MLOps for LLMs Corporate Training
Learn to fine-tune, evaluate, and serve large language models in production, in live guided labs.
Edstellar, a corporate training company, delivers corporate training that helps your team learn to build production-grade LLM pipelines. Employees develop fine-tuning, evaluation, and inference serving skills through hands-on labs that mirror day-to-day work.
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 a production-ready LLM team
The Edstellar LLM OPERATIONS Capability Model
Diagnose
Map your team's current ML platform skills and pinpoint which LLM use cases are ready for fine-tuning and production deployment.
Standardize
Establish dataset versioning, evaluation suites, and experiment tracking so every LLM build stays reproducible and auditable.
Practice in labs
Work through fine-tuning, guardrail, and inference serving labs on shared test infrastructure with instructor coaching.
Measure
Track evaluation scores, latency, cost, and guardrail signals with dashboards your team builds and owns.
Scale
Roll out CI/CD, autoscaling, and monitoring patterns that keep LLM services reliable and cost-efficient under load.
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Program Overview: What is MLOps Corporate Training for LLMs?
MLOps Corporate Training for LLMs is Edstellar's instructor-led technical program for ML engineers and platform teams taking large language models from fine-tuning experiments into production. Participants work through the LLM lifecycle, data engineering for training, fine-tuning with PEFT, evaluation and guardrails, and inference serving at scale. The focus stays on the operating discipline that keeps LLM systems reliable, safe, and affordable in production.
Training is hands-on from the first module. Participants prepare training data and run fine-tuning jobs with parameter-efficient methods in guided labs, then wire evaluation harnesses and guardrails around the resulting models. Instructors coach your team through inference serving, scaling decisions, and cost control against realistic load scenarios, reviewing every exercise with direct feedback. The program closes with a capstone: a production LLM platform build.
LLM projects frequently stall between a promising fine-tune and a system anyone trusts: evaluations done once, guardrails bolted on late, and inference bills that arrive as surprises. This program teaches your engineers to build the whole production loop, from data to deployment to monitoring. Every technique is practiced in labs, so the learning transfers directly to your LLM initiatives.
The capstone is a production LLM platform your team builds and defends: a fine-tuned model with an evaluation suite, guardrails, serving infrastructure, and cost instrumentation, all documented for the next team to run. Your engineers return treating LLM systems as products with lifecycles, not experiments with demos.
Six modules and 24 hours: LLM foundations and the MLOps lifecycle, data engineering for training, fine-tuning and PEFT labs, evaluation and guardrails, inference serving and scaling, and a capstone production platform build. Every module pairs concepts with a guided lab, so each technique is practiced on realistic workloads. It closes with a capstone the team plans, builds, and presents, ending with a runbook handoff. Delivery is hybrid across virtual and onsite sessions, scheduled around your calendar.
What's Included in this Workshop for Your Enterprise Teams
- 1
LLM foundations
Cover architectures, tokenization, and the ops lifecycle.
- 2
Data engineering labs
Curate, version, and quality-check training datasets.
- 3
Fine-tuning labs
Adapt models with LoRA and QLoRA techniques.
- 4
Evaluation and guardrails
Benchmark models and wire safety guardrails.
- 5
Capstone platform build
Ship an endpoint with monitoring and a runbook.
- Instructor-led delivery across virtual, onsite, and hybrid
- Guided labs in every module with instructor coaching
- Capstone LLM platform build, reviewed with your team
- Flexible scheduling around your calendar
- Lab guides and reusable templates included
- Group size and batches set along with you
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
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- Hands-On Labs on Live Infrastructure, Led by Industry Experts
MLOps for LLMs Course Topics and Program Outline
A 24-hour instructor-led program across six modules, each pairing concept sessions with a guided lab. Content is tailored to your stack, so every lab mirrors the workflows your team runs in production.
Built by Edstellar with LLM platform engineers
Built by practitioners who fine-tune, evaluate, and serve large language models in production, shaped around your stack and use cases.
We assess your team's ML platform skills and LLM use cases up front, so labs and the capstone match your tools and goals.
Instructor-led sessions cover LLM architectures, data pipelines, and the operations lifecycle with live demonstrations.
Guided labs walk your team through fine-tuning, evaluation, and guardrail builds on shared test infrastructure.
Your team ships a production-style LLM serving pipeline in the capstone and takes home repeatable runbooks.
From first fine-tune to a monitored production endpoint in 24 hours.
Delivered as
The program runs in hybrid, onsite, or live virtual formats, with every session led by an instructor and all labs on shared test infrastructure.
Tokenize sample prompts and register a first model build in a shared experiment tracker.
learna)Transformer architectures and how LLMs learn from text
b)Tokenization, context windows, and inference basics
c)Pretraining, fine-tuning, and prompting compared
d)The end-to-end LLM operations lifecycle
Build a versioned instruction dataset and run an automated quality report on it.
learna)Curating instruction and preference datasets
b)Data quality checks: dedup, filtering, and bias scans
c)Dataset versioning and lineage for audit trails
d)Privacy and licensing rules for training data
Fine-tune a small open model with LoRA and compare checkpoints on a held-out set.
practicea)Full fine-tuning vs parameter-efficient methods
b)LoRA and QLoRA configuration in practice
c)Hyperparameter sweeps and experiment tracking
d)Checkpointing, resuming, and comparing runs
Score a fine-tuned model on an eval suite and wire guardrails around its outputs.
practicea)Benchmarks and task-specific eval suites
b)LLM-as-judge and human review workflows
c)Red-teaming and hallucination detection
d)Guardrail pipelines for inputs and outputs
Serve a quantized model behind an autoscaled endpoint and load-test it against a latency budget.
applya)High-throughput inference servers and batching
b)Quantization: INT8 and INT4 deployment trade-offs
c)Autoscaling, caching, and latency budgets
d)Observability, cost dashboards, and alerting
Ship a complete pipeline from dataset to monitored endpoint and present the runbook to the cohort.
applya)Design review: pipeline architecture and trade-offs
b)Build: data pipeline to served endpoint
c)Evaluation and guardrail sign-off
d)Demo: deployment walkthrough and runbook handoff
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
Practical outcomes your team takes back to day-to-day work: from dataset engineering and fine-tuning to serving, guardrails, and production monitoring.
LLM lifecycle management
Plan and run the full lifecycle of an LLM initiative, from data readiness and training to deployment and retirement.
Dataset engineering for LLMs
Curate, version, and quality-check instruction datasets that make fine-tuning reproducible and auditable.
Parameter-efficient fine-tuning
Apply LoRA and QLoRA techniques to adapt open models efficiently, tracking every experiment.
LLM evaluation
Design benchmark suites and judge-based reviews that measure quality, safety, and regressions before release.
Guardrail engineering
Build input and output guardrails that reduce hallucinations and keep model behavior within policy.
Inference serving and scaling
Deploy quantized models behind autoscaled endpoints, tuned to latency budgets and cost targets.
Production monitoring
Instrument LLM services with dashboards and alerts covering quality drift, latency, and spend.
Teams that Benefit from this MLOps for LLMs Group Training
This group training is built for data science and ML teams moving large language models from pilots to production. Content adapts to each cohort's roles, tools, and experience, so every batch practices the workflows it owns, from dataset engineering to serving.
Ship fine-tuned models with confidence
For ML engineers who own model training and adaptation. Practice PEFT techniques, experiment tracking, and checkpoint management in guided labs.
Workflow friction
- Fine-tuning runs that are hard to reproduce
- Unclear which adaptation method fits the task
- Checkpoints scattered across local machines
How the program helps
- Run LoRA and QLoRA fine-tuning end to end
- Track experiments with full reproducibility
- Compare checkpoints on held-out eval sets
Practice use cases
- LoRA fine-tuning labs
- Experiment tracking setup
- Checkpoint comparison drills
Operate LLM infrastructure at scale
For platform engineers who run inference and training infrastructure. Practice serving, autoscaling, and cost control on realistic workloads.
Workflow friction
- GPU costs growing faster than usage
- Latency spikes under production load
- No shared serving standards across teams
How the program helps
- Serve models behind autoscaled endpoints
- Apply quantization for cost-efficient inference
- Build latency and cost dashboards
Practice use cases
- Inference serving labs
- Autoscaling configuration
- Cost dashboard builds
Feed LLMs with trusted datasets
For data engineers who build the pipelines behind training and evaluation. Practice dataset curation, versioning, and quality checks.
Workflow friction
- Training data with unknown provenance
- Manual dataset prep slowing experiments
- No lineage when audits arrive
How the program helps
- Version datasets with full lineage
- Run deduplication and bias scans
- Prepare eval-ready data splits
Practice use cases
- Dataset versioning labs
- Quality scan pipelines
- Lineage documentation
Put LLMs into real products
For applied AI and product teams shipping LLM features. Practice evaluation, guardrails, and responsible deployment patterns.
Workflow friction
- Hallucinations reaching end users
- No systematic way to measure quality
- Releases without safety sign-off
How the program helps
- Design eval suites for product tasks
- Wire guardrails around model outputs
- Plan safe rollout and rollback
Practice use cases
- Eval suite design
- Guardrail wiring labs
- Release checklist builds
Cohorts train as a cross-functional workforce, with each small group aligned on shared data, evaluation, and serving standards.
Prerequisites: This program benefits ML engineers, data engineers, platform engineers, and applied AI teams who build or operate language model workloads. Python and basic machine learning familiarity help but are not required, and no prior LLM fine-tuning experience is needed. Content adapts to each cohort's tools, models, and infrastructure.
ML engineer to LLM platform owner
Each skill row shows where a learner starts and where the program takes them, mapped to the modules that teach it.
One program that grows with ML careers, from foundational LLM literacy to confident production platform ownership.
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 format is instructor-led, with guided labs on shared test infrastructure and scheduling set around your team.
Hybrid Training
Blend live virtual sessions with onsite lab days. Concepts land online while intensive build sessions happen with the instructor in the room.
- Mix virtual sessions with onsite lab days
- Instructor-led throughout the program
- Guided labs on shared test infrastructure
- Flexible scheduling around your calendar
Onsite Training
Run the full program at your office with the instructor alongside your team. Labs use your stack context so practice mirrors production.
- Instructor delivers at your office
- Labs shaped around your stack
- Team practices on shared infrastructure
- Scheduling set with your calendar
Live Virtual Training
Join live virtual sessions led by an instructor, with the same guided labs and capstone as onsite. Breakout rooms keep small groups focused.
- Live instructor-led virtual sessions
- Same guided labs as onsite delivery
- Small-group breakout collaboration
- Session recordings and lab guides included
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, goals, and timelines through the lead form. We respond with a tailored training plan and a quote for your batch.
Yes. A discovery call helps us understand your LLM use cases and stack, so we can tailor the labs and capstone to your team.
Yes. The program runs live virtual, onsite, or hybrid. All formats are instructor-led with the same guided labs and capstone.
Yes. Onsite delivery brings the instructor to your office, with labs shaped around your stack and scheduled around your calendar.
The program runs 24 hours across six modules, typically delivered over three to four days or split into shorter sessions.
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.
LLMOps comes after your team can engineer machine learning code properly, and before observability and cost work. Clean, tested ML engineering makes pipelines possible; LLMOps then versions, deploys, and updates language models, while monitoring and cost tuning keep them reliable and affordable.
Why Choose Edstellar as Your MLOps for LLMs Training Provider
Edstellar partners with data science and ML teams to build production LLM capability through instructor-led, lab-driven programs.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Labs and the capstone are shaped around your models, datasets, and serving stack, so practice mirrors your production reality.
Proven Methodology
A learn, practice, and apply arc moves teams from LLM concepts to a shipped platform build with instructor review at each stage.
Practice-Based Learning
Guided labs in every module put fine-tuning, evaluation, guardrails, and serving into your team's hands from day one.
Expert Trainers
Practitioners who operate LLMs in production lead your sessions, bringing real GPU budgets and deployment trade-offs to the room.
Global Scale
Hybrid, onsite, and live virtual delivery lets you train distributed teams on one consistent program with aligned standards.
Measurable Outcomes
Evaluation checkpoints and a reviewed capstone build give you concrete evidence of skills your team can apply immediately.
Build LLM Delivery Pipelines With Evaluated Models and Observable Serving Behavior
Edstellar connects language-model development with the operational evidence needed to review a complete pipeline. Participants version instruction data, fine-tune a small model, and evaluate checkpoints before applying guardrails and load-testing inference. The final endpoint and runbook are assessed for reproducibility, measured behavior, and transparent deployment and cost trade-offs.
- Step 01
Consult
We assess Python and machine-learning familiarity alongside workload goals to identify gaps in dataset versioning, parameter-efficient tuning, model evaluation, guardrails, and inference operations.
- Step 02
Plan
We establish LLM lifecycle and tracking foundations before dataset engineering and LoRA tuning, then develop evaluation, guarded serving, and monitoring in the integrated pipeline.
- Step 03
Align
We align LLM engineering and platform expertise with ML, data, and infrastructure roles, calibrating guided support to participants' coding experience and available model environments.
- Step 04
Customize
Sample instruction datasets, prompts, and latency targets reflect relevant application needs, supporting tailored evaluation and serving exercises without using confidential training material or operational endpoints.
- Step 05
Execute
Participants version datasets, compare fine-tuning checkpoints, apply output guardrails, and load-test quantized serving, receiving feedback on evaluation quality, latency behavior, and reproducible configuration.
- Step 06
Evaluate
We assess the dataset-to-endpoint pipeline for traceable versions, held-out evaluation results, tested guardrails, measured serving behavior, and a runbook that explains monitoring and operational limits.
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?
MLOps Corporate Training for LLMs 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 MLOps for LLMs Training for Your Team
Share your team size, LLM use cases, tools, and timelines to receive a tailored training plan and quote. Book a discovery call to discuss your goals; we confirm availability and respond with a proposal.