awaBerry for Data Scientists & AI/ML Engineers
Who This Is For
Built for Data & ML Teams Working With Local Compute
You build and run machine learning models, data pipelines, and analytical workflows. Your data often lives on edge devices, on-premise servers, or distributed machines — not in a cloud bucket where a managed notebook can reach it. You spend time moving data around, fighting access restrictions, and maintaining infrastructure that should be invisible.
The Challenges
The Problems You Face
Your Data Is Distributed, But Your Compute Is Not
Edge devices generate the data. Your GPU workstation does the heavy lifting. Getting data from one to the other securely and automatically requires infrastructure that most teams build from scratch every time.
Running Models on Remote Machines Is Friction-Heavy
SSH tunnels, Jupyter port-forwarding, credential management — every remote compute session involves setup overhead that has nothing to do with your actual research.
AI Agents Cannot Reach Local Machines
Your LLM agents can call APIs, but they cannot execute commands on your local machines, read local data files, or write results back — without building a custom integration from scratch.
Research Literature Is Impossible to Keep Up With
Staying current across ML, cloud infrastructure, and your domain specialty requires monitoring dozens of sources. It does not happen consistently without a system.
How awaBerry Helps
Your Data, Compute & Agents — One Coherent Workflow
awaBerry connects your distributed infrastructure into a unified platform that your tools, your team, and your AI agents can all reach securely.
Agentic API — Give Your AI Agents Hands
Integrates directly as an MCP server. Your AI agents authenticate with a Project Key and gain scoped access to local machines: execute pre-approved commands, trigger model training scripts, read input datasets from specific directories, write results back — without ever touching the rest of the filesystem.
Explore Agentic API →awaBerry Anywhere — Access Your GPU Workstation From Anywhere
Full remote desktop (VNC/RDP) to your GPU machine — launch training runs, monitor progress, review visualizations from any location. Web-to-Local access to your local Jupyter server, MLflow dashboard, or monitoring tools. SSH terminal for notebook servers and model execution.
Explore Anywhere →Smart Automation — Data Pipelines That Run Themselves
Schedule scripts that fetch from distributed edge devices, normalize formats, and feed your training pipeline automatically. Daily arXiv digests matching your keywords. Nightly validated, encrypted, cloud-synced backups of your experiment databases — generated from plain English, zero maintenance.
Explore Automation →Why It Works
Two Products. One Unified Platform.
Your Models Run Where the Data Lives
awaBerry Anywhere puts your GPU workstation one click away from anywhere. No cloud bills for compute you already own. No VPN. No port-forwarding. Your local Jupyter and MLflow environments become securely reachable URLs.
Your Agents Have Hands. Your Pipelines Run Themselves.
The Agentic API gives AI agents permissioned access to your machines via MCP. The Automation Platform turns plain-English pipeline descriptions into self-running scripts — one generation cost, zero per-execution cost.
Summary
Your Compute Is Always Reachable. Your Agents Have Hands.
awaBerry connects distributed data, powerful local compute, and AI agents into a coherent workflow — without cloud lock-in, without VPNs, and without infrastructure projects that distract from the actual research. Your models run where the data lives. Your agents have hands. Your compute is always reachable.
