What is ModelOps and How it works.
                     WHAT IS MODELOPS 
AND HOW IT WORKS?
INTRODUCTION
• ModelOps (model operations) is a holistic approach to 
building analytics models that can quickly progress 
from the lab to production. 
Source: techtarget.com
ELEMENTS OF A MODELOPS 
APPROACH
• Accessing data from a trusted source and maintaining 
privacy and security standards. 
• Avoiding rework by keeping a deployment scenario in 
mind when creating models.  
• Retaining data lineage and track-back information for 
governance and audit compliance.
Source: techtarget.com
BENEFITS OF MODELOPS
• Although not yet widely used, ModelOps can help 
companies that face increasing challenges in scaling 
their analytics to move models from the data science 
lab into IT production. 
Source: techtarget.com
CHALLENGES OF MODELOPS
• The analytics model must be compatible from the 
creation environment to the production environment.
• The model must be portable. 
• Monolithic and locked-in platforms may limit what 
organizations can do or offer services companies don't 
need.
Source: techtarget.com
HOW DOES MODELOPS WORK?
• The ModelOps team helps foster communication 
between data scientists, data engineers, application 
owners and infrastructure owners and coordinates 
proper handoffs and execution so that models can 
advance to the so-called last mile.
Source: techtarget.com
PERFORMANCE PARAMETERS
• Set up and track accuracy goals for models through 
development, validation and deployment.
• Identify business metrics affected by the model in 
operation. Determine if the model is having the 
intended effects.
Source: techtarget.com
MONITOR
• Track metrics such as data size and frequency of 
update, locations, categories and types. 
• These metrics can help determine if model 
performance problems are a result of changes in the 
data and its sources.
• Monitor how much computing resources or memory 
models consume.
Source: techtarget.com
ARTIFICIAL INTELLIGENCE
• With the rapid adoption of artificial intelligence (AI) and 
machine learning, analytical assets and models are 
multiplying at a fast pace. 
Source: techtarget.com
MODEL DEVELOPMENT
• As model development becomes more prevalent for 
solving business problems, deployment and 
governance often are the last hurdle. 
Source: zdnet.com
THANK YOU