JENNY BROWN: With over 300 sessions at Next 2023,
it's understandable that you couldn't get to them all.
So if you didn't see Five Practical Considerations
for Adopting AI, then stay tuned because here's the rewind.
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AI has the potential to transform all industries
and is being adopted at an unprecedented rate.
And one of the most common questions we get
is what is actually going to create value for my business,
and what should I consider in formulating my roadmap?
So let's start with business value created by AI.
Typically, companies look to improve an existing workflow
or solve a new problem that couldn't be solved before.
And AI can help deliver new insights from data,
increase employee productivity, and speed up time
to innovation.
And it all starts with use case prioritization,
where we recommend looking at three things, business impact,
the bottom line, increasing revenue or decreasing
costs, including employee productivity, ease
of implementation, which depends on data
availability and difficulty of methodology
used, impacting time to market, and, lastly,
the capabilities built. Where are some repeatable patterns
across the business?
Where have you built training or monitoring capabilities
that could be reused?
Bring together business and technical stakeholders
to define the use cases and success.
However, in such a quickly moving field,
it's critical to experiment and iterate.
With traditional AI, that took a lot of time, data, experts,
and stages.
But, fortunately, with generative AI,
that lifecycle is dramatically shortened.
So while it may still feel a bit intimidating or open-ended,
gen AI enables rapid and efficient experimentation,
even if you're not using an out-of-the-box solution
or if you're only supplementing a workflow with AI.
But, of course, your key output to all this experimentation
is data and what it tells you about your actual improvements.
And for that, we suggest focusing
on three areas, accuracy.
Consider grounding in the truth of what your goals are.
It's going to be different for creative marketing
than it would be for obtaining facts or summaries.
Impact-- does this solution solve a problem,
add revenue, or lower costs?
And efficiency-- you want to make sure that this makes
sense for your business.
To get the best results and performance,
you also need to evaluate at several steps,
from finding, to tuning, and validating your model
output, to ongoing measurement and evaluation,
especially for emerging gen AI products.
While researchers continue to improve model evaluation,
first principles like evaluating against your own defined goals
and budget are reasonable targets,
like having a data set that's sufficient for what you want
to measure, defining quality metrics that
are important for you, and keeping
your decision space small.
What are you willing to adjust?
Of course, enterprise readiness is essential for adopting AI.
And, fortunately, enterprise readiness
is foundational to all of our AI products.
And while the customer is responsible for aspects
of maintaining their environments,
building on Google Cloud means that we share that security
responsibility with you, and we take that shared responsibility
one step further to operate in a shared fate
model with our customers.
With Google Cloud Secure AI Framework or SAIF.
SAIF is a conceptual framework for securing
AI systems inspired by security best practices.
This ensures that when AI models are implemented,
they're secure by default. And this
means that you can build secure private AI
applications without expending massive resources.
But as your number of use cases increases,
you'll need to automate this framework to scale,
which is where MLOps comes in.
MLOps is a set of standardized processes and capabilities
for building, deploying, and operationalizing ML systems
rapidly and reliably, involving orchestrating
and automating the execution of continuous training
pipelines, model deployment and prediction serving, data set
and feature management, and model management
and governance.
And with gen AI, organizations need
to evolve their MLOps capabilities
to address new challenges with model
adaptation like prompt engineering and tuning
techniques, model optimization for efficient deployment
and serving, and additional guardrails like safety
scores, filters, DLP, and even human feedback.
If you'd like to see the full talk,
check out the link in the description.
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Thanks for watching.
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