Blog: Natural Language Processing ala Google AI & ML
Let's level set. I don't believe technology is a complete replacement for human wit and innovation, however, used right and coupled with a mix of compassion, intention and vision, technology can greatly supplement our effort to do good work efficiently and at scale. By extension, I'm continously looking for and evaluating what tools and services are in the external and internal ecosystem of our company—both, now and those on the horizon, even if just ideas—for helping solve real-world problems.
Used right and coupled with a mix of compassion, intention and vision, technology can greatly supplement our effort to do good work efficiently and at scale.
I deal and engage with sentiment analysis on a daily basis, and my team is at a point where we're outgrowing our startup implementation for how we're processing data. So, I've been researching to see what tools may be available that allow me and my team to scale our product with confidence while not breaking the bank. Enter Google's Ai & ML product suite.
Google's AI & ML Product Suite
Simply put: the platform and products are reliable, scalable and straightforward (enough) to tap into whether you're an individual, small shop or enterprise level company. There is a learning curve to it, like anything else, but the Google team has sure come a long way in making the platform more easily accessible since the earlier days. The suite offers some good NLP options to leverage such as entity analysis, content classification, sentiment analysis and more; effectively, helping you give structure and meaning to your data.
NLP Sentiment Analysis Example
During a Sunday morning, I extended my existing node API service on my site (this one) to be able to run NLP sentiment analysis—using Google's platform—on arbritary text that I (or you) feed it. For clarity, extending my API to accomplish this was not entirely related to Google's AI & ML platform, this effort is merely a part of any fullstack development solution, however, Google's API client tools sure made connecting with their AI & ML platform nearly friction-less when the time came to wire things up from my API server to their platform. Side note: I did come across a couple of
incomplete areas of documentation that could have saved a few minutes of headscratching, but it wasn't that big of a deal. I'll see about submitting a pull-request with the Google Language team to address this.
Once the API was extended, I created some custom UI components ala React and my design system that led me to this quick-and-dirty proof-of-concept that shows how both texts and individual parts of texts (e.g. sentences) are analyzed:
For those interested, I'll write a separate post showcasing how to train your own custom model with Google AI & ML. I'll plan to go into the code weeds of the approach if it helps folks more easily learn and get started in giving things a try themselves.
Where to go from here
The upshot: I oversee my team's engineering team and product user experience, and as a part of our initiative to scale and continuously improve our own product and services, I believe Google's AI & ML product suite is going to be a top contendor in what we evaluate this year to help us reach our platform service goals and level-up our product for our customers.
I am optimistic about the challenges ahead of us, along with the rewards, with the advent of AI/ML. I am also realistically concerned if we don't collectively take proactive steps to prepare for the far reaching impacts AI/ML has on society, such as on individual relevance, economic and social (in)justice, and workforce displacement.
Whether you're trying to understand customer feedback to find actionable product and UX insights (Loop), or you want to do some social good, accomplish something with ML for yourself, or have some school project revolving around AI, I'd recommend giving Google's AI & ML product suite a whirl. I'm happy to jump on a call with anyone who hits a snag during setup and would like some high-level guidance on ways to configure things.
Let's do some good,
I am a problem solver: a tech and people leader with a passion and proven track-record in building and leading empathetic, productive teams—remote and on-site—within a continuous learning culture, while championing usable, inclusive digital products and online experiences. I am also a father, advisor, life-long learner, advocate, community builder, and speaker—I am Human. Learn more