Kortical MLOps - Deployment

Enterprise AI and ML creation, deployment and MLOps made easy

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Easy to use apps for better business outcomes

Users don’t consume machine learning models – they consume machine learning apps and services.

Build ML apps and models easily and quickly

Kortical’s groundbreaking ML deployment platform makes it easy to build machine learning models and the apps that use them.

Apply your expertise

It's designed to be super simple to get started while also being expert-friendly. It has full code and transparency, so you can change anything and build whatever you want.

Do more with less, faster

The idea is to knock out a cool POC ML app, all you'll need are:

  • 1 data scientist
  • 1 front-end Developer
  • 1 week
This would typically be ready to deploy live to production in 1 month for a relatively straightforward ML app.

Build

Deploy

MLOps

Build

Steps to create your ML App with Kortical

Create a project and environments in the platform

Create your ML project - give it a name

Install the Kortical Python package

Install the Kortical MLOps package

Select your app template to start from

Select your ML App template

Open the code, make whatever changes you like

There is a lot of code that you can tweak to make your ML Model and ML App function how you wish or you can leave the code as it is

Build your ML model in code or build model in the platform

  • Exploratory Data Analysis
  • Data Cleaning / Feature Creation
  • AutoML
  • Model Explainability
  • Automatic Experiment Tracking
Full AI platform, Exploratory Data Analysis, AutoML, Explainable AI, MLOps and more

Run `kortical app deploy` (this takes your local code and puts it in the cloud)

Run Kortical app deploy and there will be the dashboard with your ML model and app in the cloud

Check out your app

Examples of ML app providing predictions and explanations for your users - here is an example of the NHS platelet prediction and Auto Finance renewals platform

Deploy

Steps to put your app live in prod

Create a git repo for your app

Create git repo for your ML App

Commit the code

Commit the code of the ML App

Check all the continuous integration tests have passed

MLOps CI/CD integration tests

Click promote to UAT

Promote ML App to production

Click promote to Prod

Promote ML App to prod

MLOps

Steps to maintain your ML solutions

Check your dashboard / notifications to keep everything running smoothly

  • Versions tracking and governance
  • One click deployment and rollback for collections of apps and models
  • Multiple environments for staged deployment
  • Continuous integration / deployment
  • ML Apps that meet the strictest enterprise SLAs
  • Manage model / environment hardware via simple interface

Check your models

  • Data drift
  • Accuracy drift
  • Business case drift
  • Explainability
  • Automatic version tracking
MLOps platform and MLOps dashboard, where you can assign compute, see the ML model scores, ML performance

Get in touch

Whether you're just starting your AI journey or looking for support in improving your existing delivery capability, please reach out.

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