Embedding AI: Libraries vs Applications
Key Points
- AI adoption is accelerating, with companies moving from experimental use to an “AI+” mindset that embeds intelligent capabilities directly into their core solutions.
- Embeddable AI refers to enterprise‑grade, flexible AI models that developers can easily integrate into applications, delivering smarter, more efficient, and automated user experiences.
- Containerized libraries—built on open‑source frameworks—offer pre‑trained models that run anywhere, are highly extensible, and reduce infrastructure costs thanks to their lightweight nature.
- Low‑code/no‑code AI applications enable faster go‑to‑market by letting developers embed AI without deep expertise, streamlining development and focusing effort on domain‑specific functionality.
Sections
- Untitled Section
- Benefits and Governance of Embedded AI - The speaker outlines how pre‑built AI applications lower adoption barriers, accelerate market entry, reduce development costs, and require responsible, trustworthy, and secure AI practices before deciding between using a library or an application.
- AI Deployment: Apps vs Libraries - The speaker compares containerized AI libraries for hybrid‑cloud, low‑footprint needs with embedded AI applications for faster time‑to‑market and cost reduction, urging evaluation of use cases, infrastructure, and goals to select the optimal approach.
Full Transcript
# Embedding AI: Libraries vs Applications **Source:** [https://www.youtube.com/watch?v=OThahaOga20](https://www.youtube.com/watch?v=OThahaOga20) **Duration:** 00:07:35 ## Summary - AI adoption is accelerating, with companies moving from experimental use to an “AI+” mindset that embeds intelligent capabilities directly into their core solutions. - Embeddable AI refers to enterprise‑grade, flexible AI models that developers can easily integrate into applications, delivering smarter, more efficient, and automated user experiences. - Containerized libraries—built on open‑source frameworks—offer pre‑trained models that run anywhere, are highly extensible, and reduce infrastructure costs thanks to their lightweight nature. - Low‑code/no‑code AI applications enable faster go‑to‑market by letting developers embed AI without deep expertise, streamlining development and focusing effort on domain‑specific functionality. ## Sections - [00:00:00](https://www.youtube.com/watch?v=OThahaOga20&t=0s) **Untitled Section** - - [00:03:16](https://www.youtube.com/watch?v=OThahaOga20&t=196s) **Benefits and Governance of Embedded AI** - The speaker outlines how pre‑built AI applications lower adoption barriers, accelerate market entry, reduce development costs, and require responsible, trustworthy, and secure AI practices before deciding between using a library or an application. - [00:06:22](https://www.youtube.com/watch?v=OThahaOga20&t=382s) **AI Deployment: Apps vs Libraries** - The speaker compares containerized AI libraries for hybrid‑cloud, low‑footprint needs with embedded AI applications for faster time‑to‑market and cost reduction, urging evaluation of use cases, infrastructure, and goals to select the optimal approach. ## Full Transcript
Today, we’ll talk about two ways you can deploy embeddable AI:
we're going to talk about containerized libraries
and we're going to talk about applications.
Now I think it’s not much of a stretch to say that AI adoption is taking off,
and companies have recognized the capabilities of AI and moved from merely having some AI,
to embracing an AI plus mindset for business growth.
AI+ you say, what's that?
Well, one way to think of that is through something called embeddable AI. Embeddable AI goes beyond just experimenting with AI
tools and models; it involves infusing AI easily into the core of your solutions
to make them intelligent, more efficient, more intuitive and to automate them.
And how can we define embeddable AI?
Think of it as a set of flexible, enterprise-grade AI capabilities
that developers can easily embed in their applications.
They provide an enhanced user experience through powerful AI models.
Embeddable AI can be fit-for-purpose for any business –
from domain optimized applications down to embeddable libraries,
designed with trust from the ground up.
Let’s start by talking about containerized libraries.
A containerized library, which is built on an open source framework,
offers pre-trained models that reduce the time and resources required for developers
to add powerful AI to their applications.
There are a bunch of advantages to containerized libraries,
but let’s focus on three defining features.
And number one is containerized libraries can run anywhere.
There are no pre-defined prerequisites, so libraries can be embedded on-premise, on a cloud, at the edge,
or in a hybrid environment.
And number two, libraries are both flexible and they're also extensible.
Now because the only requirement for a deployment is a runtime container and one or more models
for the capability required, libraries can be fit-for-purpose – which allows developers
to leverage the functionality for their specific task or use case.
And then number three,
libraries can reduce infrastructure cost. Due to their light-weight nature,
containerized libraries don’t require a huge amount of compute resources. Less resources leads
to a smaller footprint– which ultimately brings down the cost of running the overall solution.
Okay, now lets move over to Applications. As most people know, an application is
software designed to perform a specific task or provide a functionality for an end user.
And there are three benefits of applications that I want to highlight. And the first of those, number one is low and no code.
Low-code or no code allows developers without AI expertise to embed AI into their solutions.
This lowered barrier of adoption enables developers to focus on the domain functionality of the solution.
Number two is faster go to market. Pre-built applications allow developers to infuse AI into their solutions
without having to spend many hours building out the technology – which allows them to go to market quicker.
And then number three, this time the reduction is in development costs.
Since developers don’t need to spend time creating code, as we mentioned earlier,
enterprises will save time – and therefore money – since embedding pre-built
AI applications reduce the development cycle. But look, regardless of whether you use a library
or application, your embeddable AI technology should be handled in a responsible, trustworthy,
and secure way.
So let's briefly consider each one of those, and starting with responsible AI.
Now responsible AI provides a governance framework,
defining policies and establishing accountability throughout the AI lifecycle
to ensure models adhere to principles of fairness, explainability, robustness, transparency and privacy.
We also need to consider trustworthy AI, and trustworthy AI models are trained using data that has been curated to have bias removed
and have domain specific expertise. And we of course need to consider secure AI. In addition to the security already built
into the AI technology, there should also be 24/7 enterprise grade support available.
So, when do you use a library vs an application to embed AI?
When considering what to choose, ask yourself questions like:
Does your solution run on multiple clouds?
Have you factored in the compute cost for hosting the AI portion of your solution?
What is your company’s go to market plan?
Your answers should help guide you to pick the right form factor for your situation.
Let’s look at a real-life, practical use case example that a call center might have to deal with.
And this here is my attempt to draw some call center headphones.
Now let's say a company is trying to reduce the heavy workload of its agents and analysts.
What can they do?
To help their employees, the company thinks it would be a good idea to equip their workers with a solution
that allows them to quickly identify trends and patterns in customer behavior.
With this information, they could better resolve customer requests.
And one idea that the company has is embedding AI technology with Speech
and Natural Language Processing (NLP) capabilities.
Using text analytics and sentiment analysis,
the agents could be provided with a set of solutions to help address the client ask faster.
So, how should developers decide on an approach?
Well, look, if the developer is working within a hybrid cloud environment, and reducing the overall footprint of the
solution is a main concern for the enterprise, containerized libraries -- this is the best choice.
If the developer knows that the enterprises' two primary concerns instead are actually
expediting time to market and reducing development costs,
the best option is embedding AI through applications.
But whether you choose to deploy AI as an application or as a library,
you can achieve the same great results.
Both form factors offer flexibility, security, and reliability,
ensuring that your AI implementation aligns with your unique requirements and solution.
The key is to evaluate your specific use cases,
existing infrastructure, and organizational goals to determine the best deployment option.
Remember, the success of AI deployment lies in understanding your needs
and leveraging the strengths of each form factor to drive innovation
and unlock the full potential of AI-powered solutions.
For more information on embeddable AI, please follow the links below.