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feat: Add OCI awareness across agents and skills

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Adds awareness of Oracle Cloud Infrastructure to any plugin that referenced
at least two of the major cloud vendors already. Skills updated to include
OCI services. Also updated some of the other cloud references.

Signed-off-by: Avi Miller <me@dje.li>
This commit is contained in:
Avi Miller
2026-03-16 17:49:42 +11:00
parent a6f0f457c4
commit 24df162978
68 changed files with 520 additions and 253 deletions
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4
plugins/machine-learning-ops/agents/data-scientist.md
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@@ -54,7 +54,7 @@ Expert data scientist combining strong statistical foundations with modern machi
- Data wrangling: cleaning, transformation, merging, reshaping large datasets
- Database interactions: PostgreSQL, MySQL, BigQuery, Snowflake, MongoDB
- Version control and reproducible analysis with Git, Jupyter notebooks
- Cloud platforms: AWS SageMaker, Azure ML, GCP Vertex AI
- Cloud platforms: AWS SageMaker, Azure ML, GCP Vertex AI, OCI Data Science
### Data Visualization & Communication
@@ -121,7 +121,7 @@ Expert data scientist combining strong statistical foundations with modern machi
- Model monitoring: drift detection, performance degradation alerts
- A/B testing frameworks for model comparison in production
- Containerization with Docker for model deployment
- Cloud deployment: AWS Lambda, Azure Functions, GCP Cloud Run
- Cloud deployment: AWS Lambda, Azure Functions, GCP Cloud Run, OCI Functions/Model Deployment
- Model governance and compliance documentation
### Data Engineering for Analytics

6
plugins/machine-learning-ops/agents/ml-engineer.md
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@@ -26,7 +26,7 @@ Expert ML engineer specializing in production-ready machine learning systems. Ma
- Model serving platforms: TensorFlow Serving, TorchServe, MLflow, BentoML
- Container orchestration: Docker, Kubernetes, Helm charts for ML workloads
- Cloud ML services: AWS SageMaker, Azure ML, GCP Vertex AI, Databricks ML
- Cloud ML services: AWS SageMaker, Azure ML, GCP Vertex AI, OCI Data Science, Databricks ML
- API frameworks: FastAPI, Flask, gRPC for ML microservices
- Real-time inference: Redis, Apache Kafka for streaming predictions
- Batch inference: Apache Spark, Ray, Dask for large-scale prediction jobs
@@ -35,7 +35,7 @@ Expert ML engineer specializing in production-ready machine learning systems. Ma
### Feature Engineering & Data Processing
- Feature stores: Feast, Tecton, AWS Feature Store, Databricks Feature Store
- Feature stores: Feast, Tecton, AWS Feature Store, OCI Object Storage-backed offline stores, Databricks Feature Store
- Data processing: Apache Spark, Pandas, Polars, Dask for large datasets
- Feature engineering: automated feature selection, feature crosses, embeddings
- Data validation: Great Expectations, TensorFlow Data Validation (TFDV)
@@ -133,7 +133,7 @@ Expert ML engineer specializing in production-ready machine learning systems. Ma
- Feature engineering and feature store technologies
- ML monitoring and observability best practices
- A/B testing and experimentation frameworks for ML
- Cloud ML platforms and services (AWS, GCP, Azure)
- Cloud ML platforms and services (AWS, GCP, Azure, OCI)
- Container orchestration and microservices for ML
- Distributed computing and parallel processing for ML
- Model optimization techniques (quantization, pruning, distillation)

22
plugins/machine-learning-ops/agents/mlops-engineer.md
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@@ -18,7 +18,7 @@ Expert MLOps engineer specializing in building scalable ML infrastructure and au
- Apache Airflow for complex DAG-based ML pipeline orchestration
- Prefect for modern dataflow orchestration with dynamic workflows
- Dagster for data-aware pipeline orchestration and asset management
- Azure ML Pipelines and AWS SageMaker Pipelines for cloud-native workflows
- Azure ML Pipelines, AWS SageMaker Pipelines, and OCI Data Science Jobs for cloud-native workflows
- Argo Workflows for container-native workflow orchestration
- GitHub Actions and GitLab CI/CD for ML pipeline automation
- Custom pipeline frameworks with Docker and Kubernetes
@@ -37,7 +37,7 @@ Expert MLOps engineer specializing in building scalable ML infrastructure and au
### Model Registry & Versioning
- MLflow Model Registry for centralized model management
- Azure ML Model Registry and AWS SageMaker Model Registry
- Azure ML Model Registry, AWS SageMaker Model Registry, and OCI Data Science model catalog patterns
- DVC for Git-based model and data versioning
- Pachyderm for data versioning and pipeline automation
- lakeFS for data versioning with Git-like semantics
@@ -80,6 +80,17 @@ Expert MLOps engineer specializing in building scalable ML infrastructure and au
- Cloud Build and Cloud Functions for ML automation
- Pub/Sub for event-driven ML pipeline architecture
#### OCI MLOps Stack
- OCI Data Science notebook sessions, jobs, and model deployments
- OCI Data Flow for distributed Spark-based feature processing
- OCI Object Storage and Data Catalog for ML data and metadata management
- OCI Container Engine for Kubernetes and OCIR for containerized ML workloads
- OCI Monitoring, Logging, and APM for ML system observability
- OCI Resource Manager and Functions for ML automation workflows
- OCI Vault and IAM for secrets management and access control
- OCI Events and Notifications for event-driven pipeline triggers
### Container Orchestration & Kubernetes
- Kubernetes deployments for ML workloads with resource management
@@ -96,15 +107,16 @@ Expert MLOps engineer specializing in building scalable ML infrastructure and au
- Terraform for multi-cloud ML infrastructure provisioning
- AWS CloudFormation and CDK for AWS ML infrastructure
- Azure ARM templates and Bicep for Azure ML resources
- Google Cloud Deployment Manager for GCP ML infrastructure
- Google Infrastructure Manager for GCP ML infrastructure
- OCI Resource Manager for OCI ML infrastructure
- Ansible and Pulumi for configuration management and IaC
- Docker and container registry management for ML images
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Secrets management with HashiCorp Vault, AWS Secrets Manager, OCI Vault
- Infrastructure monitoring and cost optimization strategies
### Data Pipeline & Feature Engineering
- Feature stores: Feast, Tecton, AWS Feature Store, Databricks Feature Store
- Feature stores: Feast, Tecton, AWS Feature Store, OCI Object Storage-backed offline stores, Databricks Feature Store
- Data versioning and lineage tracking with DVC, lakeFS, Great Expectations
- Real-time data pipelines with Apache Kafka, Pulsar, Kinesis
- Batch data processing with Apache Spark, Dask, Ray

2
plugins/machine-learning-ops/commands/ml-pipeline.md
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@@ -252,7 +252,7 @@ Deliver monitoring configuration, dashboards, and alert rules.
- **feature_store**: feast | tecton | databricks | custom
- **serving_platform**: kserve | seldon | torchserve | triton
- **orchestration**: kubeflow | airflow | prefect | dagster
- **cloud_provider**: aws | azure | gcp | multi-cloud
- **cloud_provider**: aws | azure | gcp | oci | multi-cloud
- **deployment_mode**: realtime | batch | streaming | hybrid
- **monitoring_stack**: prometheus | datadog | newrelic | custom

1
plugins/machine-learning-ops/skills/ml-pipeline-workflow/SKILL.md
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@@ -169,6 +169,7 @@ stages = [
- AWS SageMaker for managed ML infrastructure
- Google Vertex AI for GCP deployments
- Azure ML for Azure cloud
- OCI Data Science for Oracle Cloud Infrastructure deployments
- Kubernetes + KServe for cloud-agnostic serving
## Progressive Disclosure