ZenML

Step 1: Define the ML pipeline using ZenML's declarative syntax.

Step 2: Configure the ZenML stack with the desired components.

Step 3: Run the pipeline locally for rapid prototyping and testing.

Step 4: Deploy the pipeline to a production environment for scaling.

Step 1: Define a standard ZenML stack for the organization.

Step 2: Create reusable pipeline components using ZenML's `@step` decorator.

Step 3: Share the stack and components across teams.

Step 4: Automate the CI/CD process for ML pipelines.

Step 1: Integrate an experiment tracker (e.g., MLflow) into the ZenML stack.

Step 2: Configure ZenML to automatically log experiment parameters and metrics.

Step 3: Use the ZenML dashboard to compare different experiments.

Step 4: Reproduce any experiment by simply rerunning the corresponding ZenML pipeline.

Step 1: Define the LLM application pipeline using ZenML, integrating components like LlamaIndex and LangChain.

Step 2: Configure the infrastructure for LLM serving, ensuring adequate GPU resources.

Step 3: Implement caching and deduplication to reduce inference costs.

Step 4: Monitor the performance and cost of the LLM application in production.

Step 1: Deploy ZenML within a Kubernetes cluster.

Step 2: Define ML pipelines using ZenML’s Pythonic interface for Kubernetes.

Step 3: Leverage Kubernetes for scaling and resource management.

Step 4: Use ZenML to manage the deployment and monitoring of ML models.