AI/ML Engineer

1. 1.Programming Languages

1.1. Java

1.2. Python

1.3. Kotlin

1.4. Golang

1.5. R

1.6. CUDA

1.7. C / C++

2. 2.Mathematics

2.1. Linear Algebra

2.2. Probability & Statistics

2.3. Optimization Theory

3. 3.Deep Learning Framework

3.1. Tensorflow

3.2. PyTorch

3.3. Google Jax

4. 4.Machine Learning Basics

4.1. Deep Learning

4.2. Supervised Learning

4.3. Unsupervised Learning

5. 6.Infra Basics

5.1. Linux

5.1.1. Ubuntu

5.1.2. CentOS

5.1.3. Alpine

5.2. Cloud Computing

5.2.1. Object Storage

5.2.2. Cloud GPU VMs

5.2.3. Google Vertex VI

5.2.4. AWS Sage Maker

5.2.5. Azure ML

5.3. Docker

6. 5.Computer Science Basics

6.1. Data Structure

6.2. Algorithms

6.2.1. LeetCode

6.2.2. HackerRank

6.2.3. Codewars

6.3. Database

7. 7.MLOps Basics Model Serving

7.1. Model Management

7.1.1. MLflow

7.1.2. BentoML

7.2. Model Serving

7.3. Experiment Management

8. 8.MLOps Machine Learning Pipeline

8.1. Model Monitoring

8.2. Data Validation

8.3. Model Validation

8.4. Explainable AI

8.4.1. LIME

8.4.2. SHAP

8.4.3. Interpret

9. 9.DevOps: CI/CD

9.1. Continuous Integration

9.2. Continuous Deployment

9.3. GitHub Actions

9.4. Google Cloudbuild

9.5. Jenkins

9.6. CircleCI

9.7. Spinnaker