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.
Python
R
scikit-learn
spaCy
TensorFlow
XGBoost (XGB)
Keras (K)
MLflow
SAS Py
SAS Viya
Azure ML
Spark MLlib
PyTorch
H2O.ai
MindsDB
DataRobot
PyCaret
Ludwig
W&B
Vertex AI
Sagemaker
Streamline development using Vectice alongside your notebooks, IDEs, and CI/CD tools for a cohesive coding and deployment experience.
Jupyter notebook
RStudio
VS Code
GitHub Actions
Databricks
Jenkins
Collab notebook
Python IDE
Atom
Sagemaker
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.
GitHub
MLFlow
Weights & Biases
Jenkins
Great Expectations
Bitbucket
CodeCommit
Arize
Sagemaker
Other git based systems
Connect Vectice with leading data storage platforms to keep your data secure and accessible, enabling seamless data handling and compliance.
Snowflake
AWS S3
Google Cloud Storage
Azure Blob Storage
Redshift
Synapse
Databricks
BigQuery
SparkTable
Delta Table
Postgres
Amazon Redshift
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
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.
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.
To learn more about the code wrappers, visit our .
Please refer to the dedicated to install the Vectice Python API and view the official for the latest details and code examples.
Understand more about the package.
Please refer to the dedicated for installing reticulate and view the official for the latest details and code examples.
