Background

At IKEA we have multiple places in our customer journey in various channels where different kinds of personalization can deliver a superior customer experience. Product recommendations in the shopping basket, content recommendations in editorial sections, inspirational recommendations on product pages and more. After a while in the broader “recommendations” team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behavior and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers.

Data Driven Decisions

The first step was to radically improve our ability to get high-quality quantitative information to understand how our ‘recommendation’ solutions affected personalization. We did this through high volume A/B testing on customer behaviour and after initial experimentation, we had a few key learnings:  

1. The mix of both UX and algorithms are really important for a cohesive customer experience. 
2. The quality of personalization can’t be measured in silos. Statistical significance  can be attained  by testing several groups of recommendations at once.

Once we came up with a solid framework for gathering data and acknowledged how little we knew about our customers, we were able to explore an incredible number of creative options – nothing was off the table. This was a very humbling experience, in that it opened up new perspectives for personalization, a more curious and less confined way of thinking. We learned to trust the data because it might show you things you don’t expect. 

Experimentation and Learning Framework

Our teams created ways to quickly deploy experimental modifications to our existing solution. This enabled experimentation in the front-end with the user experience, including details in headings and images. This also covered tweaks in the backend with anything from detailed manual additions or removals of recommendations to mixing and matching of various algorithms both home grown and from Recommendations AI.

This flexibility came with an overhead–more complexity and cost relative to directly retrieving recommendations from Recommendations AI. However, the benefit was that we were no longer dependent on manual evaluation of what made for a good recommendation system. We aligned on a data-driven and qualitative approach to provisioning recommendations and significantly accelerated our experimentation timeline. Together with optimization of the CI/CD pipeline this enabled the team to take an idea or hypothesis from inception to A/B testing with customers in less than half an hour.

Recommendations AI Experiments

Our team’s infrastructure was already running on GCP and when we received early access to Recommendations AI, the requirements to get started were minimal and that allowed us to start with initial tests requiring minimal effort and investment.

We started with a few use-cases and identified places where our existing recommendation algorithms needed improvement or complementary recommendations. We also explored additional ways where more useful information could be presented to the customers through personalized recommendations. 

Recommendations AI Model Combinations

While Recommendations AI might be considered a simple API to get a set of product recommendations, as we dove deeper into the solution it became apparent that it could be tweaked in several different ways to offer many fine tuning configurations to meet business goals. While too much fine tuning and customization could lead to subpar performance, in general we found that it was a great strategy to give us several versions of ML powered recommendations to work with. The further you personalize the experience, the more options you have to likely pick the best one for the customer.

Recommendations AI models like  ‘Recommended for you’, ‘Frequently Bought Together’ and ‘Others you may like’; are coupled with business goals like optimizing for conversion rate, click through rate and revenue. We experimented with many different model combinations and custom rules. All this was easily configurable right in the GCP console. One of the simplest custom configurations we used was to only recommend items that were in stock, and when items were out of stock we looked at similar items that were available to augment the experience. 

Collaboration with Google

Our collaboration with Google Cloud accelerated our learning process during experimentation. We worked closely together early in the product development. Additionally, their model provided flexibility to change direction and allow for more options than we had previously. Ultimately, this provided us a way to drastically improve our time to market with a product that produced tremendous results that we could not have accomplished on our own.

Results and Takeaways

With more personalized and real-time recommendations available we saw great success. We were able to increase the number of relevant recommendations displayed on a page by +400%. To accommodate the wider repertoire of recommendations we had to change the user experience. For example, in some places we had horizontally scrolling displays of product recommendations which were much easier for customers to use.

Another consequence of displaying more personalized recommendations was tangible improvement to conversion rate and average order value. Recommendations AI algorithms helped customers in two ways: 

1. Customers were able to find products that they liked quickly and establish their preferred choice among other options more quickly as well, giving them confidence to make a purchase through much fewer clicks. Even though we previously already had well tuned recommendations of several types, with Recommendations AI we measured +30% improvement in click through rates. 
2. Average order value saw a +2% surge with numerous examples of how Recommendations AI could help customers find both attractive and directly complementary products, expanding the customer purchase from a single product to an entire home furnishing solution.

As a direct effect of having stronger business results, the team started exploring more places in the customer journey where our growing buffet of recommendations could be used. We’d start with an initial experiment to answer if displaying recommendations in the specific context made sense at all. Frequently the data that emerged from these experiments prodded us to iterate further on what additional types of recommendations would be most appropriate to show to the customer as the customer’s behaviour evolved. Today, most of IKEA’s site recommendations are powered by Recommendations AI.

One key takeaway is that for some types of personalized recommendations there are benefits to using advanced algorithms that require a lot of high level data science and engineering competence to build since they outperform simplistic approaches. In some places, simplistic approaches work very well and in others the right decision is to not have product recommendations at all. For an effective use of product recommendations you need to have all the above options and the ability to tell when to use which one.

Next steps

When working with something so tightly related to customer experience, there is a constant change in user behaviour and new learnings to observe and adapt to. Product recommendations are rarely the main stand alone experience and frequently something that is used to help and enhance an experience. We see a lot of value in having a large toolbox of possible options and a team with a relentless focus on collaboration to improve the customer experience. We’re working directly with the Recommendations AI team and experimenting with several new features that we’re excited about. 

In the future we see opportunities of improving the customer journey through a more visual experience that inspires the customer rather than relying on customers to use their imagination to visualize groups of products together. Vision Product Search provides that and is something we’re looking into deploying next. We’ll be sharing more about our journey with Recommendations AI at the Google Cloud Retail Summit session ‘IKEA’s Approach to Building a Powerful Recommendations Engine’ on July 27th 2021.

Best wishes to all developers from the IKEA product recommendations team & the Google Recommendations AI team!

The COVID-19 pandemic tested our nation’s public benefits system in unimaginable ways. With an unprecedented 60 million individuals turning to unemployment and social services to satisfy their basic needs, state and local governments were stretched to meet the demand.

But state and local government leaders have risen to the challenge of providing their constituents with critical services. They created and administered innovative solutions, complete with wholly original processes, and brought desperately needed employment, cash, food, and healthcare support to families in crisis. These public sector heroes raised the bar during this critical time and facilitated economic recovery for our local communities, making the impossible possible for its people. 

When state and local government agencies partner with Google Cloud, they can reimagine how they deliver human services and labor services for their communities. All in a matter of weeks, instead of months or years.

Tools that solve for remote work and service delivery hurdles

Never before was there such a need for modern and accessible tools to allow a workforce to secure new jobs and operate remotely. The Virtual Career Center (VCC), built on Google Cloud, was designed with these needs in mind.

Google Workspace, including Google Meet, allows job seekers to schedule video meetings with career coaches, job recruiters, and potential employers. The Google Job Search API will enable them to explore career opportunities best suited to their skills and interests.

A thousand miles away, the Rhode Island Virtual Career Center was helping its job seekers by offering virtual meetings with career coaches, the ability to schedule meetings with prospective employers, and help on building effective resumes. Skipper, the CareerCompass RI Bot, an intelligent agent for careers, uses data and machine learning to facilitate potential new career paths and reskilling opportunities for Rhode Islanders.

In addition to enabling remote work, Google Workspace can support remote service delivery, a critical need for those involved in health and human services programs. Engaging with individuals and families is an essential component for more of these programs, including eligibility determination, assessment, and service delivery. The ability to engage directly with the public through virtual interviews, counseling sessions, telehealth, etc. improves access for those on both sides of the screen. Further, through Google Classroom, a component of Google Workspace for Education, foster and adoptive parents can obtain virtual training required for licensing. 

Tools to expedite relief funds and employment assistance

As the number of people applying for unemployment assistance skyrocketed, so did the backlog of claims requiring review. The pandemic also saw a rise in fraudulent claims, which caused delays for families waiting on legitimate payments and resulted in unnecessary spending for government agencies. Realizing the magnitude of the challenge was beyond the agencies’ current resources, and they shifted their focus to machine learning.  

To address the massive backlog of claims, SpringML partnered with Google Cloud to develop the Improper Payment Analytics solution that successfully leverages AI to help agencies identify fraudulent claims and avoid improper payments so aid can be prioritized for those who need it. Though such a tool would typically take months to develop, the team was able to launch it in just weeks, to the benefit of all stakeholders involved. Families awaiting legitimate payments received checks more quickly, and state and local governments saved millions in spending.

For instance, to address erroneous payments totaling $330 million to fraudulent applicants, the State of Ohio partnered with Google Cloud to utilize artificial intelligence and machine learning solutions to proactively identify and decline improper payment risks. As a result, the State avoided paying fraudulent claims and accelerated the payment of legitimate benefits to families facing financial hardship.

The New York State Department of Labor launched a streamlined unemployment application to allow residents to apply for pandemic unemployment assistance without the added burden of applying for unemployment insurance. Success was immediate, and the application backlog plummeted as New Yorkers got the financial aid they desperately needed. Illinois took a different approach, deploying Contact Center Artificial Intelligence (CCAI) to create virtual agents who assist with specialized calls 24/7, in multiple languages, providing turn-by-turn guidance in real-time. Conversations can be turned into insights through analytics and reporting tools that uncover key call drivers and customer sentiment. By solving for spikes in call volume, CCAI has helped process more than 1 million unemployment claims.

Automated data solutions bring immediate food and cash assistance resources

With millions out of work and out of school during the pandemic, food and cash assistance became a critical need. Applying for all of these benefits requires significant documentation that, in most cases, has historically been manually processed. Given the volume of people in need, this took a massive amount of human resources and time.

Using Document AI (DocAI), agencies can automate this highly manual process and speed up the delivery of critical benefits to individuals and families. DocAI extracts the key data, provides a confidence score in a single review pane for staff review, and automatically uploads that data to the case management system–significantly reducing manual processing. Today, these state and local government organizations turn to DocAI to eliminate the difficult paperwork application process to help meet the benefit needs of their residents now and tomorrow, as illustrated by both the Wisconsin Department of Workforce Development (DWD) and the State of Hawaii.

The Wisconsin DWD streamlined their paper unemployment insurance claims using DocAI by enabling DWD staff to receive critical data extracted from submitted applications and make decisions rapidly, saving time for both applicants and staff. The State of Hawaii also used DocAI to extract, interpret, and transport COVID-19 test result data of incoming travelers to Google Cloud instantly. As a result, Hawaii was able to welcome travelers and reopen its economy in the midst of the pandemic.

Partner with us

After making it through this very difficult period, agencies have emerged from the pandemic with stronger labor and health and human service delivery for their residents. State and local governments recognized the necessity of virtual engagement and expanded access to services to meet this critical moment, solving for work, cash, food and healthcare needs.

To learn more about Google Cloud for human services and labor, watch our video on the power of customer innovation or contact your Google Cloud sales representative. Let’s get solving together.

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research community

Over the last several decades, I’ve witnessed a lot of change in the fields of machine learning (ML) and computer science. Early approaches, which often fell short, eventually gave rise to modern approaches that have been very successful. Following that long-arc pattern of progress, I think we’ll see a number of exciting advances over the next several years, advances that will ultimately benefit the lives of billions of people with greater impact than ever before. In this post, I’ll highlight five areas where ML is poised to have such impact. For each, I’ll discuss related research (mostly from 2021) and the directions and progress we’ll likely see in the next few years.

· Trend 1: More Capable, General-Purpose ML Models
· Trend 2: Continued Efficiency Improvements for ML
· Trend 3: ML Is Becoming More Personally and Communally Beneficial
· Trend 4: Growing Benefits of ML in Science, Health and Sustainability
· Trend 5: Deeper and Broader Understanding of ML

Trend 1: More Capable, General-Purpose ML Models

Researchers are training larger, more capable machine learning models than ever before. For example, just in the last couple of years models in the language domain have grown from billions of parameters trained on tens of billions of tokens of data (e.g., the 11B parameter T5 model), to hundreds of billions or trillions of parameters trained on trillions of tokens of data (e.g., dense models such as OpenAI’s 175B parameter GPT-3 model and DeepMind’s 280B parameter Gopher model, and sparse models such as Google’s 600B parameter GShard model and 1.2T parameter GLaM model). These increases in dataset and model size have led to significant increases in accuracy for a wide variety of language tasks, as shown by across-the-board improvements on standard natural language processing (NLP) benchmark tasks (as predicted by work on neural scaling laws for language models and machine translation models).

Many of these advanced models are focused on the single but important modality of written language and have shown state-of-the-art results in language understanding benchmarks and open-ended conversational abilities, even across multiple tasks in a domain. They have also shown exciting capabilities to generalize to new language tasks with relatively little training data, in some cases, with few to no training examples for a new task. A couple of examples include improved long-form question answering, zero-label learning in NLP, and our LaMDA model, which demonstrates a sophisticated ability to carry on open-ended conversations that maintain significant context across multiple turns of dialog.

Transformer models are also having a major impact in image, video, and speech models, all of which also benefit significantly from scale, as predicted by work on scaling laws for visual transformer models. Transformers for image recognition and for video classification are achieving state-of-the-art results on many benchmarks, and we’ve also demonstrated that co-training models on both image data and video data can improve performance on video tasks compared with video data alone. We’ve developed sparse, axial attention mechanisms for image and video transformers that use computation more efficiently, found better ways of tokenizing images for visual transformer models, and improved our understanding of visual transformer methods by examining how they operate compared with convolutional neural networks. Combining transformer models with convolutional operations has shown significant benefits in visual as well as speech recognition tasks.

The outputs of generative models are also substantially improving. This is most apparent in generative models for images, which have made significant strides over the last few years. For example, recent models have demonstrated the ability to create realistic images given just a category (e.g., “irish setter” or “streetcar”, if you desire), can “fill in” a low-resolution image to create a natural-looking high-resolution counterpart (“computer, enhance!”), and can even create natural-looking aerial nature scenes of arbitrary length. As another example, images can be converted to a sequence of discrete tokens that can then be synthesized at high fidelity with an autoregressive generative model.

Because these are powerful capabilities that come with great responsibility, we carefully vet potential applications of these sorts of models against our AI Principles.

Beyond advanced single-modality models, we are also starting to see large-scale multi-modal models. These are some of the most advanced models to date because they can accept multiple different input modalities (e.g., language, images, speech, video) and, in some cases, produce different output modalities, for example, generating images from descriptive sentences or paragraphs, or describing the visual content of images in human languages. This is an exciting direction because like the real world, some things are easier to learn in data that is multimodal (e.g., reading about something and seeing a demonstration is more useful than just reading about it). As such, pairing images and text can help with multi-lingual retrieval tasks, and better understanding of how to pair text and image inputs can yield improved results for image captioning tasks. Similarly, jointly training on visual and textual data can also help improve accuracy and robustness on visual classification tasks, while co-training on image, video, and audio tasks improves generalization performance for all modalities. There are also tantalizing hints that natural language can be used as an input for image manipulation, telling robots how to interact with the world and controlling other software systems, portending potential changes to how user interfaces are developed. Modalities handled by these models will include speech, sounds, images, video, and languages, and may even extend to structured data, knowledge graphs, and time series data.

Often these models are trained using self-supervised learning approaches, where the model learns from observations of “raw” data that has not been curated or labeled, e.g., language models used in GPT-3 and GLaM, the self-supervised speech model BigSSL, the visual contrastive learning model SimCLR, and the multimodal contrastive model VATT. Self-supervised learning allows a large speech recognition model to match the previous Voice Search automatic speech recognition (ASR) benchmark accuracy while using only 3% of the annotated training data. These trends are exciting because they can substantially reduce the effort required to enable ML for a particular task, and because they make it easier (though by no means trivial) to train models on more representative data that better reflects different subpopulations, regions, languages, or other important dimensions of representation.

All of these trends are pointing in the direction of training highly capable general-purpose models that can handle multiple modalities of data and solve thousands or millions of tasks. By building in sparsity, so that the only parts of a model that are activated for a given task are those that have been optimized for it, these multimodal models can be made highly efficient. Over the next few years, we are pursuing this vision in a next-generation architecture and umbrella effort called Pathways. We expect to see substantial progress in this area, as we combine together many ideas that to date have been pursued relatively independently.

When you choose to run your business on Google Cloud you benefit from the same planet-scale infrastructure that powers Google’s products such as Maps, YouTube, and Workspace. 

We have picked 10 ways in which Google Cloud Infrastructure services outshine alternatives in the market in how they simplify your operations, save money, and secure your data. 

1. Custom Machine Types means no wasted resources

Compute Engine offers predefined machine types that you can use when you create a VM instance. A predefined machine type has a preset number of vCPUs and a preset amount of memory; each type is billed at a set price as described on the Compute Engine pricing page. 

If predefined machine types don’t meet your needs, you can create a VM instance with a custom number of vCPUs and custom amount of memory, effectively building a custom machine type. Custom machine types are available only for general-purpose machine families. When you create a custom machine type, you are deploying a custom machine type from the E2, N2, N2D, or N1 machine family on GCP.  No other leading cloud vendor offers custom machine types so extensively.

Custom machine types are a good idea for workloads that aren’t a good fit for the predefined machine types and for workloads that require more processing power or memory but don’t need all of the upgrades provided by the next machine type level. This translates into lower operating costs.   They are also useful for controlling software licensing costs that are based on the number of underlying compute cores. 

Jeremy Lloyd, Infrastructure and Application Modernization Lead at Appsbroker, a Google partner: 

“Custom machine types coupled with Google’s StratoZone data center discovery tool provides Appsbroker with the flexibility we need to provide cost efficient virtual machines matched to a virtual machine’s actual utilization. As a result, we are able to keep our customers’ operating costs low while still providing the ability to scale as needed.”

2. Compute Engine Virtual Machines are optimized for scale-out workloads 

For scale-out workloads, T2D, the first instance type in the Tau VM family, is based on 3rd Gen AMD EPYC processors and leapfrogs VMs for scale-out workloads of any leading public cloud provider today, both in terms of performance and price-performance. Tau VMs offer 56% higher absolute performance and 42% higher price-performance compared to general-purpose VMs from any leading public cloud vendor (source). The x86 compatibility provided by these AMD EPYC processor-based VMs gives you market-leading performance improvements and cost savings, without having to port your applications to a new processor architecture. Sign up here  if you are interested in trying out T2D instances in Preview. 

For SAP HANA, Google Cloud has demonstrated with SAP how we can run the world’s largest scale-out HANA system in the public cloud (96TB).   With such innovation, you are covered as your business grows exponentially.

3. Largest single node GPU-enabled VM

Google is the only public cloud provider to offer up to 16 NVIDIA A100 GPUs in a single VM, making it possible to train very large AI models. Users can start with one NVIDIA A100 GPU and scale to 16 GPUs without configuring multiple VMs for single-node ML training, without crossing the VM layer. 

Additionally, customers can choose smaller GPU configurations—1, 2, 4 and 8 GPUs per VM—providing the flexibility to scale their workload as needed. 

The A2 VM family was designed to meet today’s most demanding applications—workloads like CUDA-enabled machine learning (ML) training and inference, for example. This family is built on the A100 GPU which offers up to 20x the compute performance compared to the previous generation GPU and comes with 40 GB of high-performance HBM2 GPU memory. To speed up multi-GPU workloads, the A2 VMs use NVIDIA’s HGX A100 systems to offer high-speed NVLink GPU-to-GPU bandwidth that delivers up to 600 GB/s. A2 VMs come with up to 96 Intel Cascade Lake vCPUs, optional Local SSD for workloads requiring faster data feeds into the GPUs and up to 100 Gbps of networking. A2 VMs provide full vNUMA transparency into the architecture of underlying GPU server platforms, enabling advanced performance tuning. Google Cloud offers these GPUs globally. 

4. ​​Non-disruptive maintenance means you worry less about planned downtime

Compute Engine offers live migration (non-disruptive maintenance) to keep your virtual machine instances running even when a host system event, such as a software or hardware update, occurs. Google’s Compute Engine live migrates your running instances to another host in the same zone without requiring your VMs to be rebooted. Live migration enables Google to perform maintenance that is integral to keeping infrastructure protected and reliable without interrupting any of your VMs. When a VM is scheduled to be live-migrated, Google provides a notification to the guest that a migration is imminent. 

Live migration keeps your instances running during:

• Regular infrastructure maintenance and upgrades
• Network and power grid maintenance in the data centers
• Failed hardware such as memory, CPU, network interface cards, disks, power, and so on. This is done on a best-effort basis; if a hardware component fails completely or otherwise prevents live migration, the VM crashes and restarts automatically and a hostError is logged.
• Host OS and BIOS upgrades
• Security-related updates
• System configuration changes, including changing the size of the host root partition, for storage of the host image and packages

Live migration does not change any attributes or properties of the VM itself. The live migration process transfers a running VM from one host machine to another host machine within the same zone. All VM properties and attributes remain unchanged, including internal and external IP addresses, instance metadata, block storage data and volumes, OS and application state, network settings, network connections, and so on. This has the benefit of reducing operational and maintenance overhead, helps you build a more robust security posture where infrastructure can be consciously revamped from a known good state and minimizes risks for advanced persistent threats. 

Refer to Lessons learned from a year of using live migration in production on Google Cloud from the Google engineering team.

5. Trusted Computing: Shielded VMs guard you against advanced, persistent attacks

Establishing trust in your environment is multifaceted, involving hardware and firmware, as well as host and guest operating systems. Unfortunately, threats like boot malware or firmware rootkits can stay undetected for a long time, and an infected virtual machine can continue to boot in a compromised state even after you’ve installed legitimate software. 

Shielded VMs can help you protect your system from attack vectors like:

• Malicious guest OS firmware, including malicious UEFI extensions
• Boot and kernel vulnerabilities in the guest OS
• Malicious insiders within your organization

To guard against these kinds of advanced persistent attacks, Shielded VMs use:

• Unified Extensible Firmware Interface (UEFI) BIOS: Helps ensure that firmware is signed and verified
• Secure and Measured Boot: Helps ensure that a VM boots an expected, healthy kernel
• Virtual Trusted Platform Module (vTPM): Establishes root-of-trust, underpins Measured Boot, and prevents exfiltration of vTPM-sealed secrets
• Integrity Monitoring: Provides tamper-evident logging, integrated with Stackdriver, to help you quickly identify and remediate changes to a known integrity state

The Google approach allows customers to deploy Shielded VMs with only a simple click, thereby easing implementation. 

6. Confidential Computing encrypts data while in use

Google Cloud was a founding member of the Confidential Computing Consortium. Along with encryption of data in transit and at rest using customer-managed encryption keys (CMEK) and customer-supplied encryption keys (CSEK), Confidential VM adds a “third pillar” to the end-to-end encryption story by encrypting data while in use. Confidential Computing uses processor-based technology that allows data to be encrypted in use while it is being processed in the public cloud. Confidential VM allows you to to encrypt memory in use on a Google Compute Engine VM by checking a single checkbox. 

All Confidential VMs support the previously mentioned Shielded VM features under the covers—you can think of Shielded VM as helping to address VM integrity, while Confidential VM addresses the memory encryption aspect which relies on CPU features. With the confidential execution environments provided by Confidential VM and AMD Secure Encrypted Virtualization (SEV), Google Cloud keeps customers’ sensitive code and other data encrypted in memory during processing. Google does not have access to the encryption keys. In addition, Confidential VM can help alleviate concerns about risk related to either dependency on Google infrastructure or Google insiders’ access to customer data in the clear. 

See what Google Cloud partners say about Confidential Computing here. 

7. Advanced networking delivers full-stack networking and security services with fast, consistent, and scalable performance

Google Cloud’s network delivers low latency, reduces operational costs and ensures business continuity, enabling organizations to seamlessly scale up or down in any region to meet business needs. Our planet-scale network uses advanced software-defined networking and security with edge caching services to deliver fast, consistent, and scalable performance. With 28 regions, 85 zones, and 146 PoPs connected by 16 subsea fiber cables around the world, Google Cloud’s network offers a full stack of layer 1 to layer 7 services for enterprises to run their workloads anywhere. Enterprises can be assured that they have best-in-class networking and security services connecting their VMs, containers, and bare metal resources in hybrid and multi-cloud environments with simplicity, visibility, and control. 

Google Cloud’s network has protected customers from one of the world’s largest DDoS attacks at 2.54 Tbps. With our multi-layer security architecture and products such as Cloud Armor, our customers ran their business with no disruptions. Furthermore, our recent integration of Cloud Armor with reCAPTCHA Enterprise adds best-in-class bot and fraud management to prevent volumetric attacks. Cloud Armor is deployed with our Cloud Load Balancer and Cloud CDN, extending the secure benefits at the network edge for traffic coming into Google Cloud so customers have security, performance, and reliability all built in. Furthermore, we are excited to offer Cloud IDS in preview, which was co-developed with security industry leader, Palo Alto Networks, to run natively in Google Cloud. 

Our advanced networking capabilities also extends to GKE and Anthos networking. With the GKE Gateway controller, customers can manage internal and external HTTPS load balancing for a GKE cluster or a fleet of GKE clusters with multi-tenancy while maintaining centralized admin policy and control. Unlike other Kubernetes offerings, we offer eBPF dataplane which brings powerful tooling such as Kubernetes network policy and logging to GKE. eBPF is known to kernel engineers as a “superpower” for its unique architecture to load and unload modules in kernel space, and now this capability is built in with Google Cloud networking. 

For observability and monitoring, our customers deploy Network Intelligence Center, Google Cloud’s comprehensive network monitoring, verification and optimization platform. With four key modules in Network Intelligence Center, and several more to come, we are working towards realizing our vision of proactive network operations that can predict and heal network failures, driven by AI/ML recommendations and remediation. Network Intelligence Center provides unmatched visibility into your network in the cloud along with proactive network verification. Centralized monitoring cuts down troubleshooting time and effort, increases network security and improves the overall user experience.  

8. Regional Persistent Disk for High Availability

Regional Persistent Disk is a storage option that provides synchronous replication of data between two zones in a region. Regional Persistent Disks can be a great building block if you need to ensure high availability of your critical applications as they offer cost-effective durable storage and replication of data between two zones in the same region. 

Regional Persistent Disks are also easy to set up within the Google Cloud Console. If you are designing robust systems or high availability services on Compute Engine, Regional Persistent Disks combined with other best practices such as backing up your data using snapshots enable you to build an infrastructure that is highly available and recoverable in a disaster. Regional Persistent Disks are also designed to work with regional managed instance groups. In the unlikely event of a zonal outage, Regional Persistent Disks allow continued I/O through failover of your workloads to another zone. Regional Persistent Disks can help meet zero RPO and near-zero RTO requirements and other stringent SLAs that your critical applications might require by maximizing application availability and protection of data during events such as host/VM failures and zonal outages. 

9. Cloud Storage’s single namespace for dual-region and multi-region means managing regional replication is incredibly simple

Similar to how Persistent Disk makes data more available by replicating data across zones, Cloud Storage provides similar benefits for object storage. Cloud Storage within a region is cross-zone by definition, reducing the risk that a zonal outage would take down your application. Cloud Storage adds to this by also providing a cross-region option that can protect against a regional outage and gets your data closer to distributed users. This comes in the form of Dual-region or Multi-region settings for a bucket. These are the simplest to implement cross-region replication offerings in the industry—just a simple button or API call to enable them. In addition to being simple to implement, they offer an added advantage of using a single bucket name that spans regions. 

This is unique in the industry. Competitive offerings currently require setting up and managing two distinct buckets, one in each region and they don’t offer the strong consistency properties Cloud Storage offers across regions. Operations and app development are burdened by this design. Google’s single namespace approach dramatically simplifies application development (the app runs on single region or dual/multi-region without any changes), and provides simpler application restarts and testing for DR.

10. Predictive autoscaling 

Customers use predictive autoscaling to improve response times for applications with long initialization times or for applications with workloads that vary predictably with daily or weekly cycles. When you enable predictive autoscaling, Compute Engine forecasts future load based on your Managed Instance Group’s history and scales out the MIG’s in advance of predicted load, so that new instances are ready to serve when the load arrives. Without predictive autoscaling, an autoscaler can only scale a group reactively, based on observed changes in load in real time. 

With predictive autoscaling enabled, the autoscaler works with real-time data as well as with historical data to cover both the current and forecasted load. Forecasts are refreshed every few minutes (faster than competing clouds) and consider daily and weekly seasonality, leading to more accurate forecasts of load patterns.

For more information, see How predictive autoscaling works and Checking if predictive autoscaling is suitable for your workload.

These are just a few examples of customer-centric innovation that set Google Cloud infrastructure apart.  Bring your applications and let the platform work for you.   

Get started by learning about your options for migration, or talk to our sales team to join the thousands of customers who have embarked upon this journey.

Acknowledgement

Special thanks to Dheeraj Konidena (Google) for contributing to this article.

The combination of a vital need for IT experts among businesses and a digital skills gap is making lifelong learning increasingly critical. Beyond professional development, learning new skills offers additional rewards from building peer connections to boosting your creativity. That’s why in 2020 Krishna Deepak Nallamilli and I launched KIMO.ai to reimagine how people approach learning, especially in developing markets. Our team is building the artificial intelligence needed to generate individual learning paths through a wide range of quality digital learning content.

Google Cloud and its Startup Program have been instrumental in connecting our team with the tools, people, processes, and best practices to grow our business. 

Existing learning platforms lack engagement

Outside of traditional education settings, massive open online learning courses (MOOCs)—often modeled after university courses—can provide a flexible and affordable way to upskill or reskill. But the vast majority of people who participate in MOOC programs fail to complete courses. Based on our research, the challenge with existing learning platforms is a lack of engagement, primarily caused by limited direction on which skills to learn, whether AI, fintech, blockchain, or other in-demand disciplines.   

We’ve also received feedback that many corporate learning management systems–developed as online training systems to upskill employees–tend to be poorly designed and time-consuming to use. 

Overall, a significant challenge with most existing learning platforms is that they’re generic. For example, suppose you’re interested in learning about AI. In that case, you need AI-related coursework that applies to your industry and the job you want because AI in medicine is vastly different from AI in financial services. Today’s online learning options typically take a one-size-fits-all approach and fail to capture the nuances of what learners really need to get ahead. 

Building a future-proof learning platform 

The commitment to highly personalized, accessible learning inspired KIMO.ai, a platform that we believe is the future of education. Depending on your goals, current skills, location, and other factors, our AI-based platform will identify which coursework (and where to find those classes) to build the skills you need. The more personalized, relevant learning recommendations even take into account people’s preferences for podcasts, MOOCs, books, articles, videos, courses, publications, and more.  

In a mix of cooperation and competition we call “coopetition,” KIMO.ai will regularly recommend courses from other established online learning systems if, based on our automated assessment, it’s the best option for a learner. There’s also the option to access free content only. 

Google cultural alignment fosters trust

Our platform started with one developer exploring NLP models and Google APIs. As we’ve grown our team and launched our beta to 110,000 users in developing markets, we discovered there is a lot of interest in our platform, and we believe we can make a significant impact. In feedback forums, we also learned that we need to focus our efforts on the mobile experience to improve engagement since 99% of the beta testers use mobile devices. 

Beyond our team’s high level of trust in Google Cloud solutions, our team also appreciates the cultural alignment with Google. We value Google’s developer-centric approach and rely on tools like Dataflow for batch data processing and Cloud TPU to reliably run machine learning models with AI services on Google Cloud. We also build all of our deployments on Google Kubernetes Engine (GKE), which makes it easy to manage all our containerized workloads 

On the front end, Google App Engine makes it easy to deploy apps and experiment, and it integrates seamlessly with Firebase for authentication and more. BigQuery is our serverless data warehouse that efficiently scales to support the millions of articles, videos, and other learning resources we need to analyze to provide the targeted coursework recommendations our learners require.

As we grow our business having a network of trusted advisors is also extremely valuable. By working closely with DoIT International, the 2020 Google Cloud Global Reseller Partner of the Year, our team has access to their cloud, Kubernetes, and machine learning expertise. DoIT has already helped us quickly resolve IT issues and create analytics dashboards that give us insights to continually enhance our services. 

Building for a growing industry

The dynamic edtech market is growing rapidly and estimated to become an $11B industry by 2025. We’re proud to be part of the next wave of personalized education that has the potential to empower people in developing markets and beyond to grow their skills with coursework tailored to their exact needs and how they like to learn. This year, we will deliver our platform to at least 400,000 more people. We’re excited to see how they use it and where it takes them. 

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