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Cloud NLP Services: Pre-trained Language Models as an API
Cloud NLP services are like having a team of linguists on-demand via an API. Use them to instantly analyze text for sentiment, entities (people, places), or topics without building your own models.
Cloud Computer Vision: Renting an AI's Eyes via API
Think of it as an API that lets your app 'see.' You send an image and get back structured data like object labels or text. It's used for content moderation, digitizing documents, or making photo libraries searchable.
MLOps: Applying DevOps to Machine Learning
MLOps applies DevOps principles to automate the machine learning lifecycle, creating an assembly line for models. It's for moving from notebooks to production systems that retrain automatically.
Feature Store: The Single Source of Truth for ML
A feature store is the single source of truth for ML models, acting as a central kitchen for prepped ingredients (features). It's used to ensure the same feature logic is applied in both training and real-time inference, preventing model drift.
Hyperparameter Tuning for LLM Inference
Control an LLM's creativity versus predictability by tweaking its inference parameters. This is crucial for tasks like generating structured JSON versus creative text. The footgun is changing parameters without a clear goal, leading to chaotic output.
ML Inference Endpoint: The API for Your Model
An ML inference endpoint is the stable API URL your application calls to get predictions. It separates the public URL from the underlying model, letting you swap models without changing client code.
Distributed Model Training: Splitting the Workload
Don't wait for one GPU to finish; use many. Distributed training splits a model's workload across multiple processors to finish faster. It's essential for massive deep learning models.
Model Drift: When Good Models Go Bad
A model is a snapshot of the world; model drift is the alarm that fires when the world changes but your snapshot has not. It detects when production data no longer statistically matches the training data, a common issue for models predicting user behavior.
Explainable AI (XAI): Why Did the Model Do That?
Explainable AI (XAI) translates a model's 'black box' decision into a human-readable reason. Use it to debug predictions, build user trust, or meet regulatory needs. The footgun: explanations are approximations of the model's logic, not absolute truth.
Cloud Adoption Framework: Your Org's Cloud Migration Playbook
A Cloud Adoption Framework (CAF) is an organization's pre-flight checklist for moving to the cloud. It provides a structured path for strategy, planning, and governance, ensuring alignment during a large-scale migration.
The 6 R's: Your Playbook for Cloud Migration
The 6 R's are a strategic menu for migrating apps to the cloud. When planning a move, you use it to decide whether to simply 'Rehost' an app, 'Refactor' it for performance, or even 'Retire' it.
AWS Well-Architected Framework: A Blueprint for Cloud Health
Think of it as a pre-flight checklist for your cloud architecture. It provides a consistent way to evaluate your systems against six pillars—like security and cost optimization—to ensure they are sound.
Cloud Business Case: Justifying Your Move to the Cloud
A cloud business case translates technical goals into financial terms like ROI and TCO to win executive support. It's the "why" and "how much" for a migration, used to secure budget and sponsorship.
Cloud Center of Excellence (CCoE): Your Internal Cloud Guides
A Cloud Center of Excellence (CCoE) is your company's internal cloud consulting team, centralizing expertise to guide adoption. It creates reusable patterns, manages costs, and ensures security, helping large orgs scale.
Cloud Landing Zone: A Blueprint for Cloud Environments
A Cloud Landing Zone is a pre-configured, secure foundation for your cloud applications, like a city grid with utilities ready for new buildings. It provides shared services like networking and identity, ensuring consistency for large organizations.
What is Cloud Native Architecture?
Cloud Native refers to a collection of practices and open-source projects, like Kubernetes, governed by the CNCF. It's used by enterprises to build modern products and services.
Circuit Breaker Pattern: Fail Fast, Not Hard
A circuit breaker wraps network calls to prevent cascading failures. It monitors for errors, and if a service seems down, it 'trips' to fail requests instantly without hitting the network. This gives the failing service time to recover.
The Sidecar Pattern: Offload and Isolate Application Logic
The Sidecar Pattern attaches a helper container to your main application, like a sidecar on a motorcycle. It offloads tasks like logging or proxying, letting you add features without changing the main app's code. The footgun is over-engineering a solution.
The Ambassador Pattern: Your App's Diplomatic Sidecar
The Ambassador pattern places a proxy next to your application to handle its network communication, like a diplomat. This adds modern features like monitoring, security, and retries to legacy apps or across languages without changing app code.
Cloud Governance: Rules for Your Cloud Kingdom
Cloud governance is like city planning for your cloud, setting automated rules to prevent chaos. It's used to control costs by blocking expensive VMs and enforce security with required settings.