AI/ML Infrastructure • v3.2

Train. Deploy. Scale.
Without the DevOps tax.

Technovaa is the end-to-end platform engineering teams use to ship production-grade machine learning — from experiment tracking to global inference — on a single, cloud-native runtime.

2.4B+Inference calls / mo
99.99%Uptime SLA
SOC 2Type II certified
app.technovaa.online / models / vision-v4

vision-v4 · training run #218

Loss
0.0184
▼ 12.4%
Accuracy
97.3%
▲ 1.1%
GPU util
88%
▲ 6.2%
Run
Model
Duration
Status
#218
vision-v4
4h 12m
RUN
#217
vision-v4
3h 58m
OK
#216
llm-summarizer
1h 04m
OK
#215
fraud-detect
0h 42m
DRIFT

Trusted by engineering teams at fast-growing startups

Why Technovaa

Everything you need to ship ML — nothing you don't.

Purpose-built for teams graduating from notebooks to production. Zero Kubernetes YAML. Zero infrastructure lock-in.

AutoML & Fine-tuning

Bring a dataset, get a production-ready model. Automatic hyperparameter search, distributed training across A100 & H100 clusters, and one-command fine-tuning for Llama, Mistral, and custom foundation models.

Global Inference

Deploy to 14 regions with a single flag. Sub-40ms p99 latency, autoscaling from zero.

Observability

Track drift, latency, and cost per model in real time — with automated alerts for silent failures.

Vector Search

Managed embedding + retrieval built into the platform. HNSW, IVF, hybrid BM25 out of the box.

Team Workspaces

Fine-grained RBAC, audit logs, and versioned model registries for regulated teams.

Cloud-native architecture

Runs on your VPC or ours. Native integrations with AWS SageMaker, S3, EKS, and Bedrock. Bring your own compute credits or use ours. Built by engineers who've operated ML at global scale.

Developer-first

One Python SDK. Every workload.

Train, tune, deploy, and monitor with the same primitives. No context switching. No infrastructure bookkeeping.

Explore the SDK →
# Fine-tune a foundation model in 4 lines
from technovaa import Model
model = Model.finetune(
  base="llama-3.1-70b",
  dataset="./support-tickets.jsonl",
  gpus=8, epochs=3,
)
model.deploy(region="aws-us-west-2")

# → https://api.technovaa.online/v1/llama-support
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