Next Steps & Your LLMOps Journey

Congratulations! You've completed the LLMOps & AI Evaluation course. Let's recap what you've learned and explore where to go next.

What You've Learned

Module 1: LLMOps Fundamentals

LLMOps vs MLOps vs DevOps
The LLM production lifecycle
Key metrics: Latency, Cost, Quality, Safety
Evaluation-driven development

Module 2: Evaluation Approaches

LLM-as-Judge patterns
Reference-based vs reference-free evaluation
Human evaluation and annotation
Building evaluation datasets

Module 3: LangSmith

Tracing with @traceable
Insights Agent for pattern discovery
Multi-turn evaluation
Custom evaluators and datasets

Module 4: MLflow

Experiment tracking
Built-in LLM scorers
Custom judges with make_judge
DeepEval and RAGAS integration

Module 5: W&B Weave

The @weave.op() decorator
Evaluation pipelines
Experiment comparison
LLM-as-Judge in Weave

Module 6: Production Operations

SLOs and alerting
Cost tracking and optimization
CI/CD integration
Quality gates

Your LLMOps Toolkit

You now have three powerful tools in your toolkit:

Tool	Best For
LangSmith	LangChain apps, Insights Agent, production tracing
MLflow	Experiment tracking, model registry, open source
W&B Weave	Evaluation-driven dev, experiment comparison

Recommended Next Steps

1. Implement in Your Project

Start small:

Week 1: Add tracing to one LLM function
Week 2: Create your first evaluation dataset (20 examples)
Week 3: Build one custom evaluator
Week 4: Set up CI quality checks

2. Build Your Evaluation Suite

Essential evaluations to create:

Accuracy: Does it answer correctly?
Helpfulness: Is the response useful?
Safety: No harmful content?
Latency: Fast enough for users?

3. Establish Baselines

Before optimizing:

Measure current quality metrics
Document what "good" looks like
Set initial SLOs
Track improvements over time

Continue Your Learning

Recommended Courses

Deepen your AI engineering skills with these courses:

Course	Focus
RAG Systems Mastery	Build production RAG with RAGAS metrics
Fine-tuning LLMs	Custom models for your domain
AI Fundamentals	Core AI/ML concepts

MLOps Fundamentals (Coming Soon)

Ready to go deeper? The upcoming MLOps Fundamentals course will cover:

Infrastructure for ML systems
Model serving and deployment
Feature stores and data pipelines
Advanced monitoring and observability
ML system design patterns

This course builds directly on what you've learned here, expanding from LLM-specific operations to broader ML infrastructure.

Key Takeaways

Evaluate first, optimize second: Build evaluation before changing prompts
Start simple: Basic metrics before complex judges
Automate everything: CI/CD for quality gates
Track trends: Quality changes over time matter
Use the right tool: Each platform has strengths

Final Checklist

Before deploying your LLM to production:

Thank You

You've taken an important step in becoming an LLMOps practitioner. The skills you've learned here are in high demand as more organizations deploy LLM applications to production.

Remember: Great LLM applications aren't just built—they're measured, improved, and maintained.

Good luck on your LLMOps journey! :::

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