AI/ML Engineers | Argonaut

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Deploying LLMs using Argonaut with ease

1. Give permissions to your cloud

Connect your AWS/GCP account to Argonaut. Using a service account key, we securely manage your resources and environments.

2. Create a kubernetes cluster with GPUs

Create GPU-enabled Kubernetes clusters with UI or using raw yaml values.

3. Pick model to deploy from Huggingface or Replicate

Pick your model from Huggingface or Replicate and deploy. Fully customizable infra and deployments.

💽 Self-host vector databases

Self-host vector databases like Weaviate and Chroma. Enjoy benefits of reduced latency and increased control over data security and privacy.

Self host vector database like Weaviate and Chroma

📊 Autoscaling with KEDA

Argonaut handles the autoscaling for your apps with KEDA with complete flexibility of scaling strategy customizable to the requirements of each workload.

📓 Your choice of tools

Bring your tools of choice to your cloud environment. Easily host Jupyter Notebooks, Airflow, dbt and more.

Provision and leverage GPU for your workloads

⏩ Kubernetes clusters with GPU support

Deploy and manage dedicated GPUs in your clusters via AWS and GCP in one click.

Integrate with vector databases

Self host chroma, weaviate in minutes or use the hosted versions

Autoscale your apps

Customizable austoscaling strategies that work out of the box

Choose your tools

Integrate with tools like airflow, dbt, and others, easily

GPU nodes in k8s

Speed up your training with dedicated GPUs

Instead of constantly thinking about infrastructure, CI/CD processes, and hiring a DevOps engineer for these, we only spend time developing our applications. Argonaut has increased our productivity and made us much faster. Because DevOps is overhead.

Kartal

Lead Developer at Bhuman.ai