Governance of Productionizing Generative AI
Key Points
- 2023 focused on experimenting with generative AI techniques, while 2024 will shift toward productionizing these methods and integrating them with traditional AI models to maximize solution value.
- Effective governance of generative AI is essential and rests on three pillars—risk management, compliance management, and lifecycle governance—encompassing model transparency, validation, and adherence to AI regulations.
- Deploying generative AI for tasks such as social‑media sentiment analysis requires carefully crafted prompts created by cross‑functional teams (data engineers, data scientists, solution architects) and systematic lifecycle management.
- A robust generative‑AI governance platform must enforce appropriate metrics (fairness, bias, quality, reference standards) and stay aligned with evolving regional AI regulations that have expanded from 2018 to 2024.
Full Transcript
# Governance of Productionizing Generative AI **Source:** [https://www.youtube.com/watch?v=kLaBaJyBe4I](https://www.youtube.com/watch?v=kLaBaJyBe4I) **Duration:** 00:05:22 ## Summary - 2023 focused on experimenting with generative AI techniques, while 2024 will shift toward productionizing these methods and integrating them with traditional AI models to maximize solution value. - Effective governance of generative AI is essential and rests on three pillars—risk management, compliance management, and lifecycle governance—encompassing model transparency, validation, and adherence to AI regulations. - Deploying generative AI for tasks such as social‑media sentiment analysis requires carefully crafted prompts created by cross‑functional teams (data engineers, data scientists, solution architects) and systematic lifecycle management. - A robust generative‑AI governance platform must enforce appropriate metrics (fairness, bias, quality, reference standards) and stay aligned with evolving regional AI regulations that have expanded from 2018 to 2024. ## Sections - [00:00:00](https://www.youtube.com/watch?v=kLaBaJyBe4I&t=0s) **Productionizing Generative AI Governance** - The speaker outlines the transition from experimental generative AI in 2023 to scaling and governing these models in 2024, emphasizing risk management, compliance, lifecycle oversight, and illustrating the approach with a social‑media sentiment‑analysis example. ## Full Transcript
2023 has been all about experimenting
with different gen AI techniques and
methods before that we had traditional
AI methods traditional AI models already
existing so in 2024 when we look into
the new landscape I Envision that it'll
be a lot about productionizing some of
these gen methods and practices and also
augmenting with the existing
traditionally AI practices methods
services to bring out the most value out
of the solution that has been created
Tech that has been built as well but
when we take this into production one of
the key factors to look into because of
the nature of generative AI is
governance when I speak about governance
governance has three major pillars
starting with risk
management
compliance
management
and life cycle
govern
it these are the three main pillars if
you drill a little bit deeper what does
this comprise of it comprises of model
transparency and explainability model
validation model risk validation and
compliance in AI
regulations to give you a brief idea as
to how would this work in an industry
setting let's start with an example one
of these examples is something that I've
been seeing day in and day out in my
day-to-day life where we are leveraging
tentative AI to do social media Twitter
sentiment analysis so you have messages
coming from different social media
platforms as you might know what is the
first thing that you do if you want to
classify this particular tweet or
message um in using generative VI first
thing that you do is utilize a
prompt what is the prompt The Prompt is
a instruction that you give your large
language model you'll have different
types of prompts depending on the
different models and also on the
different tests that you do on on these
models these different models and
prompts will be created by different
teams different people in those te teams
starting with data Engineers data
scientists solution Architects so you
want to do some sort of life cycle
Management on it as
well the other thing when you have these
different models prompts working you
want to be able to go these different
proms you want to make sure that you
have the right metrics in place and the
metrics can vary based on your different
use cases and also the different rules
and acts in a particular State and a
country the metrics is one of the key
feature that a generative AI governance
platform should have you have to make
sure that your model is fair unbiased
the quality is prale are these metric
reference metrics or are they reference
stre metrics do you have a ground truth
with it so you have to make sure that a
platform is created such to govern this
particular um use case that it make sure
that all these different metrics are
evaluated properly then comes one of the
key features which is AI
regulations in our industry in the data
science world as well and know elsewhere
we' have had different regulations
starting from 2018 to 2024 now even more
countries and different states have
these regulations which are put in place
so now a platform needs to be created
with has a risk questionnaire that is
this particular model or is this
particular prompt compliant to this
exact mandate exact act or let's say you
have an internal mandate you want to
make sure you build it and embed that
into your entire life cycle management
tool so that you can visualize all of
these different techniques on one
particular platform
now you have these different steps kind
of overwhelms paper so you need
something which kind of makes these
workflows and automates these processes
end to end so you need a platform
service whatever you call it which takes
all of these different steps into
consideration and when you create that
and you take you need to take a step
back and understand what exactly should
it comprise of first of all it needs to
be open make sure all third party models
are being able to be monitored you have
different metrics third party metrics
different websites different clients
have their own metrics that they've
defined as well make sure it's
integrable you have Legacy systems and
you want to be able to integrate these
Legacy systems with this pre-existing
metrics or you will have these new
metrics new methods that you want to
integrate on top of that so always make
sure that your different products that
you create a backward forward compatible
and one of the key aspects as we are on
this topic of compliance is make sure
its
compliance and it's compliant to all of
these different techniques and acts and
different uh eui uh platforms which are
available now