Siemens aims to shape the future—and has the scale, culture, and know-how to realize its ambitions. The Germany-headquartered industrial manufacturing business operates in more than 120 countries and focuses on electrification, automation, and digitalization. Siemens’ extensive portfolio includes industrial production digitalization and automation technologies, diagnostic and therapeutic imaging for healthcare, and building automation and management technologies.

“All our solutions are united by the fact they impact the way people live their lives,” says Stephanie Morton, Siemens’ Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management.

“‘Engineered for life’ is one of our brand claims.”

Hiring “future makers”

Hiring thinkers, dreamers, and doers hungry to transform businesses, industries, and lives is key to Siemens’ success. The business calls its people “future makers” and employs more than 370,000 of them worldwide.

Recruiting for a workforce this size is no easy task. Siemens’ global jobs and careers website has about 5,000 open positions at any one time, and the organization receives 2 million applications to fill 35,000 positions per year.

However, by 2017, the website was experiencing problems that hampered the talent acquisition team’s efforts to fill roles promptly with the right candidates. Its standard keyword matching technology could not optimize the results provided to job seekers, frustrating potential candidates and increasing the workload for recruiters.

“With Cloud Talent Solution, we saw a 30 percent uplift in candidate conversions from search to application.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

“We found the language we used about our jobs internally was often not the same language job seekers used,” explains Morton. “For example, we may have an engineering position open for our MindSphere cloud-based IoT operating system. We would post this on our jobs and careers website as ‘MindSphere engineer.’ Unfortunately, a job seeker using more general terms such as ‘IoT engineer’ may miss this advertisement.”

In addition, without intelligence and context, the keyword matching technology could not determine job seeker search intentions from misspelled terms. This became more of a problem as job seekers increasingly used mobile devices to conduct searches.

With keyword searches also generating pages and pages of results, job seekers often succumbed to the temptation to apply for every position returned. This left the talent acquisition team drowning in messages and applications.

“We experienced issues around seniority as well—for example, people looking for senior legal roles were receiving irrelevant results for internships or junior roles,” says Morton.

Change needed

These problems could not persist for a team charged with continuously improving candidate experiences. The team opened discussions with Jibe, a recruitment platform provider that operated Siemens’ external career websites. Jibe had collaborated with Google to integrate Cloud Talent Solution—a solution that uses machine learning to better understand job content and job seeker intent—into its platform.” Jibe offered the integrated solution to Siemens as one of its most forward-thinking customers,” says Morton.

Morton’s team reviewed Cloud Talent Solution and was immediately excited by its potential to quickly make a profound difference in its recruitment activities—without requiring recruiters and other team members to invest considerable time and effort. Cloud Talent Solution could enable the website to understand the broad intent of a job seeker—including synonymous positions—and deliver considerably more relevant results.

“We can authoritatively say Cloud Talent Solution is improving the experience for job seekers; they are conducting more searches, those searches are more effective, and more of them are converting to job applications.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

After Cloud Talent Solution expanded to encompass more than 100 languages—enabling Siemens to offer a consistent experience to candidates from a range of countries and backgrounds—Morton’s team gave Jibe and Google the green light to proceed.

“We had two key issues to address during the implementation,” says Morton. “The first was to make sure Google Cloud was aware of any Siemens-specific recruitment terms. For example, we have German-language posts that include terms commonly used internally regarding internships and junior positions. We worked closely with our partners to make sure these terms were represented in the algorithm to complement the vast constellation of already-mapped job titles in Cloud Talent Solution.”

Siemens also needed to quantify Cloud Talent Solution benefits to sell the service internally within the business and win support to proceed beyond a pilot. “Because Cloud Talent Solution was effectively an invisible layer behind our website, we had to test its impact,” says Morton.

A 30 percent uplift

Working with Jibe, Siemens conducted A/B testing. Five percent of candidates in the test group used the keyword-based search experience and 95 percent of candidates the search experience powered by Cloud Talent Solution. The testing accommodated changes in candidate queries over time and context.

“With Cloud Talent Solution, we saw a 30 percent uplift in conversions from search to application,” says Morton.

“We can authoritatively say Cloud Talent Solution is improving the experience for job seekers; they are conducting more searches, those searches are more effective, and more of them are converting to job applications,” she adds. “Anecdotally, recruiters are seeing fewer applications from job seekers who have applied to long lists of positions returned from search queries.”

“Partnering with market-leading providers like Google Cloud can help us stay one step ahead of the competition over the journey.”

—Stephanie Morton, Global Talent Acquisition Manager: Strategy, Technology & Talent Relationship Management, Siemens

In the longer term, Siemens plans to monitor the impact of Cloud Talent Solution on metrics such as candidate retention rates and manager satisfaction.

With Cloud Talent Solution well established within the business, Siemens is now working with Google Cloud to add new features—including voice assistants—to enhance the candidate experience. “Helping candidates laser in on the right positions and make successful connections is something we’re doing everything we can to make happen,” says Morton.

More broadly, Cloud Talent Solution and Jibe are helping Siemens’ talent acquisition and the rest of its human resources function build its reputation and execute its strategy. “We are becoming a function driven by delivering amazing technology-based solutions to our employees and our candidates,” says Morton. “People within Siemens are very impressed to see human resources leading the way in working with Google Cloud to deliver a solution like this at a global scale.”

“The more light-touch exercises like this we can do, the fewer big, expensive, time-consuming initiatives we need to take,” she says. “Furthermore, partnering with market-leading providers like Google Cloud can help us stay one step ahead of the competition over the journey.”

Introduction

In my experience, I have observed that it’s common in machine learning to surrender to the process of experimenting with many different algorithms in a trial and error fashion, until you get the desired result. My peers and I at Google have a People and Planet AI YouTube series where we talk about how to train and host a model for environmental purposes using Google Cloud and Google Earth Engine. Our focus is inspiring people to use deep learning, and if we could rename the series, we would call it AI for Minimalists since we would recommend artificial neural networks for most of our use cases. And so in this episode we give an overview of what deep learning is and how you can use it for tracking deforestation in supply chains. I also included a summary of the architecture and products you can use in this blog that I presented at the 2022 Geo For Good Summit. For those of you interested in diving even deeper into code, please visit our end-to-end sample (click “open in colab” at the bottom of the screen to view this tutorial in a notebook format).

What’s included in this article

• What is Deep Learning?
• Measuring deforestation in extractive supply chains with ML
• When to build a custom model outside of Earth Engine?
• How to build a model with Google Cloud & Earth Engine?
• Try it out!

What is Deep Learning?

Out of the many ML algorithms out there, I’m happy to share that deep learning or artificial neural networks is a technique that can be used for almost any supervised learning job.

In supervised learning, you tell a computer the right answers to look for, through examples. Deep learning is very flexible, and is a great go-to algorithm. Especially for images, audio, or video files which are types of multidimensional data. This is because each of these data types have one or more dimensions with specific values for each point.

And training a model to classify tree species using satellite images is kind of like an image segmentation problem, where every pixel in the image is classified.

“Deep learning approaches problems differently”

David Cavazos, Developer Programs Engineer

There’s no writing a function with explicit & sequential steps that reviews every single pixel one by one for every image, as traditional software development does. Let’s say you wish to build a model that classifies tree species. You don’t spend time coding all the instructions, but instead give a computer examples of images with tree species labels, and let it learn from these examples. And when you want to add more species, it’s as simple as adding new images of that species to retrain the model.

Measuring deforestation in extractive supply chains with ML

So let’s say we would like to measure deforestation using deep learning; to get started with building a model we first need a dataset that includes satellite images with an even amount of labels marking where there are trees and where there aren’t. Next, we choose a goal, here are a few common ones. In our case, we simply want to know if there are trees or not for every pixel, and so this would be a binary semantic segmentation problem.

And based on this goal, we expect the outputs to be the percentage of trees for every pixel; as a number between 0 and 1. Zero represents no trees, and one represents a high confidence there are trees.

But how do we go from input images into probabilities of trees? Well think about it this way…there are many ways to approach this problem, here are 3 common ways of doing so. My peers and I prefer using Fully convolutional networks when building a map with ML predictions

And since a model is a collection of interconnected layers, we must come up with an arrangement of layers that transforms our data inputs based on our desired outputs. Each layer by the way has something called an activation function, which performs the transformations of each layer before it passes them to the next layer.

FYI Below is a handy dandy table, with our recommended activation and loss functions to choose from based on your goal. We hope this saves you time.

We then reach the fourth and last layer. Depending on our goals at the beginning, we also choose an appropriate loss function that helps us score how well the model did during training.

After choosing layers and functions you will split your data into training and validation datasets. Just remember that all of this work is about experimenting repeatedly until you reach desired results. Our 8min episode gives this overview more in detail.

When to build a custom model outside of Earth Engine

So now that we covered what is deep learning, the next step is understanding which tools to use to build our deforestation model. For starters it’s important to call out that Google Earth Engine is a wonderful tool that helps organizations of all sizes find insights about changes on the planet, in order to make a climate positive impact. It has built-in machine learning algorithms (classifiers) that let users quickly spin them up, with just a basic machine learning background. This is fantastic place to start when using ML on geospatial data, however there are multiple situations where you will want to opt to build a custom model such as:

• You want to use a popular ML library such as TensorFlow Keras.
• You wish to build a state of the art model to build a global and accurate land cover map product such as Google’s Dynamic World.
• Or because you generally have too much data to process that you can’t execute it in just one task in Earth Engine (and are trying to figure out hacky ways to export your data).

Whenever you identify with any of these options, you will want to roll up your sleeves and dive into building a custom model, which does require expertise and of course working with multiple products. But I have good news, using deep learning is a great go-to algorithm.

How to build a model with Google Cloud & Earth Engine?

To get started, you will need an account with Google Earth Engine which is free for non-commercial entities and Google Cloud account which has a free tier if you are just getting started for all users. I have broken up the products you would use by function.

If you are interested in looking deeper into this overview, visit our slides here starting from slide 53 and read the speaker notes. Our code sample also walks through how to integrate with all of these projects end to end (just scroll down and click “open in colab”). But here is a quick visual summary. The main place to start is to identify which are the inputs and which are the outputs.

In our latest episodes for our People and Planet AI YouTube series, we walk through how to train a model and then host it in a relatively inexpensive web hosting platform called Cloud Run in episodes of less than 10mins.

There are a few options presented in the slides, however the current best practice is to train a model using Vertex AI. Do note though that Google Earth Engine is currently not integrated with Vertex AI (we are working on this), but it is with the older (ML predecessor) called Cloud AI Platform (which is the recommended ML platform to use moving forward). As such, if you would like to import your model for detecting deforestation back into Earth Engine after training it in Vertex AI for example, you can host the model in Cloud AI Platform and get predictions. Just note that it’s a 24 hour paid service and so it can cost upwards of $100 or more a month to host your model to stream predictions. It also currently supports the following model building platforms if you don’t wish to use TensorFlow.

A cheaper alternative but without the convenience AI Platform offers is to manually translate the model’s output, which is NumPy Arrays into Cloud Optimized GeoTIFFs in order to load it back into Earth Engine using Cloud Run. Within this web service you would store the NumPy arrays into a Cloud Storage bucket, then spin up a container image with GDAL, an open source geospatial library in order to convert them into Cloud Optimized GeoTIFF files into Cloud Storage. This way you can view predictions from your browser or Earth Engine.

Try it out

This was a quick overview of deep learning and what Cloud products you can use to solve meaningful environmental challenges like detecting deforestation in extractive supply chains. If you would like to try it out, check out our code sample here (click “open in colab” at the bottom of the screen to view the tutorial in our notebook format or click this shortcut here).

We’re excited to announce that Google Cloud Next returns on October 11–13, 2022. 

Join us for keynotes from industry luminaries and engage live with Google developers. Explore dynamic content across various learning levels, and dive deep into technologies and solutions spanning the Google Cloud and Google Workspace portfolios. Participate in breakout sessions, demos, and hands-on training. Hear from the world’s leading companies about their digital transformation journeys. You’ll have opportunities to connect with experts, get inspired, and boost your skills. We can’t wait to see you at Next ’22!

It’s too early to determine how the event experience will span the digital and physical worlds, so please stay tuned for updates as we plan with the health and safety of the attendees in mind. In the meantime, mark October 11–13 in your calendar, and visit our event site for updates. For more inspiration, rediscover Next ’21, now available on demand.