GoogleCloudPlatform/ai-on-gke
Mar 2025 – Mar 2025
1 Month active
Languages Used
DockerfileHCLPythonShellTerraformYAML
Technical Skills
Argo WorkflowsArtifact RegistryCloud BuildDockerFastAPIGCP
ArthurKamalov
PROFILE
Arthurkamalov
Developed comprehensive deployment guides for AI workloads on GoogleCloudPlatform/ai-on-gke, focusing on scalable, cloud-native solutions for Retrieval-Augmented Generation and Metaflow systems. Leveraged Terraform, Kubernetes, and Docker to automate infrastructure provisioning, containerization, and secure deployment on GKE. Integrated Redis and Ollama for RAG workflows and implemented model fine-tuning and serving with Gemma 2-9B for Metaflow. Authored detailed procedures for data ingestion and end-to-end testing, ensuring reproducibility and operational reliability. Emphasized infrastructure-as-code and best practices for managing secrets and container images, enabling faster experimentation and safer production deployments aligned with business goals for accelerated AI capability delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
2,761
Activity Months1
Your Network
37 peopleSame Organization
@akvelon.us1
Work History
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 focused on delivering scalable, cloud-native deployment guides for AI workloads on GKE. Completed end-to-end guides for RAG and Metaflow deployments, incorporating infrastructure-as-code, containerization, and secure deployment patterns. These artifacts enable faster experimentation, reproducible environments, and safer production deployments, aligning with business goals of accelerated AI capability delivery and operational reliability.
2 Commits • 2 Features
Mar 1, 2025March 2025 focused on delivering scalable, cloud-native deployment guides for AI workloads on GKE. Completed end-to-end guides for RAG and Metaflow deployments, incorporating infrastructure-as-code, containerization, and secure deployment patterns. These artifacts enable faster experimentation, reproducible environments, and safer production deployments, aligning with business goals of accelerated AI capability delivery and operational reliability.
March 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileHCLPythonShellTerraformYAML
Technical Skills
Argo WorkflowsArtifact RegistryCloud BuildDockerFastAPIGCPGKEKubernetesLLMsLlamaIndexMetaflowModel Fine-tuningModel ServingOllamaRAG
Repositories Contributed To
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