JAX for Enterprise AI: Architecture & Integration

import jax
import jax.numpy as jnp

# Define pure functional forward loss step
def loss_fn(params, x, y):
  w, b = params
  predictions = jnp.dot(x, w) + b
  return jnp.mean((predictions - y) ** 2)

# Wrap with JIT compilation and automatic gradient derivation
@jax.jit
def update_step(params, opt_state, x, y, learning_rate=0.01):
  loss, grads = jax.value_and_grad(loss_fn)(params, x, y)
  
  # Immutable array update: new_w = w - lr * grad_w
  w, b = params
  grad_w, grad_b = grads
  new_w = w - learning_rate * grad_w
  new_b = b - learning_rate * grad_b
  
  return (new_w, new_b), loss

def run_jax_pipeline():
  # Explicit PRNG Key Initialization
  key = jax.random.PRNGKey(42)
  key_w, key_b, key_data = jax.random.split(key, 3)

  # Initialize weights and inputs
  w = jax.random.normal(key_w, (128, 1))
  b = jax.random.normal(key_b, (1,))
  params = (w, b)

  x = jax.random.normal(key_data, (1000, 128))
  y = jnp.ones((1000, 1))

  # Execute JIT-compiled step
  params, loss = update_step(params, None, x, y)
  print(f"JAX Step Loss: {loss:.6f}")

if __name__ == "__main__":
  run_jax_pipeline()