Innovative Skills

Course Description

Master DevOps and MLOps with this beginner-friendly course designed for software developers and aspiring engineers. Learn the essentials of Linux, Git, CI/CD, Docker, Kubernetes, and Cloud Deployment, then move into MLOps fundamentals, model versioning, experiment tracking, model deployment with Flask/FastAPI, and monitoring with MLflow & Evidently AI.

By the end of this course, you’ll be able to automate workflows, deploy ML models in production, monitor performance, and build scalable systems using industry-standard tools like Jenkins, GitHub Actions, Terraform, Prometheus, and Grafana. Perfect for developers, data scientists, and fresh graduates looking to upskill in DevOps & MLOps.

Milestone 1: Foundation

DevOps culture & principles (CLAMS).
Agile & Scrum with GitHub Projects.
Linux fundamentals (CLI. file systems. networking).
Git & GitHub (version control. PR workflow. gitflow. and trunk-based development).

Milestone 2: CI/CD Fundamentals

CI/CD pipeline concepts (build → test → deploy).
GitHub Actions: basic to modular workflows.
Build automation(npm. Python setup tools).
Unit testing(pytest. Jest).

Milestone 3: Build & Artifact Management

Docker fundamentals (images. containers. Dockerfile. Docker Compose)
Artifact registry: GitHub Packages. DockerHub/ECR.

Milestone 4: Container Orchestration & IaC

Kubernetes basics (Pods. Deployments. Services. Ingress. CRDs)
Config and package management: ConfigMaps. Secrets. Helm.
Infrastructure as Code: Terraform (AWS/DigitalOcean).
Configuration management: Ansible(optional for provisioning demos).

Milestone 5: Monitoring, Logging & Security

Observability concepts.
Prometheus & Grafana for monitoring.
ELK / LGTM stack.
DevSecOps basics: Trivy (container scanning). SonarQube/SonarCloud.
Secrets management (Vault/Infisical/AWS Secrets Manager)

Milestone 6: Cloud & Deployment Strategies

Cloud concepts.
Cloud: AWS/DigitalOcean free-tier setup.
Deploying with CI/CD to the cloud.
Strategies: Blue-Green. Canary. Rolling updates.
Serverless basics (AWS Lambda via CLI or serverless framework with CICD).

Milestone 7: Advanced DevOps

GitOps basics with Flux v2.
Site Reliability Engineering concepts: SLIs. SLOs. SLAs. Error Budgets.
Chaos Engineering intro.
Cost optimization in the cloud via usage insights & monitoring.

Milestone 8: MLOps Fundamentals

What is MLOps & how it extends DevOps
MLOps lifecycle (Data → Model → Deploy → Monitor → Retrain)
Challenges and drift overview (data drift. model drift. reproducibility)
Data versioning with DVC + Git LFS/S3/NFS

Milestone 9: Experiment Tracking & Model Packaging

Experiment tracking (MLflow. Weights & Biases)
Model packaging (Pickle. TorchScript)
Environment reproducibility (Conda/Poetry + Docker for ML)

Milestone 10: CI/CD for ML

ML pipeline automation: GitHub Actions.
Data validation: GX
TFX.
Set up CI pipelines with model test gates.

Milestone 11: Model Deployment

Inference types: batch vs real-time
Model API: REST/gRPC. FastAPI
Model Serving: TorchServe
BentoML
Deploy on Kubernetes: KFServing intro (basic demo only)
Optional: Edge & Serverless deployment context

Milestone 12: Monitoring & Model Management

Model monitoring: logs. metrics(latency. accuracy. failure rates).
Model/data drift detection: Evidently AI
loop: Retraining automation demo + logging.

Milestone 13: Cloud MLOps

Managed platforms: SageMaker (focus) / GCP Vertex AI and Azure ML overview
Model registry with MLflow.
Scalable inference + cost insights from SageMaker endpoints.

Salman Md Sultan

Education

M.Sc. in Information and Communication Engineering, Chosun University, Gwangju, South Korea. CGPA: 4.25 out of 4.50, passing year: 2021., CGPA: 4.25 out of 4.50,
B.Sc. in Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh. CGPA: 3.75 out of 4.00, passing year: 2017., CGPA: 3.75 out of 4.00,
Higher Secondary Certificate (HSC): Science, Government Laboratory High School, Dhaka. GPA: 4.40 out of 5, passing year: 2013., GPA: 4.40 out of 5,
Secondary School Certificate (SSC): Science, Government Laboratory High School, Dhaka. GPA: 4.69 out of 5, passing year: 2011., GPA: 4.69 out of 5,

IT Industrial Experience (6 Years)

No.	Institution	Designation	Duration
1	Innovative Skills Ltd.	Chief Executive Officer (CEO)	2023-Continue
2	Gachon University	External AI Researcher	01/05/2023-Continue
3	European IT	Team Leader (R&D)	01/02/22-30/04/2023
4	TISCON	Python Developer	01/10/21-31/01/22
5	Chosun University	AI Research Programmer	03/09/19-31/08/21
6	Robi Axiata Limited	Report Analyst	10/04/18-30/09/18

Project/Research

2022-2023: Development of Collective Collaboration Intelligence Framework for Internet of Autonomous Things, South Korea. [RSSI, Localization, Data Analysis, Supervised Learning]
2023 [Running]: Geobag Counting using Deep Learning. [Yolov3, Django, MySQL, Bootstrap], jQuery]
2022-2023: Employee Attendance System using Computer Vision [OpenCV, Yolov3, Django, Firebase]
2021-2022: Intelligent platform for Indoor Spatial Data Infrastructure Based on Crowd-sourcing Behavior, South Korea. [RSSI, Localization, Data Analysis, Supervised Learning]
2020-2022: Smart city urban infrastructure air quality real-time monitoring and prediction platform technology development, South Korea. [Tensorflow, Deep Reinforcement Learning, LSTM, Drone Navigation, AQI Data Analysis]
2021: Employee Attendance System using Computer Vision [Dlib, Face_recognition API]
2021: Exam Proctoring System using Computer Vision [Dlib, Face_recognition, Yolov3]
2019-2021: Development of Autonomous Collaborative Swarm Intelligence Technologies, South Korea. [Tensorflow, Google Dev. Board, Deep Reinforcement Learning, LSTM, Dense, IoT]