SV Infotech · Dilsukhnagar, Hyderabad
MLOps & LLMOps
Personal study sheet
This is an independent study aid, not an official syllabus, admission assessment or promise of class assignments. Programme availability requires confirmation. Confirm the current teaching scope and practical access with the office.
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Set your starting point
Useful foundations: Python, Git, APIs and an understanding of machine-learning or LLM application development are helpful.
Explore the learning roadmap
01Map the model lifecycle
Connect source data, training or prompt configuration, evaluation and serving. Identify who approves a change and what evidence should accompany it.
02Version inputs and artefacts
Track code, configuration and relevant datasets without publishing secrets or personal information. Make the relationship between a release and its evaluation results visible.
03Build meaningful evaluation gates
Choose checks for predictive performance or AI task quality, as appropriate. Include difficult cases and regressions rather than testing only a few successful demonstrations.
04Deliver controlled releases
Compare test environments, gradual rollout and rollback. Consider the compatibility of a model or prompt change with the surrounding application.
05Observe production behaviour
Discuss quality signals, drift, latency, failures and cost. Distinguish a monitor that detects a symptom from a diagnosis of its underlying cause.
06Review governance and recovery
Document data permissions, incident ownership and recovery procedures. Confirm the programme's platform, practical environment and evaluation scope before treating this guide as an offered syllabus.
MLOps & LLMOps: a worked study example
Illustrative scenario for discussion and self-study.
A fictional model update improves one evaluation result but worsens a critical failure case. Define the evidence required before release, record which model and data produced it and describe a rollback decision. For an LLM application, include prompt, retrieval and tool changes rather than tracking only the base model name.
What to check in your work
- Make an evaluation result reproducible from its recorded inputs.
- Define a release threshold and a useful recovery procedure.
How this pathway compares
MLOps and LLMOps connects model behaviour to reliable operation. AWS and DevOps offers a deployment comparison; Generative AI provides context for retrieval, tool use and application-level evaluation.
Check your reasoning
What should a model release record beyond the final model file?
Suggested explanation
Record the relevant data and configuration versions, evaluation results, dependencies and deployment identifier. Define monitoring and a rollback procedure. For an LLM application, include the retrieval and prompt configuration where relevant, while protecting sensitive information in logs.
Plan the conversation
Ask about the actual syllabus, practical environment and feedback arrangements. Confirm fees, duration, trainer and current batch schedule directly; this sheet supplies none of those details.
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