What are IBM Embeddable Libraries?
And why it is a better, faster & lightweight option to Embed AI into your products?
Embedded AI is everywhere and there’s a good chance you’ve already used it. Autopilot systems in your car, checking the forecast with your smart speaker or finding a ride for your way back home.. it’s all Embedded AI! There is little doubt about the success of embedded AI in large internet companies — but it’s a much different story for enterprise AI.
85% of enterprise data science projects fail to make it to production, and even fewer than 7% offer a positive return on investment.
In a global survey, released by McKinsey and Company, found most enterprise AI projects are merely focused on analytics and do not include machine learning. So what explains this dichotomy?
Why getting RoI in AI is so challenging?
Many enterprises face three big challenges when trying to replicate the same success, as large internet companies have seen in embedded AI.
- AI is hard- Unless you have an expert team of Ph.D. data scientists; it’s hard to drive impact. AI is also moving extremely fast. Your embedded AI product needs to evolve quickly but new algorithms and Large Language Models are discovered every week.
- Open source comes with big limitations–The capabilities to scale and expand need a toolkit. The difference between a 50% accurate model and a 90% accurate model is not an 80% jump in business impact but 80 times. And that is something where open source often reaches its limit. It is also a big myth that open source is free. Companies pay on the ML-Ops cost to deploy the models & to keep upgrading as the framework evolves.
- AI is expensive – Often requiring millions in R&D and budget and computing the cost to run GPUs — not to mention the cost of setting up AI infrastructure, which can be prohibitively expensive — ultimately testing the patience of your executives.
How can Watson Libraries help?
And that’s where using Watson Libraries can help — bringing capability, scalability, and flexibility, specifically designed for partners interested in embedding AI into their products.
- Superior AI capability – Watson Libraries are built on proprietary, best-in-class algorithms from IBM Research, with millions of $$ investment in annual R&D offering superior capabilities to open source in NLP & Speech. Like better Topic models than LDA, targeted sentiment analysis, or contextual phrase extraction which open source can’t match. Or for Speech tasks noise reduction, multiple speaker detection & widest language support.
- Flexible — Watson Libraries are flexible by design. It offers the power of inner source with the flexibility of open source. It has the form and factor of open source libraries, easily extensible to any other algorithm like BERT.
- Affordable & Scalable — Watson Libraries are a lightweight containerized solution and run anywhere — in the cloud, on-prem, local machine, etc. and support all hyperscalers including AWS, Azure and Kubernetes container services.
For many software companies whose main business isn’t AI research, this is a great opportunity to unlock value by building with Watson libraries whether it’s ISV (Independent Software Vendors), GSI (Software Integrators) or ecosystem partners.
It comes with hundreds of pre-trained models in the widest assortment of languages available, for dozens of NLP tasks, including sentiment analysis, phrase extraction, and speech recognition — saving time and computing cost in training models — and returning the value of embedded AI — Faster.
When are Watson Libraries the rIght choice?
Watson NLP and speech capabilities are available in SaaS API in the form of AI applications or as newly launched Watson Libraries in containerized form.
AI applications are a great choice for you as
- No need to invest in expensive AI infra. You can get started quickly with pre-built models and directly draw inferences with models running in the cloud
- Embed AI model endpoints quickly into the applications to produtionalize
- This is a great option if you are getting started with your journey in AI or want to try the value of new models first (like say NLP or speech) before investing in scaling it up on dedicated AI infra.
- AI applications are also designed for end developers who have little or no skills in data science and can deliver value quickly out of the box using GUI.
On the other hand, Watson Libraries in Containerized form offers a powerful & robust way to expand AI journey.
- This is tremendously helpful to those who have already worked with some other open-source packages before and want to expand the depth of their AI models by adding proprietary research to their bag
- It keeps a simple & flexible form-factor of library structure and is plug-gable to any other open-source algorithms
- It can deploy speech and NLP in containers making it portable & deployed to run anywhere. Advantages of embedded AI in containers for companies include
- Immutable infrastructure
- Complete control over data, infra, and model updates
- Portable architecture (Cloud, on-premise, local machine, edge)
- Scalability to mix n match best in the class of automation to run your AI in production.
Unlocking the value of Watson Libraries
With the first launch of Watson Libraries we bring the Watson NLP and Speech libraries
The Watson NLP library comes with 12 pre-built NLP task wrappers to quickly build models in sentiment analysis, entity extraction, relation extraction, topic modeling, phrase & keyword extraction, etc. The Watson Speech library comes with ready-to-consume packages for Speech to Text and Text to Speech.
Here is a quick overview of Watson NLP & Speech libraries in action!
Get ready to Embed AI
The IBM Build Lab team is here to work with you on your AI journey.
As a partner, you can start your AI journey by browsing and building AI models through a Digital Co-Create guided wizard.
You can further browse the collection of self-serve assets on Github, and if you are an IBM Business Partner, you can also start building AI solutions on Tech Zone.
