Manage your AI pipeline life cycle with ModelOps.

Define machine learning and deep learning tasks

Define, create, and manage different machine learning and deep learning pipeline tasks and workflow components.

---

Easy to integrate the machine learning pipeline with your AI DevOps pipeline

Pull or push different microservices from your AI DevOps pipeline and create machine learning or deep learning pipeline tasks.

---

Support AI model version control

Version your datasets, developed models, and AI pipelines for reference, benchmarking, and improvements.

---

Stream and batch mode data analytics

Create and manage batch and stream mode analytics pipelines with few clicks.

---

Composable AI workflow

Compose and configure pipeline as a workflow based model across a directed acyclic graph(DAG).

---

AI pipeline life cycle management

Create, save, edit, deploy, run, monitor, stop, un-deploy, delete AI pipelines with few clicks.

---

Machine learning model performance monitoring

Continuously monitor the model performance, drift, decay, and trigger the retraining once the model performance declines below a threshold level.

---

Machine learning model retraining

Continuously collect the data variant records, retrain the model, and automatically deploy the new model with improved performance.

---

Distributed computing for AI pipelines

Distribute the pipeline execution across different servers to improve the horizontal scalability and performance of the pipeline.

---

Composable dashboard

Ad-hoc dashboarding helps customers to visualize the analytics output through simple drag and drop dashboard composing.

---

Index and search

Index and create search head based out of the AI pipeline output, and allow end users to perform ad-hoc queries.