AI and LLM Observability Corporate Training
Make every LLM decision traceable, measurable, and safe in production.
Learn how to make LLM behavior visible and controllable. Edstellar's AI and LLM Observability corporate training program helps your employees trace prompts and completions, develop reliable eval metrics, and build drift alerts and dashboards that catch problems early. As the corporate training company enterprises rely on for deep technical programs, we shape every session around your team's AI stack, roles, and goals, with outcomes you can measure.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build an observability-driven LLM team
THE EDSTELLAR LLM OBSERVABILITY CAPABILITY MODEL
Baseline observability
Skill checks and stack reviews map how your team currently traces, evaluates, and monitors LLM behavior.
Standardize telemetry
One shared approach to trace logging, eval metrics, and alerting, aligned to your LLM stack and use cases.
Practice on real traces
Guided exercises work through trace logs, eval runs, and drift scenarios from production-like LLM traffic.
Prove the improvement
Before-and-after exercises benchmark each participant's ability to diagnose and monitor LLM systems.
Scale across AI teams
One observability standard rolls out across cohorts, products, and LLM use cases as your AI footprint grows.
Need more than one program?Get a group quote
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Program Overview: What is AI and LLM Observability Corporate Training?
AI and LLM Observability Corporate Training is Edstellar's instructor-led technical program for teams running LLM applications in production. Participants learn trace logging of prompts and completions, eval metrics for output quality, drift detection, and production monitoring. The focus stays on operational skills: knowing exactly what your LLM systems are doing, at all times.
Training is hands-on from the first module. Participants instrument prompt and completion logging, build repeatable eval suites, and configure drift alerts in guided labs on realistic LLM traffic. Instructors coach your team through latency breakdowns, token usage tracking, and incident triage against scenarios drawn from your own stack, reviewing every exercise with direct feedback. The program closes with a capstone where the team ships an end-to-end observability build for a demo LLM application.
LLM systems fail in ways traditional monitoring cannot see: silent quality decay, prompt regressions, and rising token costs with no owner. This program teaches your teams to detect drift early, evaluate output quality continuously, and respond to incidents with runbooks instead of guesswork. Every technique is practiced on realistic traffic, so the skills transfer directly to production.
They leave having instrumented a live LLM application end to end, evals running, drift alerts firing, costs visible. When the next model misbehaves in production at 2 a.m., they'll know exactly where to look.
Across six modules, your team moves from observability foundations to a production monitoring capstone. Sessions use realistic LLM traffic, trace logs, and eval scenarios, so every technique transfers directly to your environment.
Delivered as a hybrid technical program, it fits around your operations, with scheduling set along with you.
What's Included in this Workshop for Your Enterprise Teams
- 1
Trace logging foundations
Instrument prompts, completions, and latency.
- 2
Eval metrics and quality checks
Score LLM outputs with repeatable eval methods.
- 3
Drift detection and alerting
Flag behavior changes before users notice.
- 4
Production dashboards
Build monitors your team can run every day.
- 5
Observability capstone
Ship an end-to-end monitoring plan for one LLM app.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
AI and LLM Observability Course Topics and Program Outline
Six Modules follow a clear learning path from trace logging to production monitoring. Each Session builds on the last, with content tailored to your LLM stack and roles.
Built by Edstellar with AI observability experts
Built by engineers running LLM systems in production, shaped around your stack and use cases before delivery.
Baseline each participant's LLM observability readiness before module 01 begins
Build strong LLM observability foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on live LLM pipelines
Apply LLM observability skills to real-world scenarios and a capstone monitoring project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: guided labs on live LLM pipelines and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
Explore sample LLM traces and map the telemetry your own apps should emit.
learna)Observability vs monitoring for LLM systems
b)Traces, spans, metrics, and logs explained
c)Anatomy of a prompt-completion trace
d)Core telemetry every LLM app should emit
e)Technical baseline check
Instrument a demo app to log prompts, completions, latency, and errors.
practicea)Prompt and completion logging patterns
b)Span structure and trace context
c)Latency breakdowns and token usage tracking
d)Error and exception capture in LLM calls
e)Redacting sensitive data in traces
Score model outputs with an eval suite and catch a planted regression.
practicea)Quality, accuracy, and safety metrics
b)Building repeatable eval suites
c)Regression testing for prompt changes
d)Human review vs automated evals
e)Reading eval results as production signals
Detect drift in sample traffic and tune alerts that route to owners.
practicea)Types of drift in LLM systems
b)Monitoring input and output distributions
c)Quality decay detection techniques
d)Designing alert rules and thresholds
e)Alert routing and on-call workflows
Triage a failing eval and a latency spike, then document the fix.
practicea)Production dashboards for LLM health
b)Cost and latency tracking
c)Guardrails for risky outputs
d)Incident triage for LLM failures
e)Runbooks your team keeps
Capstone: instrument an app, build evals and alerts, present the plan.
applya)Capstone scope and success criteria
b)Instrumenting a demo LLM application
c)Eval suite and alert configuration
d)Monitoring plan presentation
e)Instructor review and next steps
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Your team will leave with the competency to keep LLM systems visible, reliable, and safe in production. Each outcome below is practiced in guided exercises and demonstrated in the capstone build.
Trace LLM requests end to end
Capture complete traces of prompts, completions, latency, and errors, and query them to explain any model decision.
Build reliable eval suites
Design repeatable evaluations that score output quality, accuracy, and safety, and catch regressions when prompts or models change.
Detect drift before users do
Monitor input and output distributions for data drift, prompt drift, and quality decay, and tune alerts that reach the right people fast.
Build production dashboards
Combine traces, eval trends, cost, and latency into dashboards that show LLM health at a glance for engineers and stakeholders.
Triage LLM incidents fast
Diagnose failing evals, latency spikes, and drifting assistants with a repeatable triage workflow, and document fixes in runbooks.
Apply guardrails in production
Configure guardrails that block or flag risky outputs, and monitor their effectiveness alongside the rest of your telemetry.
Ship a monitoring plan
Turn the capstone build into a production monitoring plan for one of your LLM apps, ready to present to stakeholders.
Teams that Benefit from this AI and LLM Observability Group Training
This program is built for engineering, data, and IT groups who ship and support LLM-powered products, from ML engineers to support leads. Content adapts to role and experience, so each cohort practices the workflows it actually owns.
Traces and dashboards your platform team can trust.
Platform engineers instrument LLM services with trace logging, build production dashboards, and set alerts that scale across models and tenants.
Workflow friction
- Blind spots in prompt and completion flows
- No standard telemetry across LLM services
- Alerts that fire too late or never at all
How the program helps
- Instrument services with consistent trace logging
- Build dashboards for health, cost, and latency
- Set alerts that catch drift before users do
Practice use cases
- Trace logging drills
- Dashboard build sessions
- Alert tuning practice
- Telemetry reviews
Eval metrics that prove model quality over time.
Data scientists build repeatable eval suites, track quality and drift metrics, and turn evaluation results into production signals that cross-functional partners trust.
Workflow friction
- Eval scores that change with every prompt edit
- No regression checks when models update
- Quality decay spotted only after complaints
How the program helps
- Build repeatable eval suites for every use case
- Track drift in inputs, outputs, and quality
- Turn eval results into production signals
Practice use cases
- Eval suite workshops
- Drift analysis sessions
- Metric review drills
On-call for LLM systems without the guesswork.
SREs get runbooks, alert routing, and triage workflows for LLM incidents, so failing evals and latency spikes are handled like any other production issue.
Workflow friction
- LLM incidents with no triage playbook
- Latency spikes with no trace to follow
- On-call alerts that lack clear ownership
How the program helps
- Triage LLM incidents with repeatable workflows
- Route alerts to the right owners fast
- Document fixes in runbooks the team keeps
Practice use cases
- Incident triage drills
- Alert routing practice
- Runbook writing sessions
See what users see, before they report it.
Product and support teams learn to read traces and dashboards, spot quality issues early, and give engineering precise, evidence-backed bug reports.
Workflow friction
- User complaints with no supporting evidence
- Cannot tell model errors from product bugs
- No visibility into what the assistant did
How the program helps
- Read traces to explain assistant behavior
- Spot quality issues in dashboards early
- File evidence-backed reports for engineers
Practice use cases
- Trace reading sessions
- Dashboard walkthroughs
- Issue triage practice
Your workforce leaves with reusable monitors, dashboards, and runbooks for production use.
Prerequisites: This program benefits network engineers, RF designers, planners, and wireless researchers most. Familiarity with basic wireless communications concepts helps, but is not required: foundations are taught from the ground up. No prior radar or 6G experience is needed, and labs adapt to each cohort's tools, whether teams work in simulation or on lab hardware.
From Blind-Spot Debugging to Production-Grade LLM Observability
Learners begin with LLM observability foundations, establishing what to instrument and why, before hands-on practice in trace logging and eval metrics for LLM systems. Practice then extends to drift detection, alerting, and monitoring LLM apps in production, building the signals platform, data science, and SRE teams rely on. The course closes with a capstone end-to-end observability build, wiring traces, evals, and alerts into one production-ready system.
Teams move from ad-hoc troubleshooting to a full observability practice that catches drift and incidents before users do.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is shaped around your LLM stack before delivery. The curriculum, guided exercises, and capstone stay the same in each format, so what your team can do after training never depends on how you run it.
Hybrid Training
Live instruction plus guided lab exercises across office and remote sessions. Your team traces, evaluates, and monitors realistic LLM traffic with instructor coaching. Instructor-led throughout, and best for deep technical practice.
- Live instruction across office and remote sessions
- Guided exercises with instructor support
- Realistic LLM tracing and eval scenarios
- Best for flexible schedules with real interaction
Onsite Training
Your instructor delivers at your facility with exercises set up for your team. Every participant works through tracing, eval, and monitoring tasks with direct coaching. Best for co-located teams.
- Instructor delivers at your facility
- Exercises set up for your team and stack
- Direct coaching during technical tasks
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live instructor-led sessions on your platform, with remote access to the full exercise environment. Sessions run on Zoom or Microsoft Teams, with breakout rooms for focused troubleshooting. Recordings land in your LMS after each session.
- Live sessions on Zoom or Microsoft Teams
- Remote access to the exercise environment
- Screen-shared guidance through every task
- Ideal for distributed teams across locations
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, preferred dates, and delivery mode in the lead form. We reply with a training plan covering scope, schedule, and pricing for your group.
We map your LLM stack, observability gaps, and team goals, then outline a training plan with scope, schedule, and pricing. The call takes 30 minutes and carries no obligation.
Yes. The full program, including guided exercises and the capstone, runs live virtual on your platform of choice, with the same curriculum as hybrid and onsite formats.
Yes. Your instructor delivers at any of your offices globally, with exercises set up for your team. Many clients mix onsite and remote sessions across batches.
The program runs 24 hours across six modules, typically over three to four days. Dates are set at mutually agreed times to fit your team's calendar.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Observability comes once your team runs LLMs in production with LLMOps, and before deeper evaluation and cost work. You cannot guard or optimise what you cannot see, so tracing and metrics come first, then evaluation and guardrails, then cost and latency tuning based on what the data shows.
Why Choose Edstellar as Your AI and LLM Observability Enterprise Training Provider
If your team needs to see exactly what their LLM systems are doing, start here. Tell us about your LLM stack, timelines, and preferred delivery mode, and we will shape a program around them.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your AI observability use cases, so every session maps to your real LLM operations needs.
Proven Methodology
5+ years delivering corporate training to ML platform engineers and data science teams across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds LLM observability pipelines with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your AI observability stack, with a full trainer profile shared before your program is booked.
Global Scale
One AI observability program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your AI observability capability lift visible.
Trace LLM Behavior Through Instrumentation, Evaluation, and Incident Practice
Edstellar helps AI application teams connect telemetry with decisions about quality and reliability. Participants instrument requests, score outputs, detect drift, and tune alerts before investigating failures and latency spikes. An end-to-end observability build brings traces, evaluations, dashboards, and response planning together for review against the application’s monitoring needs.
- Step 01
Consult
We examine application behavior and troubleshooting practices to identify gaps in trace coverage, output evaluation, drift detection, alert ownership, or incident diagnosis.
- Step 02
Plan
We build from observability foundations and instrumentation to evaluation metrics, drift alerts, and incident triage before assembling a complete monitoring and response plan.
- Step 03
Align
We align instruction with the cohort’s LLM application and code-reading experience, using structured logs and demo requests to connect instrumentation with operational questions.
- Step 04
Customize
We tailor trace examples, planted regressions, and traffic-drift scenarios to familiar application patterns while retaining latency, quality, error, and ownership checks.
- Step 05
Execute
Participants instrument prompts and responses, detect regressions, and investigate latency spikes, receiving feedback on telemetry usefulness, alert thresholds, and the evidence behind diagnoses.
- Step 06
Evaluate
We assess the observability build for useful traces, evaluations that expose failures, actionable alert routing, and a monitoring plan linked to demonstrated incident investigations.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
AI and LLM Observability Corporate Training Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request AI and LLM Observability Training for Your Team
Tell us your team size, LLM observability challenges, preferred delivery mode, and timelines. We will respond with a tailored training plan covering scope, schedule, and pricing, and confirm trainer availability.