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Bra Fit or Brad Pitt? Fun Moments of Using AI for Contact Centers
For many enterprises, the wish to tap into the power of AI is negated only by a lack of know-how: How AI works, what sort and how much data they will need to gather and structure correctly, and how to use the platforms that make AI adoption easier.
Google understands that. Which is why the AI solutions they have built “are basically plug and play, ready to go with our partners, so that you (businesses) can have immediate business value for your specific use case, and for your specific workflow without a deep investment of any kind into machine learning,” says Levent Besik, Group Product Manager, Google Cloud.
Among the more interesting use cases that’s seeing adoption is contact center AI.
“So one thing that we see quite often, especially from our B2C customers, is that they often have this growing pain in their call centers. They face a trade-off between operational efficiency and great customer service. With the advances in language and conversational AI, that doesn’t have to be the case anymore,” says Besik.
That’s exactly the problem in front of Akash Parmar, Enterprise Architect,. “One of the big challenge we had was: how do we effectively manage 14 million calls, which come into our contact center, stores and head office? In the past, these calls were managed by completely different platforms with their own IVRs, with their own routing and reporting solutions. It was expensive, plus the experience across them was very inconsistent.”
Digging deeper, Parmar and team figured that the challenge was in the fact that an IVR couldn’t really capture the hundreds of reasons customers call.
To get around the problem Parmar and team decided to let customer tell them why they were calling. They then used AI to decipher what the customer was saying, extract the customer’s intent from that, and then help them directly, or re-route them to the best department.
Overall, it proved to be a big success, says Parmer. Although there were funny moments.
“In our early days, we were getting some transcriptions from the Speech API, which were not what we expected. It was not word by word and we had to kind of train the model to make sense of it. For example, we started to get calls about Brad Pitt. We were like, we’ve really won the Oscar here. Brad Pitt is calling us. It was not actually call for Brad Pitt. These were calls about bra fit, which it’s a big business for M&S,” remembers Parmer.
Find out how three companies, including Marks and Spencer, are using AI today.
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38:26 Minutes
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The Secret to Accelerated ML Model Training
As an infrastructure or a data science professional, it’s more critical than ever to keep abreast of the changes taking place to the infrastructure powering machine learning.
If we take a step back, we will realize that there’s been a tremendous amount of progress in machine learning in the last few years, resulting in multiple benefits including higher accuracy in speech and image recognition, for instance. This progress is in part due to the advances in the systems to train models.
One of these advances comes in the type of processors being leveraged to power ML. There’s been an evolution—from CPUs to GPUs to TPUs—each of which bring additional computational power.
Google Cloud TPUs allow data scientists to train their models at lightning speed on Google Cloud’s latest ML hardware.
In this video, you will see how to use your raw data to create different ML applications. More importantly, Zak Stone, Product Manager For TensorFlow and Cloud TPUs, Google Brain, will show you how to accelerate ML model training on TPUs using Google Cloud’s optimized, TensorFlow image classification code. Each step will be explained in detail.
Architecting Data Pipelines Directly Improves Customer Experience at Universe.com
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At Universe, we serve customers day and night and are always working to make sure they have a great experience, whether online or at one of our live events.
Our technology has to make that possible, and our legacy systems weren’t cutting it anymore. What we needed was a consistent, reliable infrastructure that would help our internal teams provide a fast and innovative ticket-buying experience to customers.
With our data well-managed, we could free up time for our developers to bring new web features to customers, like tailored add-ons at checkout.
Our team of about 20 software engineers needed more flexibility and agility in our infrastructure; we were using various data processing tools, and it wasn’t easy to share data across teams so that everyone saw the same information.
We also needed to incorporate streaming data into the data warehouse to ensure the consistency and integrity of data that’s read in a particular window of time from multiple sources. Our developer teams needed to be able to ship new features faster, and the data back ends were getting in the way.
In addition, when GDPR regulations went into effect, we needed to make sure all our data was anonymized, and we couldn’t do that with our legacy tools.
Finding the right data tools for the job
To make sure our customers were getting a top-notch online experience, we had to make the right technology choices.
Our first step was to centralize multiple data sources and create a single data warehouse that could serve as the foundation for all of our reporting requirements, both internal and external.
The new technology infrastructure we built had to let us move and analyze data easily, so our teams could focus on using that data and insights to better serve our customers.
Previously, we had lots of siloed systems and applications running in AWS. We did a trial using Redshift, but we needed more flexibility than it offered in how we loaded historical data into our cloud data warehouse. Though we were using MongoDB Atlas for our transactional database, it was important to continue using SQL for querying data.
The trial task that really sold us on BigQuery was when we wanted to alter a small table that had about 20 million rows, used for internal reporting.
We needed to add a value, but our PostgreSQL system wouldn’t allow it. Using Apache Beam, we set up a simple pipeline that moved data from the original source into BigQuery to see if we could add the column there.
BigQuery ingested the data and let us add the new value in seconds. That was a significant moment that led us to start looking at how we could build end-to-end solutions on Google Cloud. BigQuery gave us multiple options to load our historical data in batches and build powerful pipelines.
We also explored Google Cloud’s migration tools and data pipeline options. Once we saw how Cloud Dataflow worked, with its Apache Beam back-end, we never looked back. Google Cloud provided us with the data tools we needed to build our data infrastructure.
Cloud for data, and for users
Introducing new technologies isn’t always simple—companies sometimes avoid it altogether because it’s so hard. But our Google Cloud onboarding process has been easy.
It took us less than two months to fully deploy our BigQuery data warehouse using the Cloud Dataflow-Apache Beam combination.
Moving to Google Cloud brought us a lot of technology advantages, but it’s also been hugely helpful for our internal users and customers. The data analytics capabilities that we’re now able to offer users has really impressed our internal teams, including developers and DevOps, even those who haven’t used this type of technology before.
Some internal clients are already entirely self-service. We’ve hosted frequent demos, and also hosted some “hack days,” where we share knowledge with our internal teams to show them what’s possible.
We quickly found that BigQuery helped us solve scale and speed problems.
One of our main pain points had been adding upsell opportunities for customers during the checkout process. The legacy technology hadn’t allowed us to quickly reflect those changes in the data warehouse. With BigQuery, we’re able to do that, and devote fewer resources to making it happen. We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.
We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.
Two features in particular that we implemented using BigQuery have helped us improve the performance of our core transactional database. First, using Cloud Dataflow to convert raw MongoDB logs to structured rows under a BigQuery table, which we can then query using SQL to identify slow or underperforming queries. Second, we can now query multiple logging tables using wildcards, since we load Fastly logs to BigQuery.
Along with MongoDB Atlas as our main transactional database, much of our infrastructure now runs as Google Cloud microservices using Google Kubernetes Engine (GKE), including the home page and our payment system. Kubernetes cron jobs power background scheduled jobs, and we also use Cloud Pub/Sub. Cloud Storage handles any data storage if any space constraints emerge.
Our overall performance has increased by about 10x with BigQuery. Both our customers and internal clients, like our sales and finance teams, benefit from the new low-latency reporting. Reports that used to be weekly or monthly are now available in near-real time. It’s not only faster to read records, but faster to move the data, too.
We have Cloud Dataflow pipelines that write to multiple places, and the speed of moving and processing data is incredibly helpful. We stream in financial data using Cloud Dataflow in streaming mode, and plan to have different streaming pipelines as we grow. We have several batch pipelines that run every day. We can move terabytes of data without performance issues, and process more than 100,000 rows of data per second from the underlying database. It used to take us a month to move that volume of data into our data warehouse. With BigQuery, it takes two days.
We’re also enjoying how easy and productive these tools are. They make our life as software engineers easier, so we can focus on the problem at hand, not fighting with our tools.
What’s next for Universe
Our team will continue to push even more into Google Cloud’s data platform. We have plans to explore Cloud Datastore next. We’re also moving our databases to PostgreSQL on GCP, using Cloud Dataflow and Beam. BigQuery’s machine learning tools may also come into play as Universe’s cloud journey evolves, so we can start doing predictive analytics based on our data. We’re looking forward to gaining even more speed and agility to meet our business goals and customer needs.
What Interests Baseball Fans? Unravel with Google’s Data Science Tools and ML
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3:00 Minutes
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The game of baseball has no shortage of statistics — from batting average to exit velocity, strikeouts to wins above replacement. Among all sports, Major League Baseball (MLB) arguably contains the most analytical and data-driven participants and fan base. Subconsciously or viscerally, players and managers on the field and those following from anywhere are constantly assessing and making decisions based off of game play trends and expectations — whether a batter will come through with a hit in an important situation, when a pitcher should be pulled. Less analyzed, however, is what leads fans to become engaged with certain players or teams, and what factors drive their love of the game. This is the motivation behind the problem being posed by Major League Baseball in their Kaggle competition for Player Digital Engagement Forecasting. Can you use machine learning to deconstruct baseball fandom?
This competition asks you to predict measures of digital engagement for each active player on a daily basis during the MLB season. So, how large was the surge in fan interest after Joe Musgrove threw the first no-hitter in Padres history? Is Shohei Ohtani’s engagement higher when he pitches well, when he hits a monster home run…or when he does both? You’re provided a wealth of game, team and player information – detailed stats, awards, rosters, and transaction information – as well as social and digital engagement data as your inputs. Data scientists will recognize this as an exciting forecasting problem with both traditional regression and time series components, where having this input data just prior to the prediction date is critical to determining which players will receive the most engagement.
With so many variables in the game, there are an endless number of vectors which could possibly influence fan engagement. Eleven-time All-Star Miguel Cabrera delighted fans by hitting the first home run of the season – in the snow! Occasionally a lesser-known player like Musgrove or Carlos Rodón “wins the day” with an unlikely no-hitter. And sometimes just getting traded to an iconic franchise like the Yankees generates a ton of fan interest, like it did for Rougned Odor in early April.
As these examples show, a player’s digital engagement can be pretty dynamic during the season, with many different potential contributors to who is “trending” on a given day. How can you use data to uncover which factors are the most influential of engagement with each player’s digital content?
Ready to play ball? Check out the competition on Kaggle for all the details. $50,000 in prizes is up for grabs in two prize categories. The code competition puts your machine learning skills to the test, to see who can build the most accurate forecasting models to predict daily digital engagement for every active player. You’ll have until July 31st to build your models and then be evaluated on a future time frame, which will determine the winners. For data visualization and exploration experts out there, the explainability prizes give you an opportunity to analyze more broadly which factors, even those outside of what we’re providing directly, most influence digital engagement. You’ll be evaluated on how well you can use what the data is telling you to support your findings.
And if you’re looking to get started, we’ve provided an introductory video and some notebook tutorials, including a starting point for harnessing the power of Vertex AI through tools including Cloud Notebooks, Explainable AI, and Vizier.
With the second half of the season upon us, it’s an exciting time to be an MLB fan. With this Kaggle competition, it’s also a perfect opportunity to use data science to help understand baseball fandom and potentially earn some of your own accolades in the process. Step up to the plate!
Major League Baseball trademarks and copyrights are used with permission of Major League Baseball. Visit MLB.com.
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5:30 Minutes
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The 5-Min Demo: How to Develop, Train and Deploy Training Models on Kubernetes
Machine learning has taken businesses by storm. A growing number of organizations, both large and small, and spread across a swathe of industries, are figuring out how to quickly adopt ML.
But in the midst of that accelerated push, infrastructure and data science teams have to come to terms with high operational overheads especially considering all the time and effort it takes to develop a specific model and have it run in different places.
If you’re a data scientist or part of the data team, you’ve probably been here: You develop a model on your laptop, train it on the cloud, and then serve it in production, which could be on-prem or in a different cloud. The challenge is that in these various environments, hardware and software configurations could be totally different—and that leads to things breaking down.
That’s where Kubernetes comes in. It provides a way to easily develop training and deployment learning models in the very scalable and flexible way.
In
Southwire Completes SAP Migration to Google Cloud as a First Step of its Tech Evolution
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“Talk about tough times, right?”
That’s how Dan Stuart, Senior Vice President of IT Services at Southwire Company, refers to the months following a December 2019 ransomware event, and the COVID crisis that began in spring of 2020. Those events hit just as the company was preparing for an overhaul of their SAP environment. This comprehensive plan included three key elements. First, the company wanted to upgrade their SAP ECC environment to take advantage of the latest functionality available for this critical ERP system. Second, Southwire aimed to deploy SAP Business Warehouse on SAP HANA to accelerate vital reporting for all business users. Third, the company wanted to upgrade to the latest version of SAP Process Orchestration—an essential component that touches key manufacturing interfaces in all Southwire facilities.
Southwire had looked at multiple options for the upgrades, including remaining entirely on-premises, colocation, and full cloud migration. “Going to the cloud seemed a lot more compelling,” says Joe Schleupner, Southwire’s Senior Director of PMO & ITS planning and implementation. “We were going to the cloud eventually, so why take these intermediary steps? Let’s just get it done.”
After looking at several options, Southwire decided to migrate to Google Cloud. “We wanted to be on a platform for SAP that was flexible, scalable, and secure; that we could count on to get up and running quickly,” says Stuart. “We chose Google Cloud not only for those reasons, but also because we recognize that Google has other assets that we may be able to take advantage of down the line, such as technologies like artificial intelligence (AI).”
More stability, less worry
As one of the leading manufacturers of wire and cable used in the transmission and distribution of electricity, Southwire aids the delivery of power to millions of people worldwide. They have more than 30 manufacturing facilities across the United States running 24/7. Any downtime directly affects productivity and revenue. With help from Google Cloud and their implementation partner NIMBL, Southwire completed the SAP migration to Google Cloud over a planned maintenance weekend on July 4th.
The migration itself, while complex, went quickly and smoothly. “Just moving to the cloud was quite a feat because we were dealing with so much data, but in total the SAP system was down for only ~16 hours,” says Schleupner.
“As a project manager, I always felt that Google Cloud had my back” Schleupner says. The Process Orchestration (PO) migration was of particular concern, considering that it controlled all of Southwire’s manufacturing interfaces across the entire company. “Every critical piece of information that goes from SAP down to the manufacturing system goes through that system,” says Schleupner.
Even after migrating, Southwire discovered that making changes to the system was fast, easy, and resulted in no downtime. Normally, certain types of changes would have involved taking down SAP for at least an hour.
The Southwire team also appreciates the fact that the modern cloud architecture means spending less time on routine infrastructure maintenance. “It’s one less thing for me to worry about,” Stuart says, “I can focus on the business side of the house and move the technology and responsibilities to what we do within the Google Cloud Platform.”
What comes next?
While the cloud migration will increase stability, uptime, performance, and security, there is much more to come. Southwire is currently working on a disaster recovery implementation for their SAP environment on Google Cloud. Stuart and Schleupner are excited about where Google Cloud can further take Southwire. They are considering an SAP Hybris e-commerce implementation as well as connected factory and/or factory automation initiatives that can take advantage of artificial intelligence and machine learning.
To Stuart and Schleupner, the migration of Southwire’s SAP environment to Google Cloud, as important as it was, really represents the first step in the company’s tech evolution. Now that much of the heavy lifting is complete, Southwire’s digital transformation can begin in earnest. “There’s no shortage of areas where I think Google Cloud will come into play,” Stuart says, “and we intend to look at these things with an open mind to understand how we can leverage current investments to take our organization where we want to go.”
Learn more about Southwire’s SAP on Google Cloud deployment and how Google Cloud can transform the way you work with your SAP enterprise applications. Visit cloud.google.com/solutions/sap.
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