MODELOPS
OR
THE LAST MILE
OF ANALYTICS
ARE USING AI EXTENSIVELY
BELIEVE AI WILL ALLOW THEIR COMPANIES TO OBTAIN OR SUSTAIN
A COMPETITIVE ADVANTAGE
Only a fraction of models make it to production
And for those that do it takes months to deploy
DATA SCIENTISTS
Lack of governance
and documentation impact
model performance.
Manual maintenance
and retraining impact
development of new models
BUSINESS
Ever-changing business
requirements cause delays
in development.
Models are blamed in case
of poor results
IT OPERATIONS
Delays are caused by
bottlenecks of IT resources
recreating data structures
and recoding models.
Limited storage and
compute resources cause
additional constraints
Taking its roots in DevOps, ModelOps is going beyond a set of tools with framework and methodology to move models from ideation to production as quickly as possible, bridging the gaps between involved functions and their mindsets, making sure your AI-related dreams come true

Idea
Find out whether similar problems been solved in past and what are the results and pitfalls via modeling and experimentation repository
Resource provisioning
Set up an independent development environment with few mouse clicks and any ds tool/language you need in a container - never worry again about cannibalizing all cluster resources
Data prep
Deploy dedicated analytics data store including both ABT and pre-ABT layers to lower data-prep efforts and make your analysts code production-ready
Modeling
Use any tool you need and any model you want without fear of not being able to push it further
Model Validation
Stop struggling to find out "what gone wrong"
with set process of model validation, tracking and documentation in model validation framework

Model Deployment
No need to recode model in "conventional" language, deploy models in the same way it was developed via CI/CD tools and methodology
Model Execution
Ensure scalable and reliable execution in a containerised environment
Model Monitoring
Have all the up to date information on models performance, population statistics and experimentation health-status in dedicated dashboard
Retraining
Automate most of retraining process while being sure, that all the changes are well-documented
SINGLE DATA-ENV
CONTAINERS FOR DEV. ENVIRONMENTS
RETRAINING AUTOMATION
CI/CD
CONTAINERS FOR PROD. DEPLOYMENTS
MONITORING DASHBOARDS
Unlike most data-projects ModelOps can be introduced to organisation step-by-step,
ensuring that every other month and model you become more and more efficient.
MLOps in brief: how to streamline the most cumbersome areas of ML development
The first step to better model analytics
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