ML Architecture Blog | mlai.qa - Insights for AI Startups
ML architecture insights, MLOps patterns, and data pipeline design guides for Series A–C AI startups. Practical advice from the mlai.qa team.
Databricks Alternative: Replace Databricks with Claude Code + Spark + MLflow in 2026 (Save $500K+/year)
Independent guide to replacing Databricks with self-hosted Apache Spark, MLflow, Airflow, and Claude Code. Cost …
Hire ML Engineer 2026 - Salary, MLOps Tools, Certifications, Interview Guide
Hiring ML engineers and MLOps engineers in 2026 - salary benchmarks (USD 140-380k+), MLOps platform fluency (Kubeflow, …
Prefect vs Metaflow vs Flyte vs Airflow 2026 - ML Workflow Orchestration
ML workflow orchestrators compared for 2026 - Prefect, Metaflow, Flyte, Airflow. Python-native, Kubernetes scaling, …
MLOps Platform Comparison 2026: Kubeflow vs MLflow vs SageMaker vs Vertex AI vs Databricks
MLOps platforms compared for 2026 - Kubeflow, MLflow, AWS SageMaker, Google Vertex AI, Databricks, Metaflow, Flyte, …
When to Build vs Buy Your ML Infrastructure
A framework for deciding when to build ML infrastructure from scratch vs. use managed services - with a decision matrix …
The ML Architecture Review: 20 Things We Check
The complete checklist we use in our ML architecture reviews - training infrastructure, data pipelines, model serving, …
Model Monitoring vs Observability: What ML Startups Get Wrong
The difference between monitoring and observability in ML systems - what to instrument, which tools to use, and the …
MLOps Stack Comparison: Kubeflow vs Metaflow vs Prefect
An honest comparison of the three most popular MLOps frameworks for AI startups - when to use each, setup complexity, …
ML Platform Engineering: What It Is and When You Need It
A practical guide to ML platform engineering - what it covers, when startups need it, and how to build a serving and …
ML Architecture Mistakes That Kill Series B Due Diligence
The 5 ML architecture decisions that Series B investors flag in technical due diligence - and how to fix them before …
Fine-Tuning vs RAG: How to Choose for Your AI Product
A practical decision framework for choosing between fine-tuning and retrieval-augmented generation - with cost, latency, …
Data Pipeline Architecture for Real-Time ML
Architecture patterns for building real-time ML data pipelines - streaming vs batch, feature store design, and the tools …