LLMOps & AI Engineering for Production
capability building,
designed for your organisation.
A custom-built corporate programme for ML engineers, MLOps engineers, platform engineers, SREs, DevOps engineers, and senior software engineers (3+ years) responsible for shipping and operating LLM-powered applications in production. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- MLOps vs. LLMOps: shared discipline + LLM-specific concerns
- What's LLM-specific: prompt versioning, eval-driven deploy, drift on retrieval/embedding/prompt, cost variability
- What's not LLM-specific: CI/CD, identity, networking, observability fundamentals
- Production maturity model: prototype → pilot → managed-prod → mature-platform
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
Demonstrable skills your team will apply on live projects.
Architect production LLM systems end-to-end
Identity, networking, observability, evaluation, cost governance, security across model, RAG, and agent layers.
Implement eval-driven CI/CD for LLM applications
Reference test sets, automated evaluation, CI gates on prompt/model regressions.
Govern LLM costs at scale
Routing, caching, batching, model selection, prompt compression with measurable savings against capstone systems.
Pass LLMOps certification
Two attempts; cohort first-attempt pass rate 87%.
Reduce LLM operating costs by 50–70%
Documented savings on capstone systems via routing, caching, batch, and prompt-engineering.
Lead production AI engineering
Equipped to take an organisation from notebook prototypes to operationally-mature production AI.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has shipped LLM-powered demos but never operated one in production at scale
- ·Limited fluency with LLM-specific operational concerns: drift, eval-driven CI/CD, cost spikes, jailbreak detection
- ·Cannot architect a production LLM stack with proper observability, evaluation, and cost governance
- ·Familiar with classical MLOps (MLflow, model registry) but unsure how it applies to LLMs
- ·No working knowledge of LLM-specific tooling: Langfuse, LangSmith, Arize, Helicone, promptfoo
- ·Unfamiliar with LLM cost optimisation patterns at scale: routing, caching, batch, prompt compression
Where they'll arrive
- ✓Production LLM architecture designs and ships full LLM stacks with identity, network, observability, evaluation, cost, security
- ✓LLM-specific tooling fluency Langfuse, LangSmith, Arize, Helicone, promptfoo for observability and evaluation
- ✓Eval-driven CI/CD automated regression detection on prompt and model changes
- ✓Cost governance at scale routing, caching, batching, prompt compression, fine-tune-vs-prompt decisions
- ✓Incident response for LLM systems playbooks for drift, jailbreaks, cost spikes, hallucination-driven harm
- ✓LLMOps credential practitioner-level production engineering certification
Built for L&D outcomes, not seat counts.
LLMOps corporate training programme built for production engineers
This LLMOps corporate training programme is designed for ML engineers, MLOps engineers, platform engineers, SREs, and DevOps engineers who need to ship and operate LLM-powered applications at enterprise scale not a conceptual overview for non-technical audiences.
LLMOps training grounded in real production tooling
LLMOps training in this programme uses the tools production teams actually use Langfuse, LangSmith, Arize, Helicone, and promptfoo. Every lab produces a working, instrumented output that learners can carry directly into their production environment.
Enterprise LLM observability training with end-to-end instrumentation
Enterprise LLM observability training covers distributed tracing across model, RAG, agent, and tool layers with LLM-specific metrics including token usage, latency, cost attribution, and faithfulness scoring. Learners instrument a full working application from end to end in the observability lab.
LLM production engineering upskilling with documented cost savings
LLM production engineering upskilling in this programme produces measurable outcomes. The cost governance module targets a 50% or greater reduction in capstone system costs through routing, caching, batching, and prompt compression with before-and-after documentation that learners can take back to their organisation.
LLMOps training for ML teams India cohort-tailored delivery
LLMOps training for ML teams India is delivered as a cohort programme tailored to your organisation's tech stack, deployment environment, and target maturity level. Curriculum depth, lab environments, and capstone briefs are adapted per engagement not delivered as a fixed catalogue.
LLMOps training for DevOps and SRE teams incident response and platform engineering
LLMOps training for DevOps and SRE teams goes beyond model deployment. Dedicated modules cover LLM incident taxonomy, alert design, triage playbooks, post-mortem patterns, and internal platform engineering so SREs and DevOps engineers can operate LLM systems with the same discipline they apply to classical services.
A four-milestone path from skill gap to client-ready.
LLMOps foundations & production architecture baseline
Learners establish a precise understanding of where LLMOps diverges from classical MLOps, map the production maturity model, and design reference architectures for chat assistant, RAG service, and agent system patterns. This LLMOps corporate training programme begins with architecture and maturity assessment not theory because production decisions start at the design stage.
Observability, evaluation, and drift detection labs
Learners instrument a working LLM application end-to-end using Langfuse and Arize, build reference test sets, implement LLM-as-judge evaluation, and establish CI gates on prompt and model regressions. This is the core of enterprise LLM observability training what you cannot see, you cannot operate, and what you cannot evaluate, you cannot safely deploy.
Cost governance, security, incident response, and scale architecture
Learners apply cost optimisation patterns targeting 50%+ savings, build incident response playbooks for LLM-specific failure modes, implement security controls including prompt injection detection and PII redaction, and design multi-region and multi-tenant architectures. LLM production engineering upskilling at this stage addresses the full operational surface that separates production systems from demos.
LLM platform engineering, FinOps, capstone & certification
Learners design an internal LLM platform specification for their organisation, close the FinOps loop with cost attribution and executive reporting frameworks, and present a production-grade capstone system full observability, eval-driven CI/CD, cost governance, incident playbook, and red-team report to a panel including NovelVista AI Practice leads and an invited industry SRE or platform engineering leader.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"AI transformation is not just about adopting new tools it is about building the operational discipline, observability infrastructure, and engineering culture that allows organisations to ship LLM-powered systems that are reliable, cost-governed, and safe at scale."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·ML engineers and MLOps engineers who need a structured LLMOps corporate training programme to move from classical MLOps patterns into LLM-specific production operations at enterprise scale
- ·Platform engineers and SREs responsible for LLM infrastructure who require LLMOps training for DevOps and SRE teams covering incident response, alert design, observability tooling, and LLM platform engineering
- ·DevOps engineers at AI-forward organisations in India and globally who need LLMOps training for ML teams India aligned to their organisation's cloud stack, deployment environment, and production maturity level
- ·Senior software engineers and solution architects designing or operating RAG services, agent systems, or chat assistant platforms who need LLM production engineering upskilling that covers the full ops surface
- ·L&D leaders and engineering managers at enterprises building or scaling LLM-powered products who want a cohort-based LLMOps corporate training programme with measurable engineering outcomes and certification
Pre-requisites
- ·3+ years of software engineering, MLOps, DevOps, SRE, or platform engineering experience this programme is designed for practitioners who build and operate production systems
- ·Working familiarity with at least one cloud platform (AWS, Azure, or GCP) and foundational knowledge of containerisation, CI/CD pipelines, and API-based service architectures
- ·Basic exposure to LLM APIs (OpenAI, Anthropic, or equivalent) is helpful learners do not need deep model knowledge but should understand what an LLM API call looks like and what tokens and context windows are
- ·Enterprise cohorts should confirm lab environment access and align data-handling, PII, and network security expectations before programme start
Trusted by L&D leaders across the world.
"The observability module was transformative for our team. We had been running LLM applications in production without proper tracing or cost attribution. The Langfuse and Arize labs gave us a working instrumentation setup we deployed to our own environment within a week of the programme."
"The cost governance module alone justified the investment. Our team cut LLM inference costs by 58% on our primary RAG service within six weeks of the programme close using the routing and caching patterns from the lab directly. The capstone made us document the savings, which made the ROI conversation with leadership straightforward."
"As an SRE team lead, what stood out was the incident response and platform engineering modules. LLM systems fail differently from classical services the hallucination-driven harm taxonomy and the jailbreak detection patterns were things our team had never formally mapped before. We shipped an updated incident playbook the week after the programme ended."
Questions L&D teams ask before signing.
MLOps focuses on training, deploying, and monitoring predictive ML models, while LLMOps focuses on prompts, RAG pipelines, evaluation, observability, guardrails, token costs, and production reliability for LLM applications.