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Mlops Engineer
A comprehensive guide for Mlops Engineer
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1
PROGRAMMING SKILLS
2
SOFTWARE ENGINEERING PRINCIPLES
3
MACHINE LEARNING FUNDAMENTALS
4
DATA ENGINEERING AND MANAGEMENT
5
CONTAINERIZATION AND ORCHESTRATION
6
CLOUD PLATFORMS AND INFRASTRUCTURE
7
MODEL TRACKING AND VERSIONING
8
MODEL OPTIMIZATION AND DEPLOYMENT
9
MONITORING, OBSERVABILITY, AND SECURITY
10
ADVANCED ML SYSTEMS AND LLMOPS
Mlops Engineer
Python
Advanced Data
Structures and
Pydantic
Object-Oriented
Programming
OOP
Asynchronous
Programming
Packaging and
Dependency Management
Poetry
pip
virtualenv
Profiling and
Performance
Optimization
Bash/Shell Scripting
Linux Command Line
Basics
File System Operations
Process Management
SQL
Advanced Queries
CTEs
Window Functions
Database Normalization
Query Optimization
Advanced/Systems Languages (Optional)
C++
for high-performance
inference
Go / Rust
for building
infrastructure tooling
Version Control (Git)
Branching Strategies
GitFlow
Trunk-based
Resolving Merge
Conflicts
Git Hooks
Testing
Unit Testing
pytest
unittest
Integration Testing
Mocking and Patching
Property-based Testing
Continuous Integration/Continuous Deployment (CI/CD)
GitHub Actions
GitLab CI/CD
Jenkins
Machine Learning CI/CD
CML - Continuous
Machine Learning
Code Quality
Linting and Formatting
flake8
black
isort
ruff
Type Hinting
mypy
Design Patterns
System Design
Microservices
Architecture
Event-Driven
Architecture
Scalability and High
Availability
Supervised and Unsupervised Learning
Regression and
Classification Models
Clustering Algorithms
Tree-based Models
XGBoost
LightGBM
Deep Learning Basics
Neural Networks
Fundamentals
PyTorch and TensorFlow
Basics
Large Language Models (LLMs)
Transformer
Architecture
Pre-training and
Fine-tuning
Evaluation Metrics
Precision, Recall,
F1-Score
ROC-AUC
Mean Squared Error
(MSE) and R-Squared
LLM Metrics
ROUGE
BLEU
Perplexity
Data Versioning
Data Version Control
DVC
Pachyderm
Data Pipelines and Orchestration
Apache Airflow
Prefect
Dagster
Mage.ai
Data Annotation and Labeling
Label Studio
Argilla
Programmatic Labeling
Snorkel
Data Quality and Validation
Great Expectations
Pandera
Deequ
Feature Stores
Feast
Hopsworks
Amazon SageMaker
Feature Store
Big Data Technologies
Apache Spark
Apache Kafka and
Stream Processing
Apache Flink
Data Lakes and Data
Warehouses
Snowflake
BigQuery
Databricks
Containerization
Docker Fundamentals
Docker Compose
Multi-stage Builds and
Optimization
Distroless Containers
Container Orchestration
Kubernetes (K8s)
Architecture
Pods, Deployments, and
Services
Helm Charts
Kustomize
End-to-End MLOps Platforms
Kubeflow Architecture
and Pipelines
ZenML
Metaflow
Cloud Providers (AWS/GCP/Azure)
Identity and Access
Management
IAM
Object Storage
S3
GCS
Compute Services
EC2
GKE
EKS
Vertex AI
SageMaker
Serverless
Architectures
Hardware and Accelerators
GPUs and TPUs
CUDA and NVML Basics
GPU Cloud Providers
RunPod
Modal
Lambda Labs
Infrastructure as Code (IaC)
Terraform
Pulumi
Ansible
Cost Optimization (FinOps)
Instance Types and
Pricing Models
Spot/Preemptible
Auto-scaling and
Resource Allocation
Cloud Cost Monitoring
Experiment Tracking
MLflow Tracking
Weights & Biases
W&B
Neptune.ai
Comet.ml
Model Registry
MLflow Model Registry
SageMaker Model
Registry
Model Optimization
Format Conversion
ONNX
Quantization
PTQ
QAT
Pruning and Knowledge
Distillation
Compilers and Runtimes
TensorRT
OpenVINO
API Development
FastAPI
Flask / Django
gRPC vs REST
GraphQL
Model Serving Frameworks
TensorFlow Serving
TorchServe
NVIDIA Triton
Inference Server
Ray Serve
BentoML
Deployment Patterns
Batch Inference
Online / Real-time
Inference
Edge Deployment / IoT
Infrastructure Monitoring
Prometheus
Grafana
ELK Stack
Elasticsearch
Logstash
Kibana
Machine Learning Monitoring
Data Drift and Concept
Drift
Evidently AI
Arize AI
Alibi Detect
Fiddler
Logging, Tracing, and Alerting
Distributed Tracing
Jaeger
OpenTelemetry
PagerDuty Integration
Model Security and Governance
Adversarial Robustness
Data Privacy and
Anonymization
Bias and Fairness
Auditing
Model Explainability
SHAP
LIME
Regulatory Compliance
AI Act
GDPR
HIPAA
Continuous Training (CT)
Automated Retraining
Pipelines
Triggers and Feedback
Loops
Deployment Strategies
A/B Testing
Canary Releases
Shadow Deployment
Distributed Training
Data Parallelism
DDP
FSDP
Model Parallelism
Pipeline
Tensor
Ray
Distributed Computing
MPI / NCCL
Generative AI and LLMOps
Retrieval-Augmented
Generation
RAG
Vector Databases
Milvus
Pinecone
Qdrant
Weaviate
Parameter-Efficient
Fine-Tuning
LoRA
QLoRA
PEFT
LLM Serving Frameworks
vLLM
TGI
Ollama
Prompt Engineering and
Versioning
LangChain
LlamaIndex
LLM Evaluation
Ragas
TruLens
LLM-as-a-judge
Agentic Workflows
LangGraph
AutoGen
CrewAI