Containers package an application with its dependencies into a lightweight, portable unit that runs consistently across environments. Unlike virtual machines, containers share the host kernel and isolate processes using namespaces and control their resource usage with cgroups, which keeps startup times low and density high.

An image is a layered, read-only filesystem built from ordered instructions; a container is a runtime instantiation of that image with writable state layered on top. Layers use union filesystems so only changes consume disk space, and images are cached to speed up repeated builds and deployments.

Efficient images are small and explicit. Use minimal base images (for example, distroless or alpine where appropriate), employ multi-stage builds to remove build-time artifacts, and pin base image versions to avoid accidental drift. Keep Dockerfiles declarative: one responsibility per image, clear ENTRYPOINT/CMD, and meaningful labels for automation.

Run containers with least privilege: avoid running processes as root, drop unnecessary capabilities, and declare resource limits (cpu, memory). Store persistent data in volumes rather than inside containers, and treat environment variables as configuration while keeping secrets out of images—use secret stores or runtime injection.

Operational hygiene matters. Include health checks so orchestrators can restart unhealthy services, emit structured logs to stdout/stderr for centralized collection, and prefer immutable deployments. For networking, prefer service discovery via orchestration layers rather than baking IPs into images.

Designing Robust RESTful APIs

RESTful APIs model server-side entities as resources with well-structured URIs and rely on HTTP semantics to express intent. Keep endpoints noun-focused (for example, /orders, /users/{id}) and use HTTP verbs to convey actions: GET for retrieval, POST for creation, PUT/PATCH for updates, and DELETE for removal.

Status codes are your contract with clients. Return 200 for successful GETs, 201 for created resources with a Location header, 204 for successful requests with no body, 400 for client errors, 401 for authentication, 403 for forbidden, 404 for not found, and 500+ for server errors. Use consistent error response bodies that include an error code, message, and optional details.

Design for scale and usability. Implement pagination, sorting, and filtering for list endpoints and support partial responses via fields selection or sparse fieldsets. Use standard patterns for versioning—URI versioning (e.g., /v1/) or content negotiation—and document breaking changes clearly.

Security and idempotency matter. Protect endpoints with appropriate authentication (OAuth 2.0, JWT) and authorization checks, validate inputs server-side, and make non-safe operations idempotent when possible. Employ caching headers (Cache-Control, ETag) to reduce load and use HTTPS everywhere to protect data in transit.

Machine Learning Evaluation: Metrics and Methodology That Matter

Evaluation starts with data partitioning. Reserve a test set for final evaluation, and use train/validation splits or cross-validation for model selection and hyperparameter tuning. A strict separation prevents optimistic bias and supports reproducible comparisons.

Choose metrics that reflect the real objective. For classification, accuracy is simple but can mislead on imbalanced data; prefer precision, recall, F1-score, and ROC AUC depending on whether false positives or false negatives are more costly. For regression, use MAE for interpretability, RMSE when large errors should be penalized, and R² to quantify explained variance.

Confusion matrices reveal error patterns: which classes are confused and whether thresholds need adjustment. ROC and precision-recall curves show performance across thresholds; use PR curves when positives are rare. For probabilistic models, check calibration—well-calibrated probabilities improve decision-making.

Guard against overfitting with regularization, simpler models, and more data. Learning curves expose whether adding data or capacity will help. Always compare models to a sensible baseline (random, majority class, or simple heuristic) and report uncertainty—confidence intervals or repeated cross-validation—so results reflect variability, not luck.

Secure Password Storage: Practical Guidance

Never store plaintext passwords. Instead, store a salted, slow hash so stolen credentials are expensive to crack. Salting prevents attackers from using precomputed tables; a unique per-password salt is essential.

Use purpose-built password hashing algorithms: Argon2 or bcrypt are recommended. They provide tunable cost parameters (time, memory, iterations) so you can increase work factors as hardware advances. Avoid general-purpose hashes like MD5 or SHA1 for passwords; they are too fast and susceptible to brute-force attacks.

A typical workflow: generate a strong random salt, hash the password with Argon2 or bcrypt using appropriate parameters, and store the algorithm identifier, salt, and hash together. Optionally, a server-side pepper (a secret kept outside the database) can add an extra layer of defense against database leaks.

Operational protections complement hashing. Apply rate limiting and multi-factor authentication to reduce brute-force attempts, monitor login anomalies, and enforce secure password reset flows that use time-limited, single-use tokens. When migrating legacy hashes, detect algorithm type, rehash on next successful login, and phase out weak schemes.

Example (conceptual) bcrypt usage in Python:

import bcrypt
password = b"correcthorsebatterystaple"
salt = bcrypt.gensalt(rounds=12)
hashed = bcrypt.hashpw(password, salt)
# Store hashed; verify with bcrypt.checkpw(entered, stored)