MLOps
43 items tagged with "mlops"
Best Practices4
Feature Store Best Practices
A centralized system for defining, storing, and serving machine learning features consistently for training and inference, avoiding skew and duplicated work.
MLOps Principles
The discipline of applying DevOps and engineering rigor to machine learning so models are built, deployed, monitored, and retrained reliably and reproducibly.
ML Model Monitoring and Drift Detection
Continuously tracking deployed ML models for performance decay, data drift, and concept drift so degradation is caught and corrected before it harms outcomes.
Data Version Control (DVC)
Versioning datasets, models, and ML pipelines alongside code so experiments are reproducible, using Git for metadata and external storage for large files.
Blueprints4
Batch ML Scoring to Real-Time Model Serving Blueprint
Move from nightly batch ML scoring to low-latency online model serving with a model server, feature lookups, and autoscaling.
Self-Hosted LLM to Managed Inference Blueprint
Migrate self-hosted GPU LLM serving to a managed inference API to cut operational burden while controlling cost, latency, and data governance.
Notebooks to MLOps Pipeline Blueprint
Productionize ad-hoc data science notebooks into reproducible, versioned MLOps pipelines with CI/CD, tracking, and automated retraining.
Classic ML to Feature Store and Serving Blueprint
Migrate ad-hoc feature engineering for classic ML models to a central feature store with consistent offline training and online serving.
Reference Architectures11
Feature Store Platform
A feature store on GCP providing consistent online and offline ML features with point-in-time correctness via Feast.
Real-Time Model Serving on GCP
A reference design for low-latency online inference on GCP using Vertex AI endpoints, autoscaling, and a feature lookup path for sub-100ms predictions.
Batch Inference Pipeline on Azure
A reference design for scheduled, large-scale offline scoring on Azure using Synapse, Azure ML batch endpoints, and a data lake for inputs and results.
End-to-End MLOps Platform on Kubernetes
A reference design for a portable MLOps platform on Kubernetes covering pipelines, experiment tracking, a model registry, serving, and monitoring.
Feature Store and Online Serving on AWS
A reference design for a dual offline/online feature store on AWS that keeps training and serving features consistent and serves them at low latency.
LLM Fine-Tuning Pipeline on GCP
A reference design for fine-tuning open LLMs on GCP using Vertex AI custom training, parameter-efficient methods, and an evaluation gate before deployment.
Recommendation System on AWS
A reference design for a two-stage recommender on AWS combining candidate retrieval and ranking, with streaming feedback and real-time serving.
Real-Time Fraud Detection on GCP
A reference design for streaming fraud detection on GCP that scores transactions in milliseconds using a feature store, rules, and an ML model.
Computer Vision Inference Pipeline on Azure
A reference design for an image and video computer-vision pipeline on Azure spanning ingestion, GPU inference, and human-in-the-loop review.
Streaming ML Feature Pipeline on GCP
A reference design for a real-time feature engineering pipeline on GCP that computes streaming aggregates and serves them to online models consistently.
Data Labeling and Training Platform on AWS
A reference design for a closed-loop data labeling and training platform on AWS that turns raw data into labeled datasets and continuously retrains models.
Playbooks5
Feature Store Program Playbook
Build a feature store to unify ML feature engineering, ensure online and offline consistency, and enable feature reuse across teams.
MLOps Platform Build Playbook
A phased program to stand up an end-to-end MLOps platform covering feature stores, training pipelines, model registry, and automated deployment.
Model Serving Migration Playbook
A phased program to migrate ML model serving from bespoke endpoints to a standardized, autoscaling, observable serving platform.
Real-Time Inference Program Playbook
A program to deliver low-latency, high-throughput real-time ML inference with autoscaling, feature freshness, and strict SLOs.
Feature Store Rollout Playbook
A program to roll out a feature store that unifies feature engineering for training and serving with consistency and reuse.
Checklists2
ML Model Deployment Checklist
Pre-flight verification for promoting a trained machine learning model into a production serving environment.
MLOps Pipeline Review Checklist
Audit items for assessing the maturity, reproducibility, and automation of an end-to-end machine learning operations pipeline.
Stacks7
MLOps Stack
MLflow, Kubeflow, TensorFlow, Feature Store - ML lifecycle
MLflow MLOps Stack
End-to-end MLOps pattern using MLflow for experiment tracking, model registry, packaging, and deployment, integrated with feature, data, and serving layers.
Kubeflow ML Platform
Kubernetes-native ML platform: Kubeflow Pipelines, training operators, KServe serving, and Katib tuning run the ML lifecycle on Kubernetes.
Feast Feature Store Stack
Feature store pattern using Feast to define, materialize, and serve consistent ML features from an offline warehouse and a low-latency online store.
Vertex AI Pipeline
A managed MLOps stack on Google Cloud using Vertex AI Pipelines to orchestrate training, evaluation, and deployment of ML models.
Amazon SageMaker MLOps
A managed MLOps stack on AWS using Amazon SageMaker to build, train, deploy, and monitor machine learning models end to end.
Tecton Feature Store
A production feature platform stack using Tecton to define, compute, and serve consistent ML features for training and real-time inference.
Comparisons7
Batch vs Real-Time Inference
Batch inference processes data in scheduled bulk jobs; real-time inference serves predictions on demand. They trade latency against throughput, cost, and complexity.
CPU vs GPU Inference
CPUs and GPUs both run ML inference. GPUs excel at parallel, large-model workloads; CPUs are cheaper and simpler for small models and low concurrency.
MLflow vs Weights & Biases
MLflow is an open-source ML lifecycle platform; Weights & Biases is a polished experiment-tracking SaaS. The choice trades self-hosted breadth against managed experience.
Kubeflow vs SageMaker
Kubeflow is an open-source ML platform on Kubernetes; Amazon SageMaker is AWS's managed ML service. The choice trades portability and control against managed convenience.
Feature Store: Build vs Buy
Teams can build a custom feature store or adopt a managed or open-source one. The choice trades control and fit against time-to-value and maintenance burden.
Self-Hosted vs Managed Inference
Self-hosted inference runs models on your own infrastructure; managed inference uses a provider's endpoint. The choice trades control, privacy, and cost-at-scale against simplicity.
DVC vs Git LFS
DVC and Git LFS both version large files alongside Git. DVC targets ML data and pipelines; Git LFS is a general-purpose large-file extension.
FAQs2
What is inference in machine learning?
Inference is the phase where a trained model is used to make predictions or generate output on new inputs, as opposed to training where the model lear...
What is MLOps?
MLOps is a set of practices for reliably building, deploying, monitoring, and maintaining machine learning systems in production, applying DevOps prin...