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. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

With over ⅓ of the food in the USA ending up as waste according to the USDA, it is a compelling challenge to address this travesty. What will happen to hunger, food prices, trash reduction, water consumption, and overall sustainability when we stop squandering this abundance?

Beginning with the departure from the farm to the back of the store, the freshness clock continues to run. Grocers work very hard to purchase high quality produce items for their customers and the journey to the shelf can take a toll in both quality and remaining shelf life. Suppliers focus on delivering their items through the arduous supply chain journey to the store with speed and gentle handling. The baton is then passed to the store to unload and present the items to customers with care to sell through each lot significantly before the expiration or sell by date. This is to ensure that the time spent in the customer’s home is ample to ensure a great eating experience as well. Food waste is a farm to fork problem with opportunity at every step of the chain, but today we will focus on the segment that the grocery industry oversees.

With the complexities of weather, geopolitical issues, distribution, sales variability, pricing, promotions, and inventory management, it seems daunting to impact waste. Fortunately, data analytics and machine learning in the cloud is a powerful weapon in the fight against food waste. Data Scientists harness knowledge to draw meaning from data turning that data into decision driving information.

One key Google has been working on to accelerate value is to break down data silos and leverage machine learning to realize better outcomes, using our Google Data Cloud platform. This enables better planning through demand forecasting, Inventory management, assortment planning, and dynamic pricing and promotions.

That sounds great but how does it work?

Let’s walk through a day in the life journey to see how the integrated Google Data Cloud platform can change the game for good. Our friendly fictitious grocer FastFreshFood is committed to selling high quality perishable items to their local market. Their goal is to minimize food waste and maximize revenue by selling as much perishable fresh food as possible before the sell by date. Our fictitious grocer in partnership with Google Cloud could build a solution that will take a significant bite out of their food waste volume and better satisfy customers.

1. Sales through the register and online are processed in real time with Datastream, Dataflow to keep an accurate perpetual inventory by minute of every single item.
2. A Demand forecasting model using machine learning algorithms in BigQuery then identifies needs for back room replenishment, so Direct Store Delivery and daily store Distribution Centers manage ordering more efficiently to ensure just the right amount of each product each day.
3. Realtime reporting dashboards in Looker with alerting capabilities enable the system to operate with strong associate support and understanding. The reporting suite shows inventory levels into the future, daily orders, and at risk items.

The pricing algorithm could also alert store leadership concerning any items that will not sell through and suggest real time in store specials resulting in zero waste at shelf and maximized revenue.

This approach is not just for perishable categories and is a pattern that works well for in-store produced items and center store items. The key point is that by bringing ML/AI to difficult business problems grocers are reinventing what is possible for both their profitability and sustainability.

The technical implementation of this design pattern in Google Cloud leverages Datastream, Dataflow, BigQuery and Looker products, it is detailed in a technical tutorial accompanying this blog post.

In partnership with Google Cloud, retailers can solve complex problems with innovative solutions to achieve higher quality, lower cost, and provide great customer experiences. To learn more from this and other use cases, please visit our Design Patterns website.

Curious to learn more?

We’re excited to share what we know about tackling food waste at Google, a topic we’ve been working on in the last decade as we’ve embarked on reducing our own food waste in our operations in over 50 countries in the world. The Google Food for Good team works exclusively on Google Cloud Platform with our partners on this topic. Two additional articles below.

Silos are for food, not for data – tackling food waste with technology
This business Cloud blog directly addresses information silos that currently exist across many nodes in the food system and how to break down cultural and organizational barriers to sharing.

“Unsiloing” data to work toward solving food waste and food insecurity
This follow-on technical Cloud blog articulates the path to setting up data pipelines, translating between data sets (not everyone calls a tomato a tomato!) and making sense of emergent insights.

Investing in Artificial Intelligence (AI) can bring a competitive advantage to your organization. If you’re in charge of an AI or Data Science team, you’ll want to measure and maximize the value that you’re providing. Here is some advice from our years of experience in the field. 

A checklist to embark on a project: 

As you embark on projects we’ve found it’s good to have the following areas covered: 

• Have a customer. It’s important to have a customer for your work, and that they  agree with what you’re trying to achieve. Be sure to know what value you’re delivering to them. 
• Have a business case.  This will rely on estimates and assumptions, and may take no more than a few minute’s work.  You should revise this, but always know what justifies your team’s effort, and what you (and your customer) expect to get in return. 
• Know what process you will change or create. You’ll want to put your work in production, so you have to be clear about what business operations are changing or created around your work and who needs to be involved to make it happen
• Have a measurement plan. You’ll want to show that ongoing work is impacting some relevant business indicator. Measure and show incremental value. The goal of these measurements is to establish what has changed because of your project that would otherwise not have changed. Be sure to account for other factors like seasonality or other business changes that may affect your measurements.
• Use all the above to get your organization’s support for your team and your work. 

What measures to use?

As you start the work, what measures and indicators can you use to show that your team’s work is useful for your organization?

How many decisions you make. A major function of ML is to automate and optimize decisions: which product to recommend, which route to follow, etc. Use logs to track how many decisions your systems are making. 

Changes to revenue or costs. Better and quicker decisions often lead to increased revenue or savings. If possible, measure it directly, otherwise estimate it (for example fuel costs saved from less distance traveled, or increased purchases from personalized offers). 

As an example, the Illinois Department of Employment Security is using Contact Center AI to rapidly deploy virtual agents to help more than 1 million citizens file unemployment claims. To measure success the team tracked the two outcomes:  (1) the number of web inquiries and voice calls they were able to handle, and (2) the overall cost of the call center after the implementation. Post implementation, they were able to observe more than 140,000 phone and web inquiries per day and over 40,000 after-hours calls per night. They  also anticipate an estimated annual cost savings of $100M based on an initial analysis of IDES’s virtual agent data (see more in the link to case study).

Implementation costs. The other side of increased revenue or savings, is to put your achievements in the context of how much they cost. Show the technology costs that your team incurs and, ideally, how you can deliver more value, more efficiently. 

How much time was saved.  If the team built a routing system then it saved travel time, if it built an email classifier then it saved reading time, etc. Quantify how many hours were given back to the organization thanks to the efficiency of your system. 

In the medical field, quicker diagnostics matter. Johns Hopkins University’s Brain Injury Outcomes (BIOS) Division has focused on studying brain hemorrhage aiming to improve medical outcomes. The team identified the time to insights as a key metric in measuring business success. They experimented with a range of cloud computing solutions like Dataflow, Cloud Healthcare API, Compute Engine, and AI Platform for distributed training to accelerate iterations. As a result, in their recent work they were able to accelerate insights from scans from approximately 500 patients from 2,500 hours to 90 minutes.

How many applications your team supports. Some of your organization’s operations don’t use ML (say reconciling financial ledgers) but others do. Know how many parts of your organization benefit from the optimization and automation your team builds.

User experience. You may be able to measure your customer’s experience: fewer complaints, better reviews, reduced latency, more interactions, etc. This is valid both for internal and external stakeholders. At Google we measure usage and regularly ask for feedback on any internal system or process.

One of our customers, The City of Memphis, is using VisionAI and ML to tackle a common but very challenging issue: identifying and addressing potholes.  The implementation team identified the percentage increase of potholes identified as one of the key metrics along with accuracy and cost savings. The solution captures video footage from it’s public vehicles and leverages Google Cloud capabilities like Compute Engine, AI Platform, and BigQuery to automate the review of videos.  The project increased  pothole detection by 75% with over 90% accuracy. By measuring and demonstrating these outcomes, the team proved the viability of a cost-effective, cloud-based machine learning model and is looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents. 

Acknowledgements

Filipe and Payam would like to thank our colleague and co-author Mona Mona (AI/ML Customer Engineer, Healthcare and lifesciences) who contributed equally to the writing.