TensorFlow

TensorFlow — end-to-end ML platform with Keras high-level API.

AnalyticsComplex

Stack Components

FrameworkTensorFlow 2.x

APIKeras 3

Why

Production deployment ecosystem, TF Serving, TFLite for mobile, TPU support. PostHog and Plausible offer privacy-first analytics without GDPR consent banners, growing 200% in 2024.

BigQuery and ClickHouse process petabyte-scale data at $5/TB scanned.

When

Use for production ML at scale. Avoid for research preferring PyTorch.

How

1. pip install tensorflow 2. Build model with Keras 3. Compile and fit 4. Export with tf.saved_model

Example

import tensorflow as tf model = tf.keras.Sequential([tf.keras.layers.Dense(1)]) model.compile(optimizer='adam', loss='mse')

Common Problems

⚠GPU not detected — install tensorflow[and-cuda]
⚠Graph mode errors — use tf.function carefully
⚠Configuration drift between environments — use infrastructure-as-code (Terraform/Pulumi) to keep dev/staging/prod identical

Best Practices

✓Use Keras 3 for multi-backend support
✓Profile with tf.profiler for bottlenecks
✓Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit

FAQ

What is TensorFlow and what projects is it for?

TensorFlow — end-to-end ML platform with Keras high-level API. It pairs layered technologies for end-to-end type safety and out-of-the-box deploys, fitting everything from MVPs to production SaaS.

How do I spin up a working demo with TensorFlow in 10 minutes?

Two steps: (1) pip install tensorflow; (2) Build model with Keras. Get the local dev server running, then wire one real data source to verify the end-to-end loop.

What is the most common pitfall with TensorFlow and how do I avoid it?

The most common pitfall is: GPU not detected — install tensorflow[and-cuda]. Avoid it by adding the matching checklist to your PR template and running type-check + lint + unit tests in CI — three gates before merge.

What are the best practices for TensorFlow?

Best practices for TensorFlow include: Use Keras 3 for multi-backend support; Profile with tf.profiler for bottlenecks; Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit. The first one — "Use Keras 3 for multi-backend support" — should be set up at project init to avoid costly rework later.

When should I NOT choose TensorFlow?

Pick something else in three cases: (1) the team has zero experience with the core framework and the deadline is tight (learning-curve risk); (2) it's a pure content/marketing site (Astro is lighter); (3) you need very high throughput (>10k RPS) or strict-consistency microservices (Go/Rust). TensorFlow shines for mid-to-high complexity full-stack and API work — don't over-engineer simple static pages with it.