Corporate DevOps Training in Greater Noida: A Complete Guide

import pandas as pd

# Example survey: each person rates skills 1-5 (made-up practice data)
survey = pd.DataFrame({
    "person":     ["P1", "P2", "P3", "P4"],
    "git":        [4, 3, 5, 2],
    "ci_cd":      [2, 2, 3, 1],
    "docker":     [3, 1, 2, 2],
    "kubernetes": [1, 1, 2, 1],
    "terraform":  [1, 2, 1, 1],
})
target = 3
summary = survey.drop(columns="person").agg(["mean", "min"]).T
summary["below_target"] = (survey.drop(columns="person") < target).sum()
print(summary.sort_values("mean"))

Training goals (example):

By the end of training and four follow-up weeks:
1. Every team member can add a test stage to the CI pipeline.
2. The main service runs in a container in staging.
3. Two engineers can deploy and roll back on Kubernetes.
4. One environment is managed with Terraform.
5. Dashboards and alerts cover the main service.

#!/usr/bin/env bash
# Each participant runs this before day one
set -u
for tool in git docker kubectl kind terraform; do
  if command -v "$tool" >/dev/null 2>&1; then
    echo "OK      $tool"
  else
    echo "MISSING $tool"
  fi
done
docker info >/dev/null 2>&1 && echo "OK      docker daemon running" || echo "MISSING docker daemon"

Workshop series (example: 8 sessions, 3 hours each)

1. Git workflows and pull requests
2. CI pipelines: build, test, scan
3. Docker: images, best practices
4. Kubernetes basics: deployments, services
5. Kubernetes operations: probes, rollouts, debugging
6. Terraform basics and state
7. Monitoring and alerting
8. Capstone: team pipeline from commit to monitored deploy

# Compare delivery measures before and after training (example values)
before = {"deploys_per_week": 2, "failed_changes_%": 25, "restore_hours": 6}
after = {"deploys_per_week": 0, "failed_changes_%": 0, "restore_hours": 0}
# Fill 'after' with real data from your tools 4-8 weeks after training.

for k in before:
    if after[k]:
        print(f"{k:18} before {before[k]:>4}  after {after[k]:>4}")
    else:
        print(f"{k:18} before {before[k]:>4}  after (collect data)")

Ask each provider for a written quote that states:

[ ] Number of sessions and total hours
[ ] Share of hands-on lab time
[ ] Lab environments (who provides, any cloud costs)
[ ] Syllabus adapted to our stack
[ ] Trainer profile and a demo session
[ ] Materials and recordings
[ ] Follow-up support period
[ ] Total fee, taxes and payment terms

Company / team: ______________   Location: Greater Noida
Participants: ____   Roles: ______________
Current stack: ______________
Top skills gaps (from survey): ______________
Goals (things people will do at work): ______________
Format: onsite / live online / hybrid
Schedule: ______________
Success measures: ______________

Criteria              Provider 1   Provider 2   Provider 3
Syllabus fit          ____         ____         ____
Lab share             ____         ____         ____
Stack adaptation      ____         ____         ____
Trainer demo          ____         ____         ____
Follow-up support     ____         ____         ____
Written quote clear   ____         ____         ____

Weeks 1-2 after training: each person applies one lab to a real task
Weeks 3-4: team reviews progress in a short meeting
Week 6: compare delivery measures with the baseline
Week 8: decide on follow-up sessions for remaining gaps