THIS IS WHY
Consider a non-ML project like a calculator, which can still correctly add
and multiply numbers a month, a year, or 10 years later. This is no longer
the case when you are deploying machine learning (ML) models because
your ML system does interact with the real world and the real world is
changing rapidly over time.
ML projects != non-ML projects
Not Enough Expertise
A Disconnect Between Data Science and Traditional Software Development
The data in your model will always be slightly wrong.
Projects Are Too Complex
No Confidence in the Models Built
Issues with Integrating to existing systems
Taking an ML model from desktop POC to running in production implies a
massive, continuous effort.
It’s Difficult to Update Models
Lack of Leadership
Reasons why ML projects never
make it into production
Development of the machine learning-based applications is fundamentally
different from the development of the traditional software. The complete
development pipeline includes three levels of change: Data, ML Model, and
Code. This means that in machine learning-based systems, the trigger for a
build might be the combination of a code change, data change, or model
Changing Anything Changes Everything
learn.machinelearning Source - ml-ops.org
The performance of ML models in production degenerate over time
because of changes in the real-life data that has not been seen during the
model training. Take an example like ad classification models in which
preferences change over time.
Problem - 1 Model Drift
Since ML models are built on data, they are sensitive to the semantics,
amount and completeness of incoming data.
Problem - 2 Data quality
when transferring ML models to new business customers, these models,
which have been pre-trained on different user demographics, data, might
not work correctly according to quality metrics.
Problem - 3 Environment changes
Since ML/AI is expanding into new applications and shaping new industries,
building successful ML projects remains a challenging task. As shown,
there is a need to establish effective practices and processes around
designing, building, and deploying ML models into production - MLOps.
Solving above problems