This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Great customer service builds trust, inspires brand loyalty, and earns repeat business. So it's no surprise that, according to Deloitte, close to 90 percent of organizations name improving the quality of their customer service as a strategic focus. Customer service helpdesks know this all too well. They often deal with an ongoing flow of tickets that sometimes have little information or context, which can slow down agents and impact service quality. What if you could use historical data to predict key KPI or fields of a support ticket to handle it in the most efficient way? Some support ticket data -- such as resolution time, priority, tags, or sentiment -- is only known once a ticket is being handled or once closed.

This guest post explains how Wootric's platform uses Google Cloud Natural Language API to complement its own machine learning for saving infrastructure and engineering costs. Wootric is a customer feedback management platform that allows businesses to gauge and quantify customer loyalty through proven feedback metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT) and Customer Effort Score (CES). For example, here's an NPS survey that we present in-app (we also support mobile, email and SMS) that usually takes a user less than 30 seconds to complete. As you can see, the question above is very specific and objective. Applying simple arithmetic on this score from your customer base gives you your Net Promoter Score, and allows you to sort your customers into sets of {Promoters, Passives and Detractors}.

TensorFlow is the machine-learning library open sourced by Google in November 2015. It gained over 11,000 stars on GitHub in its first week after launch, and has built up quite a community since then: at the time of this writing, TensorFlow has over 45,000 stars, 13,000 commits and 21,000 forks. This is the second installment in our interview series with Jeff Dean, Google Senior Fellow and lead of the Google Brain research team. In our first installment, we talked about the landscape of machine learning: its past, present and future. In this installment, we'll cover TensorFlow: why we built it originally, how to use it, and what its future may hold.

This 3-hour course (video slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Deep learning (aka neural networks) is a popular approach to building machine-learning models that is capturing developer imagination. If you want to acquire deep-learning skills but lack the time, I feel your pain. In university, I had a math teacher who would yell at me, "Mr. Görner, integrals are taught in kindergarten!"

If you've ever learned to program, you've probably written a Fizz Buzz test. With Fizz Buzz, you print the numbers from 1 to 100, except if it is divisible by 3, you print "fizz"; if it's divisible by 5, you print "buzz"; and if it's divisible by 15 you print "fizzbuzz." This trivial coding problem is typically achieved with a couple of if statements and checking whether each number can be divided by 3 or 5. In his recent blog post "Fizz Buzz in TensorFlow," Grus imagines he's asked to solve Fizz Buzz as part of a job interview. But instead of taking the obvious approach, he uses TensorFlow, the open-source machine learning library developed by Google.