scaling and
sustaining

intelligence with
MLOps 

The major challenges in ML Development

AI is scaling at a faster rate than ever before. Every day, new organizations are introducing AI into their business for various reasons – to serve customers better and help their teams perform productively. But there are two significant problems today:

Building AI is time consuming

When building an AI model, you are dealing with many components and people. The more variables you have, the more complex and slower your model development process will be.

Scaling AI is unsustainable

You are not going to build one model and just deploy it into the real world. As you gain more insights, you will develop more and more models. However, each model need not be started from nothing.

Bringing Agility &
Sustainability intoML Model Development.

MLOps is the machine learning version of DevOps, which is altered to address some ML components like the changing data and the addition of new roles in development - ML engineers, data architects.

MLOps breaks the silos in ML model development and creates an agile system similar to DevOps. This system defines every individual involved’s exact roles and responsibilities and provides fluidity for working together.

MLOps also brings the ability to reproduce components from one model – say pipelines, training algorithms, etc. – to another model. This reproducibility enables organizations to ship models faster since they can now reuse components from their previous models.

//- the missing component in MLOps 

MLOps has multiple components that keep the ML model development lifecycle functional. However, most organizations are missing out on a crucial component of MLOps - Model Monitoring.

Data and Model Versioning
Feature Management & Storing
Automation of Pipelines and Processes
CI/CD for Machine Learning
Continuous Monitoring of Models

Versioning is the process of uniquely naming multiple iterations of a model that is created at different stages of ML development. It primarily helps in retracking a model to a previous iteration when faced with an adversary 

Features are the data variables that are selected from the raw data to efficiently identify and solve the business problem using AI. Feature stores are repositories of such variables that can be used while building new AI models.

An ML pipeline usually refers to a process that orchestrates the end-to-end flow of data–raw data, features, model inputs, model outputs. Automating such a pipeline enables the extraction of data, processing it, and storing it at a data lake or warehouse before feeding into the model.

Continuous Integration (CI) enables teams to simultaneously work together and upload code, data, or features into a central repository. Continuous Delivery (CD) automates the deployment of the above elements into production by eliminating manual and multi-stage tasks.

Continuous Model Monitoring refers to keeping a watch on the models in production so that they stay consistent with their performance while delivering the set business objectives.
‍
Some of the primary reasons behind the insufficient focus on monitoring are the lack of specialized knowledge, complexities with running ad-hoc scripts, and a time-consuming process due to irreproducible setup.

Monitoring + Accountability + Explainability
=‍
AI Observability

When models are increasingly being integrated into our lives to make simple as well as complicated decisions, we need to go beyond monitoring. We need to understand models and build accountability into them.

As we scale intelligence, we will also need to break down obstacles to understanding AI and create trust in order for it to be more widely adopted. This can be accomplished by combining monitoring with accountability and transparency.

//- building the future with Censius AI Observability

Censius AI Observability Platform combines monitoring, accountability, and explainability in ML.

It completes your MLOps tool stack as an ideal model monitoring platform that not just helps you monitor models but also explains its decisions and builds trust around your AI.

Monitor Drift
Monitor Types
Model Behavior
Censius AI Platform

Monitors Drift and More

Continuously monitors models for drifts, data changes, performance metrics, and alerts model owner of the same.

Measures What Matters

Supports tracking and measuring up to 12 metrics like Precision, Recall, Specificity, Sensitivity, and more.

Explains Model Behavior

Provides a root cause analysis that identifies the what, how, and where of all the changes detected in the model.

Censius AI Observability Platform makes it effortless to monitor the pipeline, analyze issues, and explain models. Works with any model and any platform.

Explore Platform
Monitor Drift

Monitors Drift and More

Continuously monitors models for drifts, data changes, performance metrics, and alerts model owner of the same.

Monitor Types

Measures What Matters

Supports tracking and measuring up to 12 metrics like Precision, Recall, Specificity, Sensitivity, and more.

Model Behavior

Explains Model Behavior

Provides a root cause analysis that identifies the what, how, and where of all the changes detected in the model.

Censius AI Platform

Censius AI Observability Platform makes it effortless to monitor the pipeline, analyze issues, and explain models. Works with any model and any platform.

Explore Platform

Censius automates model monitoring

so that you can 

boost healthcare

improve models

scale businesses

detect frauds

boost healthcare

improve models

scale businesses

detect frauds

boost healthcare

Start Monitoring