Shipping is step one. Operating is the career.
8 weeks. 48 hours live. A full Databricks-native LLMOps curriculum: MLflow tracing, evaluation, Vector Search, prompt registry, agent development, Mosaic AI Model Serving, Asset Bundles, CI/CD, and post-deployment monitoring. You graduate with a deployed, observable, and reproducible LLM application you can walk into any interview with.
✓ Live sessions Saturdays & Sundays · 8:00 PM – 11:00 PM WAT · Cohort 1 · Enrolling
Building an LLM app is easy. Keeping it reproducible, observable, and safe in production is where teams get stuck.
When outputs go wrong, you can't replay what happened. No call graph, no prompt version, no retrieval snapshot. Debugging becomes guesswork and your users notice first.
Prompts edited in UI, models updated by hand, endpoints configured ad hoc. Environments drift, rollbacks are scary, and nobody can tell you what's actually running in prod.
Token spend triples overnight, hallucination rates creep, and nobody notices until the invoice or the complaint. Without evals and monitoring, you learn about problems from stakeholders.
From workspace-ready engineer to platform operator shipping production LLM applications.
Trace, evaluate, and version everything
Set up your Databricks workspace, build knowledge pipelines, trace every LLM call, evaluate quality systematically, and register prompts as versioned artifacts. By Week 4 your pipeline is fully observable.
Deploy, govern, and monitor at scale
Log agents into Unity Catalog, serve them through Mosaic AI Model Serving, define infrastructure with Asset Bundles, wire up CI/CD, and run your system with live monitoring and drift detection.
Each week you learn the pattern, then ship a piece of the platform. Click a week to see what you'll own by the end of it.
What LLMOps actually means, how it extends MLOps, and the operator mindset. Provision a Databricks workspace, configure compute, set up version-controlled notebooks, and ship your first traced LLM call end-to-end.
Ingest a real corpus, pick the right chunking strategy, generate embeddings, and wire up a Databricks Vector Search Index. Everything lands in governed Delta tables so your knowledge layer stays reproducible.
Instrument every LLM call, retrieval, and tool invocation with MLflow Tracing. Build an evaluation harness with curated datasets, LLM-as-judge, and custom metrics so quality is measured, not guessed.
Stop treating prompts as code comments. Register them as versioned artifacts, run automated prompt optimization, and A/B test prompt variants against your eval harness with rollback you can trust.
Compose a tool-calling agent, plug in managed Databricks MCP servers, and build custom MCP tools for your own systems. Log the agent using MLflow and register it in Unity Catalog with full lineage.
Deploy your registered agent through Mosaic AI Model Serving. Configure autoscaling, route traffic, set token and rate limits, and wire up cost guardrails so production behaves predictably under load.
Define your whole system as code with Databricks Asset Bundles. Promote through dev, staging, and prod with GitHub Actions, manage secrets and permissions cleanly, and make deploys boring.
Add post-deployment monitoring, drift detection, and cost dashboards. Tighten your test pyramid for LLM workloads, then present your end-to-end system on Demo Day with traces, evals, and production metrics.
A Databricks-native stack for LLM applications, paired with the open tooling that surrounds it.
Databricks Workspace, Unity Catalog, Delta tables, Clusters & compute
MLflow Tracing, MLflow Evaluation, LLM-as-judge, Golden datasets
MLflow Prompt Registry, Prompt optimization, Versioning & rollback
Databricks Vector Search, Embedding models, Chunking pipelines, Delta sync
Tool calling, Managed MCP servers, Custom MCP apps, Agent logging
Mosaic AI Model Serving, Autoscaling, Rate limits, Cost guardrails
Databricks Asset Bundles, GitHub Actions, Environment promotion, Secrets
Production monitoring, Drift detection, Cost dashboards, Alerting
Unit & integration tests, Eval pipelines, LLMOps vs MLOps testing patterns
Live instruction, production builds, reference architecture, and an operator community. One price. Everything in.
Feedback from engineers who ran LLMOps playbooks inside real teams.
"Week 3 alone paid for the cohort. MLflow Tracing gave us a call graph we'd been reverse-engineering from logs for months. Root-cause time on hallucinations dropped from hours to minutes."
"Asset Bundles changed how our team ships. No more click-ops in the workspace — every prompt, endpoint, and job is code. Our staging environment actually mirrors prod now."
"The Prompt Registry module was the missing piece. We stopped arguing over which prompt was in prod. Versioning, rollback, and A/B against the eval harness is now the standard workflow."
"I'd built agents before. I'd never properly served them. Week 6 walked me through Mosaic AI Serving with autoscaling and cost limits, and our first prod agent has been running for eight weeks without a page."
"The evaluation playbook is what we use across every LLM project now. Golden datasets, LLM-as-judge, CI gates — it's how we give stakeholders a number they can trust."
"I joined as a DevOps engineer trying to understand what my ML team actually needed. I left owning our LLMOps platform. The cohort conversations alone were worth the price."
Practising AI Platform Engineers
The LLMOps Bootcamp is led by SoftBricks Academy professionals — engineers who operate LLM applications on Databricks for real clients every week. The curriculum is distilled from production engagements: platforms we've architected, incidents we've debugged, and evaluation harnesses we've used to defend quality in front of stakeholders. Every module comes with the patterns, templates, and guardrails we use in our own work.
LLMOps is a platform discipline. This cohort is built for people ready to own the whole system.
No upsells. No locked modules. Everything you need to ship and operate LLM applications on Databricks.
✓ Secure checkout · EMI available · Invoice on request
Enroll now and get setup access before Cohort 1 kicks off — provision your workspace, clone the repo, and walk into Week 1 already oriented. The earlier you start, the further you go.
Enroll Now — $887What operators usually ask before joining.
Plan for 10-15 hours per week. That includes 6 hours of live sessions (two 3-hour sessions on Saturday and Sunday) plus 4-9 hours on the weekly build and self-study. The builds are where the operator instincts get wired in — don't skip them.
No. You need solid Python, comfort with APIs and version control, and basic familiarity with GenAI use cases. We teach the operator discipline from the ground up in Week 1. If you've built an LLM prototype once, you're ready.
A free Databricks trial is enough to follow every build in the course. We walk through workspace setup, compute, and permissions on day one so no one gets stuck on infrastructure. If your employer already has Databricks, even better — you'll be applying the patterns directly to your job.
All sessions are recorded and available within 24 hours. You also get unlimited re-attendance for future cohorts at no extra cost. Life happens — the program is designed for real working engineers.
Agentic AI is about building the systems — how to architect agents, memory, RAG, and multi-agent workflows. LLMOps is about operating them — how to trace, evaluate, deploy, serve, govern, and monitor LLM applications at production quality on Databricks. The two bootcamps are complementary: build with Agentic AI, operate with LLMOps.
Yes. EMI is available at checkout. Employer sponsorship and invoicing also available — email academy@softbricks.ai and we'll send the paperwork.
Live sessions are every Saturday and Sunday, 8:00 PM – 11:00 PM WAT. All sessions are recorded for anyone who can't attend live. The community and async channels are active 24/7.
No bootcamp can promise a job — anyone who does is lying. What you get here is what most AI hires are missing: a deployed, traced, evaluated, and monitored LLM application you can walk through live, plus operator-level fluency that shows up the moment you open your laptop in an interview. That combination is what gets people hired.