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MLops Byte Sized Program - Kubernetes & LLMOps: From Basics to Best Practices


Course Source: Internal Course Duration: 30 hours

Content
  • Introduction to Kubernetes
  • What is Kubernetes ?
  • Kubernetes Components
  • Kubernetes Architecture
  • Benefits of Kubernetes
  • Minikube and Kubectl
  • Basic Kubectl Commands
  • YAML Configuration File in Kubernetes
  • Complete Application Deployment using Kubernetes Components
  • Kubernetes Namespaces
  • Pods and Containers - Kubernetes Networking
  • Kubernetes ConfigMap and Secret as Kubernetes Volumes
  • Kubeflow
  • Intro to Kubeflow
  • Basic kubeflow and kubernetes install
  • Kubeflow Install breakdown
  • kubeflow install on minikube
  • Kubeflow Pipelines
  • Multi-Tenancy in the modern world! An introduction for kubeflow MT
  • kubeflow and dex add user + simple kubeflow team collaboration
  • State of kubeflow at home early 2021
  • Assignment on Kubeflow
  • LLMOps monitoring
  • What is LLM monitoring, and why is it important?
  • Data Drift in LLMs—Causes, Challenges, and Strategies
  • Evaluation Metrics
  • Monitoring ML systems in production. Which metrics should you track?
  • Data Drift vs. Concept Drift and Why Monitoring for Them is Important
  • 5 methods to detect drift in ML embeddings
  • Monitoring NLP models in production: a tutorial on detecting drift in text data
  • Drift Monitoring and Evaluation for LLM Apps | Evidently AI
  • Monitoring LLM Performance with LangChain and LangKit
  • Understanding and Mitigating Model Drift in Machine Learning
  • LLM Guardrails
  • Guardrails for LLMs: A Practical Approach
  • LLM Guard: Controls and Guardrails for LLMs
  • Anonymize - LLM Guard
  • Ban Competitors - LLM Guard
  • Sentiment - LLM Guard
  • Relevance - LLM Guard
  • Factual Consistency - LLM Guard
  • Assignment on LLM Guardrails
  • Assignment on LLM Monitoring
  • Feedback
  • Feedback
Completion rules
  • All units must be completed