The AI life cycle consists of several stages, starting with data collection from diverse sources to ensure quality. This is followed by data preparation, model building with machine learning algorithms, and evaluation of model performance. Finally, successful models are deployed in production, with ongoing monitoring and maintenance to ensure accuracy and relevance.

1. WHAT IS THE
AI LIFE CYCLE?
Data Collection:
The process begins
with gathering
diverse and relevant
data from various
sources, ensuring its
quality and integrity.
Data Preparation:
Model Building: Evaluation and
Testing:
Monitoring and
Maintenance:
Deployment:
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Next, the data
undergoes cleaning,
preprocessing, and
transformation to
make it suitable for
analysis.
The models are
evaluated using
metrics to assess
their performance
and effectiveness.
In this stage, machine
learning algorithms are
applied to the prepared
data to train models
that can make
predictions or
decisions.
Successful models
are deployed into
production
environments to
address real-world
problems.
Once deployed,
continuous monitoring
and updates ensure
the model's accuracy
and relevance over
time.