solutions during the outer loop phase. Monitoring activities are relevant for personas such as Data Scientists, Business Stakeholders, Project Leads, Project Owners, Platform Technical Support, CI/CD processes, and Monitoring Processes.
Choose your monitoring and verification platform depending on your Machine Learning workspace setup, such as a project, team, or department.
Model performance
Monitor model performance to detect model problems and performance degradation early. Track performance to ensure that models remain accurate, reliable, and aligned with business objectives.
Data drift
Data drift tracks changes in the distribution of a model's input data by comparing it to the model's training data or recent past production data. These changes are a result of changes in market dynamics, feature transformation changes, or upstream data changes. Such changes can degrade model performance, so it's important to monitor for drift to ensure timely remediation. To perform a comparison, data drift refactoring requires recent production datasets and outputs.
Environment: Production
Azure facilitation: Machine Learning – Model monitoring
Prediction drift
Prediction drift tracks changes in the distribution of a model's prediction outputs by comparing it to validation, test-labeled, or recent production data. To perform a comparison, data drift refactoring requires recent production datasets and outputs.
Environment: Production
Azure facilitation: Machine Learning – Model monitoring
Resource
Use several model serving endpoint metrics to indicate quality and performance, such as CPU or memory usage. This approach helps you learn from production to help drive future investments or changes.
Environment: All
Azure facilitation: Monitor - Online endpoints metrics
Usage metrics
Monitor the usage of endpoints to ensure that you meet organization-specific or workload-specific key performance indicators, track usage patterns, and diagnose and remediate problems that your users experience.
Client requests
Track the number of client requests to the model endpoint to understand the active usage profile of the endpoints, which can affect scaling or cost optimization efforts.
Environment: Production
Azure facilitation: Monitor - Online endpoints metrics, such as RequestsPerMinute.
Notes:
- You can align acceptable thresholds to t-shirt sizing or anomalies that are tailored to your workload's needs.
- Retire models that are no longer in use from production.
Throttling delays
Throttling delays are slowdowns in the request and response of data transfers. Throttling happens at the Resource Manager level and the service level. Track metrics at both levels.
Environment: Production
Azure facilitation:
- Monitor - Resource Manager, sum of RequestThrottlingDelayMs, ResponseThrottlingDelayMs.
- Machine Learning - To check information about your endpoints' requests, you can enable online endpoint traffic logs. You can use a Log Analytics workspace to process logs.
Notes: Align acceptable thresholds to your workload's service-level objectives (SLOs) or service-level agreements (SLAs) and the solution's nonfunctional requirements (NFRs).
Errors generated
Track response code errors to help measure service reliability and ensure early detection of service problems. For example, a sudden increase in 500 server error responses could indicate a critical problem that needs immediate attention.
Environment: Production
Azure facilitation: Machine Learning - Enable online endpoint traffic logs to check information about your request. For example, you can check the count of XRequestId by using ModelStatusCode or ModelStatusReason. You can use a Log Analytics workspace to process logs.
Notes:
- All HTTP responses codes in the 400 and 500 range are classified as an error.
Cost optimization
Cost management and optimization in a cloud environment are crucial because they help workloads control expenses, allocate resources efficiently, and maximize value from their cloud services.
Workspace compute
When monthly operating expenses reach or exceed a predefined amount, generate alerts to notify relevant stakeholders, such as project leads or project owners, based on the workspace setup boundaries. You can determine your workspace setup based on project, team, or department-related boundaries.
Environment: All
Azure facilitation: Microsoft Cost Management - Budget alerts
Notes:
- Set budget thresholds based on the initial NFRs and cost estimates.
- Use multiple threshold tiers. Multiple threshold tiers ensure that stakeholders get appropriate warning before the budget is exceeded. These stakeholders might include business leads, project owners, or project Leads depending on the organization or workload.
- Consistent budget alerts could also be a trigger for refactoring to support greater demand.
Workspace staleness
If a Machine Learning workspace shows no signs of active use based on the associated compute usage for the intended use case, a project owner might decommission the workspace if it's no longer needed for a given project.
Environment: Preproduction
Azure facilitation:
- Monitor - Machine Learning metrics
- Machine Learning - Workspace metrics, such as the count of active cores over a period of time
Notes:
- Active cores should equal zero with aggregation of count.
- Align date thresholds to the project schedule.
Security
Monitor to detect deviations from appropriate security controls and baselines to ensure that Machine Learning workspaces are compliant with your organization's security policies. You can use a combination of predefined and custom-defined policies.
Environment: All
Azure facilitation: Azure Policy for Machine Learning
Endpoint security
To gain visibility into business-critical APIs, implement targeted security monitoring of all Machine Learning endpoints. You can investigate and improve your API security posture, prioritize vulnerability fixes, and quickly detect active real-time threats.
Environment: Production
Azure facilitation: Microsoft Defender for APIs offers broad lifecycle protection, detection, and response coverage for APIs.
Notes: Defender for APIs provides security for APIs that are published in Azure API Management. You can onboard Defender for APIs in the Microsoft Defender for Cloud portal or within the API Management instance in the Azure portal. You must integrate Machine Learning online endpoints with API Management.
Deployment monitoring
Deployment monitoring ensures that any endpoints you create adhere to your workload or organization policies and are free from vulnerabilities. This process requires that you enforce compliance policies on your Azure resources before and after deployment, provide continued security through vulnerability scanning, and ensure that the service meets SLOs while in operation.
Standards and governance
Monitor to detect deviations from appropriate standards and ensure that your workload adheres to guardrails.
Environment: All
Azure facilitation:
- Managed policy assignment and lifecycle through Azure Pipelines to treat policy as code.
- PSRule for Azure provides a testing framework for Azure infrastructure as code.
- You can use Enterprise Azure policy as code in CI/CD-based system deploy policies, policy sets, assignments, policy exemptions, and role assignments.
Notes: For more information, see Azure guidance for Machine Learning regulatory compliance.
Security scanning
Implement automated security scans as part of the automated integration and deployment processes.
Environment: All
Azure facilitation: Defender For DevOps
Notes: You can use apps in Azure Marketplace to extend this process for non-Microsoft security testing modules.
Ongoing service
Monitor the ongoing service of an API for performance optimization, security, and resource usage. Ensure timely error detection, efficient troubleshooting, and compliance with standards.
Environment: Production
Azure facilitation:
- Monitor - Machine Learning metrics
- Machine Learning - You can enable online endpoint traffic logs to check information about your service.
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal authors:
- Scott Donohoo | Senior Cloud Solution Architect
- Moritz Steller | Senior Cloud Solution Architect
Other contributors:
- Scott Mckinnon | Cloud Solution Architect
- Nicholas Moore | Cloud Solution Architect
- Darren Turchiarelli | Cloud Solution Architect
- Leo Kozhushnik | Cloud Solution Architect
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Next steps
- What is Azure Pipelines?
- Azure Arc overview
- What is Machine Learning?
- Data in Machine Learning
- Azure MLOps v2 solution accelerator
- End-to-end machine learning operations (MLOps) with Machine Learning
- Introduction to Azure Data Lake Storage Gen2
- Azure DevOps documentation
- GitHub Docs
- Synapse Analytics documentation
- Event Hubs documentation
- How Machine Learning works: resources and assets (v2)
- What are Machine Learning pipelines?