Scaling Virtual Support in the Pandemic Era: The AI Connection

Since the early days of the pandemic, we’ve partnered with government organizations and academic institutions to serve communities at scale with Contact Center Artificial Intelligence (CCAI). I sat down with Bill MacKenzie, IT liaison for the Upper Grand School District in Ontario, and Marco Palermo, director of digital government and modernization, to discuss how they embraced CCAI to introduce scalable service delivery to residents and students alike. I’m sharing more on their stories below, and for the full overview, check out our Google Cloud Public Sector Summit session, Scaling Virtual Support in the Pandemic Era: The AI Connection.

Upper Grand School District: Answering questions with speed and accuracy

Bill MacKenzie described the Upper Grand School District’s struggles at the beginning of the pandemic, particularly helping parents with IT issues. The staff-oriented help desk was not equipped to assist parents trying to securely login for students as young as kindergarten. Without sufficient support for parents, the District was struggling to handle thousands of phone calls a day.

To alleviate the manual strain, they turned to Quantiphi, a Google Cloud partner, to implement Google Cloud Dialogflow. They were fully functional within a few weeks. The new website provided real-time responses as well as clear documentation to help parents get up and running quickly.

“In the first 10 days, we had over 5,000 hits, and the accuracy rate was 92%,” MacKenzie said. 

The district doesn’t know what the future holds, but now that they have been through the process, they are confident they now understand how to create their own bots to meet critical needs.

City of Toronto: Getting critical information to the community

Marco Palermo explained how the city of Toronto was facing a very rapid and fluid situation at the beginning of the pandemic. Getting information to constituents was extremely important, and they needed alternative channels to deliver that information.

Toronto has been committed to workforce equity and inclusivity in order to best represent the diversity of its residents. As a result, solutions had to be accessible to all. The bot handled 25,330 unique users and addressed 20,174 total questions with an 80% accurate response rate in the first four months. It’s been a huge success for the city, with plans to expand its capabilities.

Personalized response to the pandemic challenge

I noted that even before the pandemic, government leaders were asking for a way to provide more flexible personal experiences and better support outside of normal business hours. Around 60% of constituents want more self-service options and 75% would happily interact with digital resources if they could get the answers they needed.

Google Cloud CCAI  addresses these needs with a single core of intelligence that provides a consistently high-quality conversational experience—human or virtual—across all channels and platforms. It can be deployed on legacy infrastructure without upgrades in an average of two weeks.

CCAI operates with a conversational core that centralizes the ability to talk, understand, and interact, orchestrating high-quality conversations at scale. It provides services in three different ways:

• Virtual Agent AI allows natural conversation with customers to identify and address their issues effectively
• Agent Assist AI helps human agents by providing real-time turn-by-turn guidance so they can better serve constituents
• Insight AI determines metrics and trends in real-time to enable faster and more accurate insights

CCAI provides consistency across every application. It can go off-script when answering complex questions, adjusting human conversation with the capacity to handle unexpected stops and starts, odd word choices, or implied meanings. It can also handle multiple use cases for the customer, such as taking payments, updating information, providing information, and more. By allowing agencies to automate routine tasks and reduce the amount of time employees need to dedicate to answering calls, the solution created cost savings for the agency.

Explore more on this session by visiting the Public Sector Summit on-demand video.

Every organization has its own unique data culture and capabilities. Yet each is expected to use technology trends and solutions in the same way as everyone else. Your organization may be built on years of legacy applications, you may have developed a considerable amount of expertise and knowledge, yet you may be asked to adopt a new approach based on a technology trend. On the other hand, you may be on the other side of the spectrum, a digitally native organization built with engineering principles from scratch without legacy systems but expected to follow the same principles as process driven, established organizations. The question is, should we treat these organizations in the same way when it comes to data processing? In this series of blogs and papers this is what we are exploring: how to set up an organization from the first principles from data analyst, data engineering and data science point of view. In reality, there is no such organization that is solely driven by one of these but it is likely to be a combination of multiple types. What type of organization you become is then driven by how much you are influenced by each of these principles. 

When you are considering what data processing technology encompasses, take a step back and make a strategic decision based on your key goals. This can be whether you optimize for performance, cost, reduction in operational overhead, increase in operational excellence, integration of new analytical and machine learning approaches. Or perhaps you’re looking to leverage existing employees’ skills while meeting all your data governance and regulatory requirements. We will be exploring these different themes and will focus on how they guide your decision-making process. You may be coming from technologies which are solving some of the past problems and some of the terminologies may be more familiar, however they don’t scale your capabilities. There is also the opportunity cost of prioritizing legacy and new issues that arise from a transformation effort, and as a result your new initiative can set you further behind on your core business while you play catch up to an ever changing technology landscape. 

Data value chain

The key for any ingestion and transformation tool is to extract data from a source and start acting on it. The ultimate goal is to reduce the complexity and increase the timeliness of the data. Without data, it is impossible to create a data driven organization and act on the insights. As a result, data needs to be transformed, enriched, joined with other data sources, and aggregated to make better decisions. In other words, insights on good timely data mean good decisions.

While deciding on the data ingestion pipeline, one of the best approaches is to look into the volume of data, the velocity of the data, and type of data that is arriving. Other considerations include the number of different data sources you are managing, whether you need to scale to thousands of sources using generic pipelines, whether you want to create one generic pipeline but then apply data quality rules and governance. ETL tools are ideal for this use case as generic pipelines can be written and then parameterized. 

On the other hand, consider the data source. Can the data be directly ingested without transforming and formatting the data? If the data does not need to be transformed and can be ingested directly into the data warehouse as a managed solution. This not only reduces the operational costs but also allows for more timely data delivery. If the data is coming in through an unstructured format such as XML or in a format such as EBCDIC and needs to be transformed and formatted, then a tool with ETL Capabilities can be used depending on the speed of the data arrival. 

It is also important to understand the speed and time of arrival of the data. Think about your SLAs and time durations/windows that are relevant for your data ingestion plans. This would not only drive the ingestion profiles but would also dictate which framework to use. As discussed above, velocity requirements would drive the decision-making process.

Type of Organization

Different organizations can be successful by employing different strategies based on the talent that they have. Just like in sports, each team plays with a different strategy with the ultimate goal of winning. 

Organizations often need to decide on what’s the best strategy to take in respect to data ingestion and processing – whether you need to hire an expensive group of data engineers, or exploit your data wizards and analysts to enrich and transform data that can be acted on, or whether it would be more realistic to train the current workforce to do more functional/high value work rather than to focus on building generally understood and available foundational pieces.

On the other hand, the transformation part of ETL pipelines as we know it, dictates where the load will be. All of these are made a reality in the cloud native world where data can be enriched, aggregated, and joined. Loading data into a powerful and modern data warehouse means that you can already join and enrich the data using ELT. Consequently, ETL isn’t really needed in its strict terms anymore if the data can be loaded directly into the data warehouse.

All of the above was not possible in the traditional, siloed, and static data warehouses and data ecosystems whereby systems would not talk to each other or there were capacity constraints in respect to both storing and processing the data in the expensive Data Warehouse. This is no longer the case in the BigQuery world as storage is now cheap and transformations are now much more capable without constraints of virtual appliances. 

If your organization is already heavily invested into an ETL tool, one option is to use them to load BigQuery and transform the data initially within the ETL tool. Once the as-is and to-be are verified to be matching, then with the improved knowledge and expertise one can start moving workloads into BigQuery SQL, and effectively do ELT. 

Furthermore, if your organization is coming from a more traditional data warehouse that extensively relies on stored procedures and scripting, then the question that one may ask is, do I continue leveraging these skills and expertise and use these capabilities that are also provided in BigQuery? ELT with BigQuery is more natural, similar to what’s already in Teradata BTEQ, Oracle PL/SQL but migrating from ETL to ELT requires changes. This change then enables exploiting streaming use cases, such as real-time use cases in retail. This is because there is no preceding step before data is loaded and made available.

Organizations can be broadly classified under 3 types as Data Analyst Driven, Data Engineering driven, and Blended organization. We will be covering a Data Science driven organization within the Blended category.   

Data Analyst Driven

Analysts understand the business and are used to using SQL/spreadsheets. Allowing them to do advanced analytics through interfaces that they are accustomed to enables scaling. As a result, easy to use ETL tooling to bring data quickly into the target system becomes a key driver. Ingesting data directly from a source or staging area then also becomes critical as it allows analysts to exploit their key skills using ELT and increases timeliness of the data. This is commonplace with traditional EDWs and realized by extended capabilities of using Stored Procedures and Scripting. Data is enriched, transformed, and cleansed using SQL and ETL tools act as the orchestration tools. 

The capabilities brought by cloud computing on separation of data and computation changes the face of the EDW as well. Rather than creating complex ingestion pipelines, the role of the ingestion becomes, bringing data close to the cloud, staging on a storage bucket or on a messaging system before being ingested into the cloud EDW. This then releases data analysts to focus on looking into data insights using tools and interfaces that they are accustomed to. 

Data Engineering / Data Science Driven 

Building complex data engineering pipelines is expensive but enables increased capabilities. This allows creating repeatable processes and scaling the number of sources. Once complemented with cloud it enables agile data processing methodologies. On the other hand, data science organizations allow carrying out experiments and producing applications that work for specific use cases but are not often productionised or generalized. 

Real-time analytics enables immediate responses and there are specific use cases where low latency anomaly detection applications are required to run. In other words, business requirements would be such that it has to be acted upon as the data arrives on the fly. Processing this type of data or application requires transformation done outside of the target.

All the above usually requires custom applications or state-of-the-art tooling which is achieved by organizations that excel with their engineering capabilities. In reality, there are very few organizations that can be truly engineering organizations. Many fall into what we call here as the blended organization.  

Blended org

The above classification can be used on tool selection for each project. For example, rather than choosing a single tool, choose the right tool for the right workload, because this would reduce operational cost, license cost and use the best of the tools available. Let the deciding factor be driven by business requirements: each business unit or team would know the applications they need to connect with to get valuable business insights. This coupled with the data maturity of the organization would be the key to making sure the right data processing tool would be the right fit. 

In reality, you are likely to be somewhere on a spectrum. Digital native organizations are likely to be closer to being engineering driven, due to their culture and business that they are in. However, brick and mortar organizations would be closer to being analyst driven due to the significant number of legacy systems and processes they possess. These organizations are either considering or working toward digital transformation with an aspiration of having a data engineering / software engineering culture like Google. 

The blended organization with strong skills around data engineering, would have built the platform and built frameworks, to increase reusable patterns would increase productivity and then reduce costs. Data engineers focus on running Spark on Kubernetes whereas infrastructure engineers focus on container work. This in turn provides unparalleled capabilities as application developers focus on the data pipelines and even the underlying technologies or platforms changes code stays the same. As a result, security issues, latency requirements, cost demands and portability are addressed at multiple layers. 

Conclusion – What type of organization are you?

Often an organization’s infrastructure is not flexible enough to react to a fast changing technological landscape. Whether you are part of an organization which is engineering driven or analyst driven, organizations frequently look at technical requirements that inform which architecture to implement. But a key, and frequently overlooked, component needed to truly become a data-driven organization is the impact of the architecture on your data users. When you take into account the responsibilities, skill sets, and trust of your data users, you can create the right data platform to meet the needs of your IT department as well as your business.

To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. The reality is that each organization is different and has a different culture, different skills, and capabilities. Key is to leverage its strengths to stay competitive while adopting new technologies when it is needed and as it fits to your organization. 

To learn more about the elements of how to build an analytics data platform depending on the organization you are, read our paper here.

The latest round of MLPerf benchmark results have been released, and Google’s TPU v4 supercomputers demonstrated record-breaking performance at scale. This is a timely milestone since large-scale machine learning training has enabled many of the recent breakthroughs in AI, with the latest models encompassing billions or even trillions of parameters (T5, Meena, GShard, Switch Transformer, and GPT-3). 

Google’s TPU v4 Pod was designed, in part, to meet these expansive training needs, and TPU v4 Pods set performance records in four of the six MLPerf benchmarks Google entered using TensorFlow and JAX. These scores are a significant improvement over our winning submission from last year and demonstrate that Google once again has the world’s fastest machine learning supercomputers. These TPU v4 Pods are already widely deployed throughout Google data centers for our internal machine learning workloads and will be available via Google Cloud later this year.

Figure 1: Speedup of Google’s best MLPerf Training v1.0 TPU v4 submission over the fastest non-Google submission in any availability category – in this case, all baseline submissions came from NVIDIA. Comparisons are normalized by overall training time regardless of system size. Taller bars are better.¹

Let’s take a closer look at some of the innovations that delivered these ground-breaking results and what this means for large model training at Google and beyond. 

Google’s continued performance leadership

Google’s submissions for the most recent MLPerf demonstrated leading top-line performance (fastest time to reach target quality), setting new performance records in four benchmarks. We achieved this by scaling up to 3,456 of our next-gen TPU v4 ASICs with hundreds of CPU hosts for the multiple benchmarks. We achieved an average of 1.7x improvement in our top-line submissions compared to last year’s results. This means we can now train some of the most common machine learning models in a matter of seconds.

Figure 2: Speedup of Google’s MLPerf Training v1.0 TPU v4 submission over Google’s MLPerf Training v0.7 TPU v3 submission (exception: DLRM results in MLPerf v0.7 were obtained using TPU v4). Comparisons are normalized by overall training time regardless of system size. Taller bars are better. Unet3D not shown since it is a new benchmark for MLPerf v1.0.²

We achieved these performance improvements through continued investment in both our hardware and software stacks. Part of the speedup comes from using Google’s fourth-generation TPU ASIC, which offers a significant boost in raw processing power over the previous generation, TPU v3. 4,096 of these TPU v4 chips are networked together to create a TPU v4 Pod, with each pod delivering 1.1 exaflop/s of peak performance.

Figure 3: A visual representation of 1 exaflop/s of computing power. If 10 million laptops were running simultaneously, then all that computing power would almost match the computing power of 1 exaflop/s.

In parallel, we introduced a number of new features into the XLA compiler to improve the performance of any ML model running on TPU v4. One of these features provides the ability to operate two (or potentially more) TPU cores as a single logical device using a shared uniform memory access system. This memory space unification allows the cores to easily share input and output data – allowing for a more performant allocation of work across cores. A second feature improves performance through a fine-grained overlap of compute and communication. Finally, we introduced a technique to automatically transform convolution operations such that space dimensions are converted into additional batch dimensions. This technique improves performance at the low batch sizes that are common at very large scales.

Enabling large model research using carbon-free energy

Though the margin of difference in topline MLPerf benchmarks can be measured in mere seconds, this can translate to many days worth of training time on the state-of-the-art models that comprise billions or trillions of parameters. To give an example, today we can train a 4 trillion parameter dense Transformer with GSPMD on 2048 TPU cores. For context, this is over 20 times larger than the GPT-3 model published by OpenAI last year. We are already using TPU v4 Pods extensively within Google to develop research breakthroughs such as MUM and LaMDA, and improve our core products such as Search, Assistant and Translate. The faster training times from TPUs result in efficiency savings and improved research and development velocity. Many of these TPU v4 Pods will be operating at or near 90% carbon free energy. Furthermore, cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators – like TPUs – running inside them can be ~2-5X more effective than off-the-shelf systems. 

We are also excited to soon offer TPU v4 Pods on Google Cloud, making the world’s fastest machine learning training supercomputers available to customers around the world. Cloud TPUs support leading frameworks such as TensorFlow, PyTorch, and Jax, and we recently released an all-new Cloud TPU system architecture that provides direct access to TPU host machines, greatly improving the user experience. 

Want to learn more? 

Please contact your Google Cloud sales representative to request early access to Cloud TPU v4 Pods. We are excited to see how you will expand the machine learning frontier with access to exaflops of TPU computing power!

^{1. All results retrieved from www.mlperf.org on June 30, 2021. MLPerf name and logo are trademarks. See www.mlperf.org for more information. Chart uses results 1.0-1067, 1.0-1070, 1.0-1071, 1.0-1072, 1.0-1073, 1.0-1074, 1.0-1075, 1.0-1076, 1.0-1077, 1.0-1088, 1.0-1089, 1.0-1090, 1.0-1091, 1.0-1092.
2. All results retrieved from www.mlperf.org on June 30, 2021. MLPerf name and logo are trademarks. See www.mlperf.org for more information. Chart uses results 0.7-65, 0.7-66, 0.7-67, 1.0-1088, 1.0-1090, 1.0-1091, 1.0-1092.}