Where is MLOps heading (2025+)?
mediumAnswer
- (1) LLMOps as first-class discipline: prompts, RAG, agents as production artifacts.
- (2) Agent-based systems: multi-step monitoring + eval + debugging.
- (3) Foundation-model-as-a-service reduces training needs for most teams.
- (4) Compound AI systems: chains, routers, tool use.
- (5) Continuous eval: LLM-judge + golden sets replace hard metrics.
- (6) Cost + latency as first-class SLOs.
- (7) Regulatory pressure (EU AI Act) drives audit / lineage / safety.
- (8) Convergence of DataOps + MLOps + AIops + SRE.
Check yourself — multiple choice
- Same as 2020
- LLMOps first-class + agent systems + FaaS reduces training + compound AI (chains/router/tools) + continuous eval judge+golden + cost/latency SLOs + regulatory audit + DataOps+MLOps+AIops+SRE convergence
- Random
- Not real
MLOps future: LLMOps + agents + compound AI + continuous eval + regulation + convergence.
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