Integrations Overview
Last updated
Last updated
Vectice easily integrates with the tools you already use—from AI/ML platforms and notebooks to MLOps and data platforms—making your workflow smoother and more efficient. Our integrations are flexible and quick to set up, allowing you to start using Vectice with minimal changes to your current process.
Explore our available integrations:
Manage models, experiments, and data directly within your favorite AI/ML tools to enhance your machine learning workflows.
Streamline development using Vectice alongside your notebooks, IDEs, and CI/CD tools for a cohesive coding and deployment experience.
You can integrate Vectice within these environments by installing the Vectice library package.
You can use Vectice with an HTTP
and a HTTPS
proxy server. This is used to connect your ecosystems to the Vectice instance. To route all Vectice traffic through the proxy server, set the environment variable to the proxy URL.
Environment variables:
HTTP_PROXY
is the proxy to use for HTTP requests.
HTTPS_PROXY
is the proxy to use for HTTPS requests. In most cases, this is the same as HTTPS_PROXY
.
PROXY
is your personal proxy.
NO_PROXY
is a comma-separated list of DNS suffixes or IP addresses that can be accessed without passing through the proxy.
Simplify model management and deployment with Vectice integrations for popular MLOps platforms, automating workflows and improving consistency.
Connect Vectice with leading data storage platforms to keep your data secure and accessible, enabling seamless data handling and compliance.
To learn more about the code wrappers, visit our API reference docs.
Vectice offers powerful tools for developers who want more control over their integrations. With our APIs, you can connect directly with Vectice, automate key tasks, and customize how Vectice fits into your workflow.
The Vectice Python API makes it easy to interact with Vectice right from your Python environment. Automate tasks, document your data and track versions—directly in your code.
Key Uses:
Upload and manage data in formats like CSV or JSON
Connect with data storage (Google Cloud Storage, Amazon S3, Databricks)
Auto-document datasets, models, and notes
Track data and model versions over time
Handle errors and logging
Please refer to the dedicated installation guide to install the Vectice Python API and view the official Vectice Python API documentation for the latest details and code examples.
Use Vectice in R with the reticulate
package, bringing Vectice’s API capabilities directly to your R workflows. It’s a smooth, flexible way to extend Vectice into R.
Understand more about the reticulate package.
Please refer to the dedicated installation guide for installing reticulate
and view the official Vectice Python API documentation for the latest details and code examples.
For advanced setups, our GraphQL and REST APIs let you integrate Vectice in a custom way. Available for self-hosted deployments, these APIs are perfect for unique workflows or internal tools.
Key Uses:
Custom integrations with in-house systems
Full control over Vectice data through API access
Support for creating workflows that meet specific needs
This solution offers seamless on-demand integration. Our expert team is available to assist with advanced customization needs. Just contact us at support@vectice.com to discuss your specific requirements.
Python
R
scikit-learn
spaCy
TensorFlow
XGBoost (XGB)
Keras (K)
MLflow
Spark MLlib
PyTorch
H2O.ai
MindsDB
DataRobot
PyCaret
Ludwig
Jupyter notebook
RStudio
VS Code
GitHub Actions
Jenkins
Collab notebook
Python IDE
Atom
GitHub
MLFlow
Weights & Biases
Jenkins
Bitbucket
CodeCommit
Other git based systems
Snowflake
AWS S3
Google Cloud Storage
Azure Blob Storage
Redshift
Synapse
Databricks
BigQuery
SparkTable
Delta Table
Postgres