More in DevOps & Cloud — page 44
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pre-trained AI Services: Renting Expertise, Not Building It
Pre-trained AI services are like renting an expert's brain via an API. Instead of training your own model, you call a service for tasks like speech-to-text or image analysis. The footgun is assuming a general model will master your specific industry jargon.
AutoML: Automating the Machine Learning Workflow
AutoML automates the repetitive, trial-and-error parts of building a machine learning model, like picking the best algorithm and tuning its settings. Use it to quickly build baseline models or when your team lacks deep ML expertise.
Managed ML Platforms: Heroku for Machine Learning
A managed ML platform is like Heroku for machine learning, providing an integrated environment for the entire model lifecycle. Use it to build, train, and deploy models without managing servers, data pipelines, or inference endpoints yourself.
Data Virtualization: One Query, Many Sources
Data virtualization creates a single logical database from many physical sources without moving the data. It's used for real-time integration across silos like SQL, NoSQL, and APIs.
Data Mesh: From Central Data Lake to Distributed Ownership
Data Mesh decentralizes data ownership, moving it from a central team to the business domains that create it. This approach, like microservices for data, is for orgs where a monolithic data lake has become a bottleneck.
Apache Iceberg: A Table Format for Huge Datasets
Apache Iceberg is an open table format for huge analytic datasets. It adds a metadata layer to files in object storage, enabling engines like Spark and Trino to work with transactional guarantees. The footgun: it's a format, not a query engine itself.
Apache Beam: Write-Once, Run-Anywhere Data Pipelines
Apache Beam is a universal remote for big data engines. You write your pipeline logic once using its SDK, and it translates your code to run on different "runners" like Spark or Flink. The footgun is thinking Beam is an engine; it's an abstraction that.
Apache Flink: Unifying Batch and Stream Processing
Apache Flink treats everything as a stream of data, even finite batches. This unified model lets you process real-time events and historical data with the same logic. Use it for live analytics or fraud detection.
OLAP Cube: Pre-Aggregating Data for Fast Analysis
An OLAP cube is like a Rubik's Cube for your data, pre-calculating answers to complex business questions. It powers BI tools, letting you 'slice and dice' sales data by region and time for fast reports. The footgun: data is typically stale, not real-time.
Lambda Architecture: Batch and Stream for Big Data
Lambda Architecture splits data into two paths: a slow, comprehensive batch layer and a fast, real-time stream layer. It's used in big data systems needing both historical accuracy and live views.