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.
Temporal vs Airflow 2026: Which Orchestrator Should You Use?
Temporal vs Airflow compared for 2026 - durable code-first execution for long-running, fault-tolerant workflows versus a …
SageMaker vs Vertex AI 2026: Which ML Platform Wins?
AWS SageMaker vs Google Vertex AI compared for 2026 - granular AWS-native ML control versus a unified GCP experience …
SageMaker vs Databricks 2026: Which ML Platform Should You Use?
SageMaker vs Databricks compared for 2026 - AWS-native managed ML lifecycle versus a unified multi-cloud data and AI …
Ray vs Dask 2026: Which Distributed Python Tool to Use?
Ray vs Dask compared for 2026 - a general distributed compute framework with an ML-native ecosystem versus parallel …
Feast vs Tecton 2026: Which Feature Store Should You Use?
Feast vs Tecton compared for 2026 - the open-source self-managed feature store versus a managed enterprise real-time …
DVC vs MLflow 2026: Which MLOps Tool Should You Use?
DVC vs MLflow compared for 2026 - Git-based data, model, and pipeline versioning versus experiment tracking and a model …
BentoML vs KServe 2026: Which Model Serving Tool to Use?
BentoML vs KServe compared for 2026 - Python-first model packaging you deploy anywhere versus Kubernetes-native …
Airflow vs Prefect (2026): Which Orchestrator to Pick
Airflow vs Prefect compared on DAG model, developer experience, ecosystem, self-hosting, and cost. Clear verdict on when …
Airbyte vs Fivetran 2026: Which ELT Tool Should You Use?
Airbyte vs Fivetran compared for 2026 - open-source, self-hostable ELT with custom connectors versus fully-managed, …
MLflow vs Kubeflow 2026: Which MLOps Tool Should You Use?
MLflow vs Kubeflow compared for 2026 - experiment tracking and model registry versus a full Kubernetes-native ML …
MLOps Engineer Salary vs ML Platform Cost 2026
MLOps engineer salary vs ML platform cost 2026 - fully-loaded hire numbers, managed platform pricing, and when a …
Build vs Buy RL Training Infrastructure 2026
Build vs buy RL training infrastructure - buy the foundation (GPU orchestration, rollout, serving), build only your …
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 …