What is MLOps and Katonic ?

Content:

• What is MLOps?
• Why do we need MLops?
• How MLOps is different from DevOps?
• what is Katonic?

What is MLOps?

MLOps stands for Machine Learning Operations, it is an engineering discipline that aims to unify ML system development(Dev) and ML system deployment(Ops) in order to standardize and streamline the continuous delivery of high-performing models in production.

The complete MLOps process includes three broad phases of “Designing the ML-powered application”, “ML Experimentation and Development”, and “ML Operations”.

The first phase is devoted to:

• Business understanding
• Data understanding
• Designing ML software

Objective of MLops team

The objective of an MLOps team is to automate the deployment of ML models into the core software system or as a service component. This means, to automate the complete ML-workflow steps without any manual intervention.

Automation

To adopt MLOps, we see three levels of automation, starting from the initial level with manual model training and deployment, up to running both ML and CI/CD pipelines automatically.

• Manual process
• ML pipeline automation
• CI/CD pipeline automation

Manual Process:

This is a typical data science process, which is performed at the beginning of implementing ML. This level has an experimental and iterative nature. Every step in each pipeline, such as data preparation and validation, model training and testing, are executed manually. The common way to process is to use Rapid Application Development (RAD) tools, such as Jupyter Notebook

ML pipleline automation:

The next level includes the execution of model training automatically. We introduce here the continuous training of the model. Whenever new data is available, the process of model retraining is triggered. This level of automation also includes data and model validation steps.

CI/CD pipeline automation:

In the final stage, we introduce a CI/CD system to perform fast and reliable ML model deployments in production. The core difference from the previous step is that we now automatically build, test, and deploy the Data, ML Model, and the ML training pipeline components.

Why do we use MLOps?

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the machine learning development lifecycle. MLOps is the discipline of integrating ML workloads into release management, CI/CD, and operations. MLOps requires the integration of software development, operations, data engineering, and data science.

DevOps vs MLOps

DevOps

• Team comprises of DevOps Engineers
• Linear Development cycle
• Versioning of only code
• Projects are not compute intensive
• CI comprises of testing and validating the code
• Monitor throughout, latency, CPU utilization etc
• DevOps is Continuous Integration and Continuous Development

MLOps

• Diverse team including data scientists, ML engineers, data engineers and DevOps
• Interative development cycle
• Versioning of code, data, features, environments etc.
• Projects are compute intensive
• CI comprises of testing and validating code and data
• CD comprises of deploying multiple pipelines
• Monitor model accuracy, data drift, features along with the basic health checks
• MLOps is Continuous Integration, Continuous Development and Continuous Training

what is Katonic?

Katonic is a MLOps Platform that combines the creative scientific process of data scientists with the professional software engineering process to build and deploy Machine Learning Models into production safely, quickly, and in a sustainable way.

It is a collection of cloud-native tools for all of the stages of MDLC (data exploration, feature preparation, model training/tuning, model serving, model testing, and model versioning). Katonic provides a Unified UI and has tooling that allows these traditionally separate tools to work seamlessly together. An important part of this tooling is the pipeline system, which allows users to build integrated end-to-end pipelines that connect all components of their MDLC.
For detail

There are challenges that are faced by chief data and analytics officers:

• I want my Data Science team to deliver value
• I need to tie my innovation budget to production solution
• Data Scientist struggles to go from small sample data to full size data
• Maintaining data science is production is nightmare
• Our Engineers and Data Scientist don't speak the same language
• Our data scientist only use notebook. Can we deploy them in productions.
• We have an extensive existing infrastructure and none of the solution on the market work on it.

These challenges are solved by the Katonic by:

• Enabling the deployment of models in production with secure, scalable, highly available endpoint deployment with out of the box auto-scaling, and load balancing.
• Allowing data scientists to focus on the core task of building ML models to improve business outcomes rather than managing infrastructure.
• And much more

Reference

• MLOps Principles
• Katonic

Hope you find this blog insightful ✨

Happy Learning! 🌟