async function deployPipeline(config) {
const { env, region, replicas } = config;
await k8s.apply({ namespace: env, region });
return scale({ replicas });
}interface AIModel {
endpoint: string;
temperature: number;
stream: boolean;
invoke(prompt: string): Promise<Response>;
}SELECT u.id, u.email,
COUNT(s.id) AS sessions,
SUM(r.revenue) AS mrr
FROM users u
JOIN sessions s ON s.user_id = u.id
JOIN revenue r ON r.user_id = u.id
GROUP BY u.id
ORDER BY mrr DESC;
const pipeline = [
vectorize(embedding_model),
retrieve(top_k=8),
rerank(cross_encoder),
generate(llm, temperature=0.3),
];
@app.route("/api/v2/infer")
async def infer(req: InferRequest):
model = await load_model(req.id)
return await model.predict(req.input)export const config = {
regions: ["us-east-1","eu-west-1"],
autoscale: { min: 2, max: 50 },
healthCheck: "/api/health",
timeout: 30_000,
};class NeuralEngine:
def __init__(self, layers, lr=1e-4):
self.model = build(layers)
self.optim = AdamW(lr=lr)
def fit(self, X, y, epochs=100):
for e in range(epochs):
loss = self.step(X, y)mutation CreateDeployment($input: DeployInput!) {
createDeployment(input: $input) {
id
status
endpoint
createdAt
}
}