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).

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation and efficient operational growth.

Whether they’re trying to extract insights from data, create faster and more efficient workflows via intelligent automation, or build innovative customer experiences, leaders at today’s tech companies and startups know that proficiency in AI and ML is more important than ever.

AI and ML technologies are often expensive and time-consuming to develop, and the demand for AI and ML experts still largely outpaces the existing talent pool. These factors put pressure on tech companies and startups to allocate resources carefully when considering bringing AI/ML into their business strategy. In this article, we’ll explore four tips to help tech companies and startups accelerate innovation and reduce costs with AI and ML.

4 tips to accelerate innovation and reduces costs with AI and ML

Many of today’s most innovative companies are creating services or products that couldn’t exist without AI—but that doesn’t mean they’re building their AI and ML infrastructure and pipelines from scratch. Even for startups whose businesses don’t directly revolve around AI, injecting AI into operational processes can help manage costs as the company grows. By relying on a cloud provider for AI services, organizations can unlock opportunities to energize development, automate processes, and reduce costs.

1. Leverage pre-trained ML APIs to jumpstart product development

Tech companies and startups want their technical talent focused on proprietary projects that will make a difference to the business. This often involves the development of new applications for an AI technology, but not necessarily the development of the AI technology itself. In such scenarios, pre-trained APIs help organizations quickly and cost-effectively establish a foundation on which higher-value, more differentiated work can be layered.

For example, many companies building conversational AI into their products and services leverage Google Cloud APIs such as Speech-to-Text and Natural Language. With these APIs, developers can easily integrate capabilities like transcription, sentiment analysis, content classification, profanity filtering, speaker diarization, and more. These powerful technologies help organizations focus on creating products rather than having to build the base technologies.

See this article for examples of why tech companies and startups have chosen Google Cloud’s Speech APIs for use cases that range from deriving customer insights to giving robots empathetic personalities. For an even deeper dive, see

• our AI product page to explore other APIs, including Translation, Vision, and more;
• and the Google Cloud Skills Boost for ML APIs.

2. Use managed services to scale ML development and accelerate deployment of models to production

Pre-trained models are extremely useful, but in many cases, tech companies and startups need to create custom models to either derive insights from their own data or to apply new use cases to public data. Regardless of whether they’re building data-driven products or generating forecasting models from customer data, companies need ways to accelerate the building and deployment of models into their production environments.

A data scientist typically starts a new ML project in a notebook, experimenting with data stored on the local machine. Moving these efforts into a production environment requires additional tooling and resources, including more complicated infrastructure management. This is one reason many organizations struggle to bring models into production and burn through time and resources without moving the revenue needle.

Managed cloud platforms can help organizations transition from projects to automated experimentation at scale or the routine deployment and retraining of production models. Strong platforms offer flexible frameworks, fewer lines of code required for model training, unified environments across tools and datasets, and user-friendly infrastructure management and deployment pipelines.

At Google Cloud, we’ve seen customers with these needs embrace Vertex AI, our platform for accelerating ML development, in increasing numbers since it launched last year. Accelerating time to production by up to 80% compared to competing approaches, Vertex AI provides advanced end-to-end ML Ops capabilities so that data scientists, ML engineers, and developers can contribute to ML acceleration. It includes low-code features, like AutoML, that make it possible to train high performing models without ML expertise.

Over the first half of 2022, our performance tests found that the number of customers utilizing AI Workbench increased by 25x. It’s exciting to see the impact and value customers are gaining with Vertex AI Workbench, including seeing it help companies speed up large model training jobs by 10x and helping data science teams improve modeling precision from the 70-80% range to 98%.

If you are new to Vertex AI, check out this video series to learn how to take models from prototype to production. For deeper dives, see

• this article about Vertex AI’s role in an ambitious project to measure climate change with AI;

• BigQuery has built-in Machine Learning (ML) and Analytics that you can use to create no-code predictions using just SQL queries.
• this blog about how Vertex AI and BigQuery work together to make data analysis easier and more powerful;
• and this blog about Example-based explanations, one of our most recent updates to make model iteration more intuitive and efficient.

3. Harness the cloud to match hardware to use cases while minimizing costs and management overhead

ML infrastructure is generally expensive to build, and depending on the use case, specific hardware requirements and software integrations can make projects costly and complicated at scale. To solve for this, many tech companies and startups look to cloud services for compute and storage needs, attracted by the ability to pay only for resources they use while scaling up and down according to changing business needs.

At Google Cloud, customers share that they need the ability to optimize around a variety of infrastructure approaches for diverse ML workloads. Some use Central Processing Units (CPUs) for flexible prototyping. Others leverage our support for NVIDIA Graphics Processing Units (GPUs) for image-oriented projects and larger models, especially those with custom TensorFlow operations that must run partially on CPUs. Some choose to run on the same custom ML processors that power Google applications—Tensor Processing Units (TPUs). And many use different combinations of all of the preceding.

Beyond matching use cases to the right hardware and benefiting from the scale and operational simplicity of a managed service, tech companies and startups should explore configuration features that help further control costs. For example, Google Cloud features like time-sharing and multi-instance capabilities for GPUs — as well as features like Vertex AI Training Reduction Server — are built to optimize GPU costs and usage.

Vertex AI Workbench also integrates with the NVIDIA NGC catalog for deploying frameworks, software development kits and Jupyter Notebooks with a single click—another feature that, like Reduction Server, speaks to the ways organizations can make AI more efficient and less costly via managed services.

4. Implement AI for operations

Besides using pre-trained APIs and ML model development to develop and deliver products, startup and tech companies can improve operational efficiency, especially as they scale, by leveraging AI solutions built for specific business and operational needs, like contract processing or customer service.

Google Cloud’s DocumentAI products, for instance, apply ML to text for use cases ranging from contract lifecycle management to mortgage processing. For businesses whose customer support needs are growing, there’s Contact Center AI, which helps organizations build intelligent virtual agents, facilitate handoffs as appropriate between virtual agents and human agents, and generate insights from call center interactions. By leveraging AI to help manage operational processes, startups and tech companies can allocate more resources to innovation and growth.

Next steps toward an intelligent future

The tips in this article can help any tech company or startup find ways to save money and boost efficiency with AI and ML. You can learn more about these topics by registering for Google Cloud Next, kicking off October 11, where you’ll hear Google Cloud’s latest AI news, discussions, and perspectives—in the meantime, you can also dive into our Vertex AI quickstarts and BigQuery ML tutorials. And for the latest on our work with tech companies and startups, be sure to visit our Startups page.

Healthcare breakthroughs change the world and bring hope to humanity through scientific rigor, human insight, and compassion. We believe AI can contribute to this, with thoughtful collaboration between researchers, healthcare organizations and the broader ecosystem. 

Today, we’re sharing exciting progress on these initiatives, with the announcement of limited access to Google’s medical large language model, or LLM, called Med-PaLM 2. It will be available in coming weeks to a select group of Google Cloud customers for limited testing, to explore use cases and share feedback as we investigate safe, responsible, and meaningful ways to use this technology. 

Med-PaLM 2 harnesses the power of Google’s LLMs, aligned to the medical domain to more accurately and safely answer medical questions. As a result, Med-PaLM 2 was the first LLM to perform at an “expert” test-taker level performance on the MedQA dataset of US Medical Licensing Examination (USMLE)-style questions, reaching 85%+ accuracy, and it was the first AI system to reach a passing score on the MedMCQA dataset comprising Indian AIIMS and NEET medical examination questions, scoring 72.3%.

Industry-tailored LLMs like Med-PaLM 2 are part of a burgeoning family of generative AI technologies that have the potential to significantly enhance healthcare experiences. We’re looking forward to working with our customers to understand how Med-PaLM 2 might be used to facilitate rich, informative discussions, answer complex medical questions, and find insights in complicated and unstructured medical texts. They might also explore its utility to help draft short- and long-form responses and summarize documentation and insights from internal data sets and bodies of scientific knowledge.

Innovating responsibly with AI

Since last year, we’ve been researching and evaluating Med-PaLM and Med-PaLM 2, assessing it against multiple criteria — including scientific consensus, medical reasoning, knowledge recall, bias, and likelihood of possible harm — which were evaluated by clinicians and non-clinicians from a range of backgrounds and countries. 

Med-PaLM 2’s impressive performance on medical exam-style questions is a promising development, but we need to learn how this can be harnessed to benefit healthcare workers, researchers, administrators, and patients. In building Med-PaLM 2, we’ve been focused on safety, equity, and evaluations of unfair bias. Our limited access for select Google Cloud customers will be an important step in furthering these efforts, bringing in additional expertise across the healthcare and life sciences ecosystem. 

What’s more, when Google Cloud brings new AI advances to our products, our commitment is two-fold: to not only deliver transformative capabilities, but also ensure our technologies include proper protections for our organizations, their users, and society. To this end, our AI Principles, established in 2017, form a living constitution that guides our approach to building advanced technologies, conducting research, and drafting our product development policies. 

From AI to generative AI 

Google’s deep history in AI informs our work in generative AI technologies, which can find complex relationships in large sets of training data, then generalize from what they learn to create new data. Breakthroughs such as the Transformer have enabled LLMs and other large models to scale to billions of parameters, letting generative AI move beyond the limited pattern-spotting of earlier AIs and into the creation of novel expressions of content, from speech to scientific modeling. 

Google Cloud is committed to bringing to market products that are informed by our research efforts across Alphabet. In 2022, we introduced a deep integration between Google Cloud and Alphabet’s AI research organizations, which allows Vertex AI to run DeepMind’s groundbreaking protein structure prediction system, AlphaFold.

Much more is on the way. In one sense, generative AI is revolutionary. In another, it’s the familiar technology story of more and better computing creating new industries, from desktop publishing to the internet, social networks, mobile apps, and now, generative AI.

Building on AI leadership

Additionally, today we’re announcing a new AI-enabled Claims Acceleration Suite, designed to streamline processes for health insurance prior authorization and claims processing. The Claims Acceleration Suite helps both providers of insurance plans and healthcare to create operational efficiencies and reduce administrative burdens and costs by converting unstructured data into structured data that help experts make faster decisions and improve access to timely patient care. 

On the clinical side, last year we announced Medical Imaging Suite, an AI-assisted diagnosis technology being used by Hologic to improve cervical cancer diagnoses and Hackensack Meridian Health to predict metastasis in patients with prostate cancer. Elsewhere, Mayo Clinic and Google have collaborated on an AI algorithm to improve the care of head and neck cancers, and Google Health recently partnered with iCAD to improve breast cancer screening with AI.

From these examples and more, it’s clear that the healthcare industry has moved from testing AI to deploying it to improve workflows, solve business problems, and speed healing. With this in mind, we expect rapid interest in and uptake of generative AI technologies. Healthcare organizations are eager to learn about generative AI and how they can use it to make a real difference.

Looking ahead

The power of AI has reinforced Google Cloud’s commitment to privacy, security, and transparency. Our platforms are designed to be flexible, including data and model lineage capabilities, integrated security and identity management services, support for third-party models, choice and transparency on models and costs, integrated billing and entitlement support, and support across many languages. 

While we’ll have some innovations like Med-PaLM 2 that are tuned for healthcare, we also have products that are relevant across industries. Last month, we announced several generative AI capabilities coming to Google Cloud, including Generative AI support in Vertex AI and Generative AI App Builder, which are already being tested by a number of customers. Developers and businesses already use Vertex AI to build and deploy machine learning models and AI applications at scale, and we recently added Generative AI support in Vertex AI. This gives customers foundation models they can fine-tune with their own data, and the ability to deploy applications with this powerful new technology. We also launched Generative AI App Builder to help organizations build their own AI-powered chat interfaces and digital assistants in minutes or hours by connecting conversational AI flows with out-of-the-box search experiences and foundation models.

As AI proves its value, it’s likely there will be increased focus on high-quality data collection and curation in healthcare and life sciences. Improving the flow and unification of data across health care systems, referred to as data interoperability, is one of the most important building blocks to leveraging AI, and it helps organizations run more effectively, improve patient care, and helps people live healthier lives. We expect to continue our investments in technology, infrastructure, and data governance.

We’re committed to realizing the potential of this technology in healthcare. By working with a handful of trusted healthcare organizations early on, we’ll learn more about what can be achieved, and how this technology can safely advance. For all of us, the prospects are inspiring, humbling, and exciting. 

If you’re interested in exploring generative AI on Cloud, you can sign-up for our Trusted Tester program or reach out to your Google Cloud sales representative.