Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast to train, and can obtain high accuracy quickly from small-scale datasets. On the other hand, deep neural networks offer superior accuracy on larger datasets, as well as the benefits of end-to-end learning, but are black-box and difficult to interpret. TabNet, an interpretable deep learning architecture developed by Google AI, combines the best of both worlds: it is explainable, like simpler tree-based models, and can achieve the high accuracy of complex black-box models and ensembles.

We’re excited to announce that TabNet is now available in Vertex AI Tabular Workflows! Tabular Workflows provides fully managed, optimized, and scalable pipelines, making it easier to use TabNet without worrying about implementation details, and to deploy TabNet with the MLOps capabilities of Vertex. TabNet on Vertex AI Tabular Workflows is optimized for efficient scaling to massive tabular datasets. Moreover, TabNet on Vertex AI Tabular Workflows come with machine learning improvements on top of the original TabNet, yielding better accuracy for real-world data challenges.

TabNet on Vertex AI is well-suited for a wide range of tabular data tasks where model explainability is just as important as accuracy, such as financial asset price prediction, fraud/cyberattack/crime detection, retail demand forecasting, user modeling, credit/risk scoring, diagnosis from healthcare records, and product recommendations.

Overview of Tabnet

TabNet has a specially-designed architecture (overviewed in Fig. 1), based on sequential attention, that selects which model features to reason from at each step. This mechanism makes it possible to explain how the model arrives at its predictions and the thoughtful design helps with superior accuracy. TabNet not only outperforms alternative models (including neural networks and decision trees) but also provides interpretable feature attributions. More details, including results on academic benchmarks, can be found in our AAAI 2021 paper.

Since its publication, TabNet has received significant traction from various enterprises across different industries and a variety of high-value tabular data applications (most of which include the ones for which deep learning was not even used a priori). It has been used by numerous enterprises like Microsoft, Ludwig, Ravelin, and Determined. Given high customer interest on TabNet, we’ve worked on making it available on Vertex given the real-world deep learning development and productionization needs, as well as improving its performance and efficiency.

Highlights of TabNet on Vertex AI Tabular Workflows

Scaling to Very Large Datasets
Fueled by the advances in cloud technologies like BigQuery, enterprises are increasingly collecting more tabular data, and datasets with billions of samples and hundreds/thousands of features are becoming the norm. In general, deep learning models get better learning from more data samples, and more features, with the optimal methods as they can better learn the complex patterns that drive the predictions. The computational challenges become significant though when model development on massive datasets is considered. This results in high cost or very long model development times, constituting a bottleneck for most customers to fully take advantage of their large datasets. With TabNet on Tabular Workflows, we’re making it more efficient to scale to very large tabular datasets.

Key Implementation Aspects: The TabNet architecture has unique advantages for scaling: it is composed mainly of tensor algebra operations, it utilizes very large batch sizes, and it has high compute intensity (i.e., the architecture employs a high number of operations for each data byte transmitted). These open a path to efficient distributed training on many GPUs, utilized to scale TabNet training in our improved implementation.

In TabNet on Vertex AI Tabular Workflows, we have carefully engineered the data and training pipelines to maximize hardware utilization so that users can get the best return for their Vertex AI spending. The following features enable scale with TabNet on Tabular workflows:

• Parallel data reading with multiple CPUs in a pipeline optimized to maximize GPU utilization for distributed training, reflecting best practices from Tensorflow.
• Training on multiple GPUs that can provide significant speedups on large datasets with high compute requirements. Users can specify any available machine on GCP with multiple GPUs, and the model will automatically run on them with distributed training.
• For efficient data parallelism with distributed learning, we use Tensorflow mirrored distribution strategy to support data parallelism across many GPUs. Our results demonstrate >80% utilization with several GPUs on billion-scale datasets with 100s-1000s of features.

Standard implementations of deep learning models could yield a low GPU utilization, and thus inefficient use of resources. With our implementation, TabNet on Vertex, users can get the maximal return on their compute spend on large-scale datasets.

Examples on real-world customer data: We have benchmarked the training time specifically for enterprise use cases where large datasets are being used and fast training is crucial. In one representative example, we used 1 NVIDIA_TESLA_V100 GPU to achieve state-of-the-art performance in ~1 hour on a dataset with ~5 million samples. In another example, we used 4 NVIDIA_TESLA_V100 GPUs to achieve state-of-the-art performance in ~14 hours on a dataset with ~1.4 billion samples.

Improving Accuracy given Real-World Data Challenges

Compared to its original version, TabNet on Vertex AI Tabular Workflows has improved machine learning capabilities. We have specifically focused on the common real-world tabular data challenges. One common challenge for real-world tabular data is numerical columns having skewed distributions, for which we productionized learnable preprocessing layers (e.g. including parametrized power transform families and quantile transformations) that improve the TabNet learning. Another common challenge is the high number of categories for categorical data, for which we adopted tunable high-dimensional embeddings. Another one is imbalance of label distribution, for which we added various loss function families (e.g. focal loss and differentiable AUC variants). We have observed that such additions can provide a noticeable performance boost in some cases.

Case studies with real-world customer data: We have worked with large customers to replace legacy algorithms with TabNet for a wide range of use cases, including recommendation, rankings, fraud detection, and estimated arrival time predictions. In one representative example, TabNet was stacked against a sophisticated model ensemble for a large customer. It outperformed the ensemble in most cases, leading to a nearly 10% error reduction on some of the key tasks. This is an impressive result, given that each percentage improvement on this model resulted in multi-million savings for the customer!

Out-of-the-box Explainability

In addition to high accuracy, another core benefit of TabNet is that, unlike conventional deep neural network (DNN) models such as multi-layer perceptrons, its architecture includes explainability out of the box. This new launch on Vertex Tabular Workflows makes it very convenient to visualize explanations of the trained TabNet models, so that the users can quickly gain insights on how the TabNet models arrive at its decisions. TabNet provides feature importance output via its learned masks, which indicate whether a feature is selected at a given decision step in the model. Below is the visualization of the local and global feature importance based on the mask values. The higher the value of the mask for a particular sample, the more important the corresponding feature is for that sample. Explainability of TabNet has fundamental benefits over post-hoc methods like Shapley values that are computationally-expensive to estimate, while TabNet’s explanations are readily available from the model’s intermediate layers. Furthermore, post-hoc explanations are based on approximations to nonlinear black-box functions while TabNet’s explanations are based on what the actual decision making is based on.

Explainability example: To illustrate what is achievable with this kind of explainability, Figure 2 below shows the feature importance for the Census dataset. The figure indicates that education, occupation, and number of hours per week are the most important features to predict whether a person can earn more than $50K/year (the color of corresponding columns are lighter). The explainability capability is sample-wise, which means that we can get the feature importance for each sample separately.

Benefits as a Fully-Managed Vertex Pipeline

TabNet on Vertex Tabular Workflows makes the model development and deployments tasks much simpler – without writing any code, one can obtain the trained TabNet model, deploy it in their application, and use the MLOps capabilities enabled by Vertex Managed Pipelines! Some of these benefits are highlighted as:

• Compatibility with Vertex AI ML Ops for implementing automated ML at scale including products like Vertex AI Pipelines and Vertex AI Experiments.
• Deployment convenience: Vertex AI prediction services, both in batch and online mode, are supported out-of-the-box.
• Customizable feature engineering to enable the best utilization of the domain knowledge of users.
• Using Google’s state-of-the-art search algorithms, automatic tuning to identify the best-performing hyperparameters, with automatic selection of the appropriate hyper-parameter search space based on dataset size, prediction type, and training budget.
• Tracking the deployed model and convenient evaluation tools.
• Easiness in comparative benchmarking with other models (such as AutoML and Wide & Deep Networks) as the user journey would be unified.
• Multi-region availability to better address international workloads.

More details

If you’re interested in trying TabNet on Vertex AI on your tabular datasets, please check out Tabular Workflow on Vertex AI and fill out this form.

Acknowledgements: We’d like to thank Nate Yoder, Yihe Dong, Dawei Jia, Alex Martin, Helin Wang, Henry Tappen and Tomas Pfister for their contributions to this blog.

“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.

“Hello, Marks & Spencer. How may we help you?”

As one of the biggest and best-loved retail brands in the UK, Marks & Spencer (M&S) is known for the personalized service it provides to its 30 million loyal customers. For 135 years and in 57 countries around the world, M&S has worked to meet and exceed customer expectations for quality and service. Since the advent of the telephone, this has meant cheerfully servicing customers who call in to M&S branches or into their contact centers, no matter what their request might be.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win. Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

—Akash Parmar, Enterprise Architect (Digital Customer Engagement), Marks & Spencer

The company now has a goal of bringing one third of its business online by 2022. In order to engage more customers online, it has opened a new voice channel hosted on Google Cloud.

The company previously had switchboards in 13 different stores across the UK and Ireland (UKI), handling up to nine million calls a year. But as its retail offering evolved across multiple channels, it was becoming increasingly difficult to quickly and effectively answer customer service requests using an outdated switchboard model.

Customers might call in to order an outfit they had seen in a store, to inquire about returning a dress they bought online, or to recover a lost umbrella in a food hall. Each of these different requests required a different routing response from staff, and if the company didn’t act soon, it knew that the cost of managing the increase in call volume would lead to a significant cost impact. M&S decided it was time to make a technological leap forward to meet customers’ expectations in the new, omnichannel retail environment.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win,” says Akash Parmar, Enterprise Architect for Digital Customer Engagement at M&S. “Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

Boosting opportunities for customer engagement with voice recognition

M&S customers were used to dealing with their local store for anything they needed. But as stores were completely separate from the online business, customers weren’t able to purchase something they’d seen online by calling stores because the store staff didn’t have access to platforms needed to place an online order securely. For a company that places a very high importance on customer experience, this was unacceptable.

Akash Parmar, Enterprise Architect for Customer Engagement, was set a the goal by Chris McGrath, M&S Programme Manager, to ensure that the right channel and the right level of assistance was available to customers at any point before, during, or after purchase. Akash set himself the challenge of building a platform that could adapt to all of these channels and scale very quickly.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle. It understood what we wanted to achieve and how Google Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

—Akash Parmar

In 2018, Akash reached out to Google Cloud partner DVELP, one of the UK’s leading experts on the Twilio programmable contact center platform and Google speech recognition technology. DVELP recommended a Google Cloud-based natural language speech recognition platform that leverages the audio stream intent detection functionality in the Contact Center AI solution, Dialogflow, as the heart of an inbound-call-handling strategy. This strategy was designed to improve routing accuracy, give customers more self-service options, and increase analyst visibility into customer journeys.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle,” says Akash. “They understood what we wanted to achieve and how Google Cloud Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

Using Google speech recognition to improve customer experience

M&S wanted to use natural language to enable customers to speak and state what help they required rather than choose from a list of options. This would help them answer the millions of calls coming in and figure out what customers needed quickly.

In order to do that, DVELP needed to consider how best to address tying customer intent to actions, while maintaining flexibility. DVELP recommended the unconventional choice of not referencing intent in the application layer, but mapping the available actions to the information required to perform them. These actions were then used to build a “declarative dictionary” for the customer service team.

By focusing on actions rather than intents, the solution enables the customer services team to configure actions to intents in virtually any combination of key-value pairs. Leveraging the fact that Dialogflow can detect and respond to customer intents in real time, M&S has already reached 92% accuracy in translating customer declarations to actionable intents.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of. No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

—Akash Parmar

Akash recalls that once customers became comfortable with the prompt, “in a few words, how may we help you?” they started providing simple, concise responses, and the customer learning curve quickly leveled out. At that point, Google Cloud speech recognition and Dialogflow took over. “The technology worked perfectly and the result was like magic,” says Akash.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of,” he says. “No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

Enabling self-service contact center improvements with Dialogflow

It was important to M&S that contact center employees be self-sufficient in updating the platform to reflect changes in demand. They needed to be able to easily react to a spike in inquiries about a special offer, for example, without relying on the engineering team. At the same time, neither Akash nor the DVELP team wanted the staff to have to learn error-prone JSON inside contexts, or write responses in order to get necessary information from customers.

DVELP’s creative solution was to fill out the “Action and parameters” section of every intent. This is usually reserved for collecting information from customer declarations, but was also easily adapted to implementing custom key-value pairs. This is particularly helpful in making sure that the contact center is ready to handle new promotions as they arise. As sales and special events are communicated to the contact center from the head office, staff can program specific vocabulary directly into Dialogflow, thanks to Contact Center AI, ensuring that the M&S system is immediately ready to handle related customer calls.

Rolling out the platform to the UK and Ireland

In just a few months after going live, calls are being efficiently routed to the contact center and its existing customer service platform. At the contact center, staff can quickly and easily respond to customer requests, place orders, and process returns. Thanks to the natural language capabilities of Google Cloud, a simple customer request like “order the red children’s dress in the Bath high street window in size six” not only gets correctly routed, but provides data points for future personalized interactions.

Being able to accurately recognize customer intent 92% of the time after less than four months since deployment is an important milestone for M&S. With a concurrent 89% voice-to-text accuracy rate for Dialogflow transcriptions, M&S has rolled out the successful speech recognition platform to all of its stores in UK and Ireland and customer service contact centers.

Akash is so pleased with the performance of the new Google Cloud platform that he’s focusing on what new functionality he can add next to improve customer experience even more.

“We’re working on collecting product codes from customers using natural language so we can give them stock availability details,” he shares. “We also want to use Google Cloud to enable a more conversational experience when customers are searching for help or FAQs on our website. At the same time, we’re looking at Contact Center AI and Dialogflow to provide a virtual assistant experience for our webchat journey. Thanks to our new voice solution, we can clearly understand the key issues that our customers face on a day-to-day basis; the aim now is to start solving these issues through self-service and automation.”

Advertising agencies are faced with the challenge of providing the precision data that marketers require to make better decisions at a time when customers’ digital footprints are rapidly changing. They need to transform customer information and real-time data into actionable insights to inform clients what to execute to ensure the highest campaign performance.

In this post, we’ll explore how two of our advertising agency customers are turning to Google BigQuery to innovate, succeed, and meet the next generation of digital advertising head on. 

Net Conversion eliminated legacy toil to reach new heights

Paid marketing and comprehensive analytics agency Net Conversion has made a name for itself with its relentless attitude and data-driven mindset. But like many agencies, Net Conversion felt limited by traditional data management and reporting practices. 

A few years ago, Net Conversion was still using legacy data servers to mine and process data across the organization, and analysts relied heavily on Microsoft Excel spreadsheets to generate reports. The process was lengthy, fragmented, and slow—especially when spreadsheets exceeded the million-row limit.

To transform, Net Conversion built Conversionomics, a serverless platform that leverages BigQuery, Google Cloud’s enterprise data warehouse, to centralize all of its data and handle all of its data transformation and ETL processes. BigQuery was selected for its serverless architecture, high scalability, and integration with tools that analysts were already using daily, such as Google Ads, Google Analytics, and Data Hub. After moving to BigQuery, Net Conversion discovered surprising benefits that streamlined reporting processes beyond initial expectations. For instance, many analysts had started using Google Sheets for reports, and BigQuery’s native integration with Connected Sheets gave them the power to analyze billions of rows of data and generate visualizations right where they were already working.If you’re still sending Excel files that are larger than 1MB, you should explore Google Cloud.Kenneth Eisinger
Manager of Paid Media Analytics at Net Conversion

Since modernizing their data analytics stack, Net Conversion has saved countless hours of time that can now be spent on taking insights to the next level. Plus, BigQuery’s advanced data analytics capabilities and robust integrations have opened up new roads to offer more dynamic insights that help clients better understand their audience.   

For instance, Net Conversion recently helped a large grocery retailer launch a more targeted campaign that significantly increased downloads of their mobile application. The agency was able to better understand and predict their customers’ needs by analyzing buyer behavior across the website, mobile application, and their purchase history. Net Conversion analyzed website data in real-time with BigQuery, ran analytics on their mobile app data through the Firebase’s integration with BigQuery, and enriched these insights with sales information from the grocery retailer’s CRM to generate propensity behavior models that  accurately predicted which customers would most likely install their mobile app.

WITHIN helped companies weather the COVID storm

WITHIN is a performance branding company, focused on helping brands maximize growth by fusing marketing and business goals together in a single funnel. During the COVID-19 health crisis, WITHIN became an innovator in the ad agency world by sharing real-time trends and insights with customers through its Marketing Pulse Dashboard. This dashboard was part of the company’s path to adopting BigQuery for data analytics transformation. 

Prior to using BigQuery, WITHIN used a PostgreSQL database to house its data and manual reporting. Not only was the team responsible for managing and maintaining the server, which took focus away from the data analytics, but query latency issues often slowed them down. 

BigQuery’s serverless architecture, blazing-fast compute, and rich ecosystem of integrations with other Google Cloud and partner solutions made it possible to rapidly query, automate reporting, and completely get rid of CSV files. 

Using BigQuery, WITHIN is able to run Customer Lifetime Value (LTV) analytics and quickly share the insights with their clients in a collaborative Google Sheet. In order to improve the effectiveness of their campaigns across their marketing channels, WITHIN further segments the data into high and low LTV cohorts and shares the predictive insights with their clients for in-platform optimizations.

By distilling these types of LTV insights from BigQuery, WITHIN has been able to use those to empower their campaigns on Google Ads with a few notable success stories.

• WITHIN worked with a pet food company to analyze historical transactional data to model predicted LTV of new customers. They found significant differences between product category and autoship vs single order customers, and they implemented LTV-based optimization. As a result, they saw a 400% increase in average customer LTV. 
• WITHIN helped a coffee brand increase their customer base by 560%, with the projected 12-month LTV of newly acquired customers jumping a staggering 1280%.

Through integration with Google AI Platform Notebooks, BigQuery also advanced WITHIN’s ability to use machine learning (ML) models. Today, the team can build and deploy models to predict dedicated campaign impact across channels without moving the data.  The integration of clients’ LTV data through Google Ads has also impacted how WITHIN structures their clients’ accounts and how they make performance optimization decisions.

Now, WITHIN can capitalize on the entire data lifecycle: ingesting data from multiple sources into BigQuery, running data analytics, and empowering people with data by automatically visualizing data right in Google Data Studio or Google Sheets.A year ago, we delivered client reporting once a week. Now, it’s daily. Customers can view real-time campaign performance in Data Studio — all they have to do is refresh.Evan Vaughan
Head of Data Science at WITHIN

Having a consistent nomenclature and being able to stitch together a unified code name has allowed WITHIN to scale their analytics. Today, WITHIN is able to create an internal Media Mix Modeling (MMM) tool with the help of Google Cloud that they’re trialing with their clients.

The overall unseen benefit of BigQuery was that it put WITHIN in a position to remain nimble and spot trends before other agencies when COVID-19 hit. This aggregated view of data allowed WITHIN to provide unique insights to serve their customers better and advise them on rapidly evolving conditions.

Ready to modernize your data analytics? Learn more about how Google BigQuery unlocks the insights hidden in your data.