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Description
Maintain your usual routine while working within Jupyter Notebooks or any Python setting. Just invoke modelbi.deploy to launch your model, allowing Modelbit to manage it — along with all associated dependencies — in a production environment. Machine learning models deployed via Modelbit can be accessed directly from your data warehouse with the same simplicity as invoking a SQL function. Additionally, they can be accessed as a REST endpoint directly from your application. Modelbit is integrated with your git repository, whether it's GitHub, GitLab, or a custom solution. It supports code review processes, CI/CD pipelines, pull requests, and merge requests, enabling you to incorporate your entire git workflow into your Python machine learning models. This platform offers seamless integration with tools like Hex, DeepNote, Noteable, and others, allowing you to transition your model directly from your preferred cloud notebook into a production setting. If you find managing VPC configurations and IAM roles cumbersome, you can effortlessly redeploy your SageMaker models to Modelbit. Experience immediate advantages from Modelbit's platform utilizing the models you have already developed, and streamline your machine learning deployment process like never before.
Description
The ultimate cron manager you've been searching for.
Effortlessly deploy human or AI-generated Python scripts on a schedule without any infrastructure requirements.
STEPS:
1. BYOC (Bring Your Own Code): If your code is already prepared in VS Code or Jupyter Notebook, simply copy and paste it into Xq1. Alternatively, you can utilize ChatGPT to create your code by prompting, "Write a Python code for ....," and then pasting the output into Xq1.
2. Execute your code: Launch your code on Xq1, which will install any necessary packages, create a container, and execute it. If the execution is error-free, you are ready to proceed.
3. Assign a name and choose a schedule: Give your code (cron) a recognizable name for easier identification upon deployment. Then, specify your preferred schedule or frequency for running the code.
4. Deploy: Click the deploy button, and Xq1 will handle the deployment of your container while scheduling it according to your selected frequency. You can monitor each execution in real-time through the 'Cron Monitor' interface on Xq1.
5. Enjoy seamless automation: Now you can sit back and relax as your scripts run automatically, freeing up your time for other essential tasks while ensuring your projects progress consistently.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Amazon Redshift
Databricks
Deepnote
GitHub
GitLab
Google Colab
Jupyter Notebook
PyTorch
Snowflake
TensorFlow
Integrations
Amazon Redshift
Databricks
Deepnote
GitHub
GitLab
Google Colab
Jupyter Notebook
PyTorch
Snowflake
TensorFlow
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$9
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Modelbit
Founded
2022
Country
United States
Website
www.modelbit.com
Vendor Details
Company Name
Xquantum
Founded
2024
Country
India
Website
xquantum.in
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization