Roadmap (Data/ML/AI/Cloud/DevOps/SRE)

By: Mregojos GitHub

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Navigation

• Roles
• Fundamentals / Prerequisites
• Data Engineering
• Machine Learning / Artificial Intelligence Engineering
• Cloud Engineering and Architecture
• DevOps
• SRE
• Management
• How
• Resources

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Roles

• Cloud Engineer/Architect Path: Fundamentals -> Cloud Engineering and Architecture (Focus on Cloud Infrastructure and Architecture)
• DevOps Engineer Path: Fundamentals -> Cloud Engineering and Architecture -> DevOps (Focus on CI/CD Pipelines, Agility and Faster Delivery)
• Site Reliability Engineer Path: Fundamentals -> Cloud Engineering and Architecture -> DevOps -> SRE (Focus on Scalability, Reliability, Availability, Stability, and Efficiency of the System)
• Data Engineer Path: Fundamentals -> Fundamentals -> Cloud Engineering and Architecture -> Data Engineering (Focus on Data Pipelines)
• AI/ML Engineer Path: Fundamentals -> Fundamentals -> Cloud Engineering and Architecture -> Data Engineering -> DevOps SRE -> Machine Learning / Artificial Intelligence (Focus on Intelligent Systems)
• Cloud Cybersecurity Engineer: Fundamentals -> Cloud Engineering and Architecture -> Cloud Security (Focus on Cloud Security, Privacy, and Compliance)

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• Tasks: Design, Build, Operationalize, Deploy, Troubleshoot, Monitor, Maintain, Secure, Optimize, Automate
• Focus on learning: Concepts, Technologies, Tools, Best Practices, Use Cases, Architecture Framework

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* Fundamentals 🔝

• System Administration (Operating System, Database, Networking, Security)
• Programming Language for Automation (General-Purpose, Scripting, Querying)
• Version Control (VC) / Source Code Management (SCM)
• Computer Science Fundamentals

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* Data Engineering 🔝

Data Analytics

• Spreadsheets
• Structured Query Language (SQL)
• Relational Database Management System (RDMS)
• Data Visualization
• Data Reporting

Data Engineering with Python

• Data Manipulation & Exploration
• Big Data Processing
• Data Workflow Orchestration
• Other Python Libraries

Modern Data Stack

• Data Integration
• Data Transformation
• Data Warehouse

Data Pipeline

• Extract, Transform, Load
• Extract, Load, Transform
• Extract, Load, Transform, Load

Data Processing Systems

• Batch
• Streaming
• Hybrid

NoSQL (Not only SQL)

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* Machine Learning / Artificial Intelligence Engineering 🔝

Data Science Workflow

• Case Study
• Business Understanding
• Data Understanding
• Data Preparation
• Exploratory Data Analysis
• Modeling
• Model Evaluation
• Deployment

Math

Data Science with Python

Machine Learning

Deep Learning

Domain Knowledge

A/B Testing

Research

MLOps

ModelOps

AIOps

Generative AI

Prompt Engineering

Multimodal

AI Applications

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* Cloud Engineering and Architecture 🔝

• Cloud Service Provider
• Cloud Computing Important Services (Compute, Storage, Database, Networking, Security, Serverless)
• Cloud Architecture
  • System Design
  • Cost Optimization
  • Operational Excellence
  • Reliability
  • Performance Optimization
  • Security, Privacy, and Compliance
• Infrastucture as Code
• Configuration Management

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* DevOps (Plan -> Code -> Build -> Test -> Release -> Deploy -> Operate -> Monitor) 🔝

• Containerization
• Container Orchestration
• Continuous Integration / Continuous Deliver (CI/CD)
• Continuous Deployment
• Continuous Testing
  • Unit Test
  • Integration Test
  • Load Test
  • Security Test
• Development
  • Web Tier / Presentation Tier
  • Application Tier / (Business) Logic Tier
  • Data Tier / Database Tier

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* Site Reliability Engineering (SRE): Scalable, Reliable, Efficient 🔝

• Four Golden Signal
  • Latency
  • Traffic
  • Error
  • Saturation
• Service Levels
  • Service Level Indicator
  • Service Level Objective
  • Service Level Agreement
• Observability
  • Logs
  • Metrics
  • Trace
• Logging
• Monitoring
• Alerting
• Incident Response and Management

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* Management 🔝

Project Management

• Traditional
• Agile

Data Management and Governance

Model Management and Governance

Cloud and DevOps Management and Governance

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How? Tips? 🔝

• ⬆️ Choose your tech role (and starts with why?)
• ⬆️ Learn (Theory + Hands-on Pratice); use AI tools and Prompt Engineering to power up your learning.
• ⬆️ Portfolio (Projects, Tutorials, Docs, Posts, Evidence to show, etc.)
• ⬆️ Industry Certifications
• ⬆️ Interview Preparations (Resume, Technical Interviews, Non-Tech / Behavioral Interviews)
• ⬆️ Networking
• ⬆️ Research (Tech is changing and evolving rapidly)
• ⬆️ Apply
• ⬆️ You can do it.

Important Skills: Communication, Collaboration, Problem-Solving, Learning, Adaptability

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Resources 🔝

• Tech Stack for Cloud, DevOps, and SRE: https://github.com/Mregojos/tech-stack
• Tech Stack for Data and ML/AI: https://github.com/Mregojos/tech-stack-data-ml
• Project Collections: https://github.com/Mregojos/MRegojos#my-project-collection