AI/ML OPS-Learning Road Map

1. Understand the Concepts First

AIOps

Definition: Applying AI and machine learning to IT operations to automate problem detection, root cause analysis, and resolution.

Focus Areas for DevOps Engineers:

Log analysis using ML (predictive analytics)

Anomaly detection in infrastructure metrics

Event correlation and alert suppression

Automated remediation (self-healing systems)

Key Tools/Platforms:

Splunk ITSI, Moogsoft, Dynatrace, Datadog AI, Prometheus + ML plugins

Learning Path:

1. Basics of monitoring and observability tools.

2. Introduction to anomaly detection and predictive analytics.

3. Practice using ML models to detect anomalies in logs/metrics.

4. Build small experiments for automated incident responses.

MLOps

Definition: Operationalizing ML models in production, including training, deployment, monitoring, and governance.

Focus Areas for DevOps Engineers:

Continuous integration & deployment for ML models (CI/CD for ML)

Data pipeline automation (ETL, preprocessing)

Model versioning and monitoring

Feedback loops for retraining models

Key Tools/Platforms:

MLflow, Kubeflow, TensorFlow Extended (TFX), SageMaker MLOps, GitHub Actions for ML

Container orchestration for ML: Docker + Kubernetes

Learning Path:

1. Learn ML basics (supervised, unsupervised learning, regression, classification).

2. Understand data pipelines and feature engineering.

3. Explore ML lifecycle: model training → versioning → deployment → monitoring.

4. Implement MLOps pipelines using CI/CD + Kubernetes + MLflow.

5. Start Practical Learning

Since DevOps is hands-on, start building small projects:

AIOps Example

1. Collect metrics from your EC2 instances using Prometheus.

2. Feed metrics to a Python script using scikit-learn for anomaly detection.

3. Trigger an alert or auto-scale instances based on detected anomalies.

4. Optional: Integrate with Slack/Jira for alert notifications.

MLOps Example

1. Pick a small ML model (e.g., predicting sales from CSV data).

2. Store your model and data in Git/GitHub.

3. Build a CI/CD pipeline:

Train the model automatically when data changes

Containerize the model with Docker

Deploy to a Kubernetes cluster

1. Monitor the model in production for data drift or accuracy degradation.

2. Automate retraining and redeployment as a pipeline.

3. Learn Key Technologies

Programming & ML Basics: Python, pandas, scikit-learn, TensorFlow

Data Handling: SQL, NoSQL, data pipelines, Airflow

Model Deployment & Serving: Docker, Kubernetes, Seldon Core, FastAPI

Monitoring & AIOps: Prometheus, Grafana, ELK Stack, AI-driven monitoring tools

CI/CD for ML: Jenkins, GitHub Actions, GitLab CI, Argo Workflows, MLflow pipelines

1. Suggested Learning Flow

2. Python & ML Basics

3. Data pipelines + ETL

4. MLOps pipelines + CI/CD integration

5. AIOps concepts + anomaly detection in monitoring

6. Hands-on projects (continuous)

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