This is a compilation of resources including URLs and papers appearing in lecture videos. If you wish to dive more deeply into the topics covered this week, feel free to check out these optional references.
NoSQL Databases: Google Cloud Memorystore
Google Cloud Firestore
Google Cloud Bigtable
Amazon DynamoDB
MobileNets: MobileNets
Serving Systems: Clipper
TensorFlow Serving
Model Serving Architecture: Model Server Architecture
TensorFlow Serving
NVIDIA Triton Inference Server
Torch Serve
Kubeflow Serving
Scaling Infrastructure: Container Orchestration
Kubernetes
Docker Swarm
Kubeflow
Online Inference: Batch vs. Online Inference
Batch Processing with ETL: Kafka ML
Pub Sub
Cloud DataFlow
Apache Spark
Experiment Tracking and Management: Tracking
Management
nbconvert
nbdime
jupytext
neptune-notebooks
git
Tools for Data Versioning: Neptune
Pachyderm
Delta Lake
Git LFS
DoIt
lakeFS
DVC
ML-Metadata
Tooling for Teams: Image Summaries
neptune-ai
Vertex TensorBoard
MLOps: Continuous delivery and automation pipelines in machine learning
Orchestrated Workflows with TFX: Creating a Custom TFX Component
Building Fully Custom Components
Continuous and Progressive Delivery: Progressive Delivery
Continuous, Incremental, & Progressive Delivery
Deployment Strategies
Blue/Green Deployment
A/B Testing
Hidden Technical Debt in Machine Learning Systems
Monitoring Machine Learning Models in Production
Google Cloud Monitoring
Amazon CloudWatch
Azure Monitor
Dapper
Jaeger
Zipkin
Vertex Prediction
Vertex Labelling Service
How “Anonymous” is Anonymized Data?
Pseudonymization