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Use No-Cost Solutions To Bring ML Into Your Medical Research

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Wonders happen when you merge science and technology! Read this informative piece to know how medical researchers can use AI systems to predict protein shapes and help accelerate research in every field of biology.

There’s a lot we can learn from combining technology with science to help support the development of amazing discoveries. By using an AI system to predict protein shapes, we have the potential to accelerate research in every field of biology. Inside every cell in your body, billions of tiny molecular machines are hard at work. They are what allow your eyes to detect light, your neurons to fire, and the ‘instructions’ in your DNA to be read.

These intricate machines are known as proteins.

The protein folding puzzle

Protein folding is something that occurs naturally so that proteins become biologically functional, but it’s a complex process that sometimes fails. For decades, scientists have been trying to find a method to reliably predict a protein’s structure from its sequence of amino acids so we can better understand how proteins work.

The challenge? There are over 200 million known distinct proteins. Each one has a unique 3D shape that determines how it works and what it does. Because there are so many sequences and determining their 3-D structure experimentally is so time-consuming and expensive, scientists only know the exact structure of a tiny fraction of the proteins. And these experimental methods still fall far short of reliable statistical accuracy.

Deepmind’s gigantic leap

In 2020, Alphabet’s artificial intelligence research arm, DeepMind, made a massive breakthrough in predicting protein structures using a deep learning model called AlphaFold.

AlphaFold is trained on publicly available data consisting of about 170,000 protein structures, and is the first computational method that can regularly predict the 3D shape of a protein, at scale with a high degree of accuracy.

AlphaFold has already sent waves throughout the scientific community and has demonstrated the potential for AI to aid fundamental scientific discovery. Recently, Deepmind has made AlphaFold predictions available and open source to anyone. To date, more than 500,000 researchers from 190 countries have accessed the AlphaFold protein structure database to get closer to finding life-saving cures for diseases like Leishmaniasis and Chagas.

And now Deepmind has expanded the set of available predictions by more than 200 times (from nearly 1 million to nearly 214 million) to cover almost all cataloged proteins found in nature.

Open source predictions available on Google Cloud

Together, Google Cloud and Deepmind have released this dataset of predicted protein structures for plants, bacteria, animals, and other organisms as part of the Google Cloud Public Dataset program to enable bulk downloads at no cost. That means you can also create custom queries of the dataset using BigQuery!

Running AlphaFold on Google Cloud Vertex AI

Let’s say you want to run AlphaFold on your own in order to get protein structure predictions against your own set of data. There are a few challenges to keep in mind:

  • You need to set up feature engineering against genetic sequence databases
  • Preprocess data
  • And run those inputs against pre-trained models

All of this requires allocating CPUs or GPUs, hosting a notebook environment, and scaling up for larger experiments. It’s hard to build and configure an on-premise system or cloud server to use AlphaFold whether you just want to try it out or run it at scale as a large organization.

That’s why we’re excited to share a deep integration between Google Cloud and Deepmind. On top of the Public Datasets program we have created end-to-end code samples for AlphaFold on Vertex AI, a managed end-to-end ML platform, to help address these challenges and speed up deployment. With AlphaFold on Vertex AI, you can manage a data science or machine learning workflow in a single development environment. You get access to pre-configured compute, storage, and end-to-end production notebooks. We have removed the heavy lifting needed to set up new ML environments, automate orchestration, and manage large clusters.

The AlphaFold inference workflow can be simplified with Vertex AI: from data preparation to feature engineering and deployment. Unlike the manual set up, the orchestrator makes it possible to parallelize steps, get predictions faster, and with better tracking.

Try it out first using Vertex AI Workbench

For those of you who want to try out a simplified version of AlphaFold, we have a Colab notebook that uses no templates (homologous structures) and a selected portion of the BFD database. You can deploy right on Vertex AI Workbench, which lets you specify a custom container image that we’ve already created for you. You’ll be able to:

  1. Configure access to genetic databases
  2. Configure GPU acceleration
  3. Search against genetic databases
  4. Use the pre-processed results as inputs to the AlphaFold model locally



In a little over an hour you can harness the power of AlphaFold to generate 3-D protein structures from amino acid sequences.

Run hundreds of experiments reliably using Vertex AI Pipelines

For organizations that want to run a full blown version of AlphaFold for many protein folding experiments a week, you’ll want an ML pipeline orchestrator. The AlphaFold Batch Inference solution is a set of code samples that uses Vertex AI Pipelines to support hundreds of concurrent inference pipelines with higher throughput to help you run experiments at scale. The solution uses Vertex AI Pipelines as an orchestrator and runtime, Vertex ML Metadata for metadata and artifacts, and Cloud Filestore to manage databases.

Because it’s built on Vertex AI Pipelines, you can automate, monitor, and experiment with interdependent parts of an ML workflow. The minimized inference elapsed times mean what normally would take you days, can now take you hours.

The solution includes two example pipelines:

1. The universal pipeline solution mirrors the exact logic in DeepMind’s open source inference script but decoupled into discrete tasks so you can run the same experiments faster, more efficiently, and with better tracking.

2. The customized pipeline solution shows you how to further optimize the inference workflow by parallelizing feature engineering steps so you can plug in your own database sources.


You get example components, pipelines, and notebooks to start, analyze, and recompile pipelines on different GPUs.

The AlphaFold Vertex AI Workbench solution is great for experimental use, while the AlphaFold Batch Inference solution on Vertex AI Pipelines is great for doing protein folding at scale with a strong process for reproducibility and tracking.

Now go forth and save the world!

Okay maybe that’s a bit hyperbolic, but this is inspiring stuff! What started as a 50 year challenge, to the discovery of AlphaFold, to being able to run it on Google Cloud, researchers, developers, and science enthusiasts now have access to one of the most pivotal advancements in the medical world. Even a non-specialist can easily use a Vertex AI notebook to exercise a simplified version of AlphaFold. The next answers to the mysteries of life and discovery of disease treatments have never felt more attainable. With these no-cost solutions to run AlphaFold on Vertex AI and the Public Dataset, you can help propel us in this worldwide endeavor.

Learn more about healthcare and life sciences solutions on Google Cloud here.

If you have feedback or want to share your experience with me, reach out to me at @stephr_wong.

Whitepaper

Data Science Teams: A Practitioners Guide to MLOps

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Across industries, DevOps and DataOps have been widely adopted as methodologies to improve quality and reduce the time to market of software engineering and data engineering initiatives.

With the rapid growth in machine learning (ML) systems, similar approaches need to be developed in the context of ML engineering, which handle the unique complexities of the practical applications of ML. This is the domain of MLOps.

MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.

Inside, you will find an outline of an MLOps framework that defines core processes and technical capabilities. Organizations can use this framework to help establish mature MLOps practices for building and operationalizing ML systems.

Adopting the framework can help organizations improve collaboration between teams, improve the reliability and scalability of ML systems, and shorten development cycle times. These benefits in turn drive innovation and help gain overall business value from investments in ML.

Trend Analysis

How Google Cloud Solutions Help Retail Firms to ABP(Always Be Pivoting)

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Data analytics and AI-based demand forecasting, product discovery that drive conversion and tools for modern store experiences--are the innovation trio to help retail firms to keep pivoting. Read Google Cloud's role in ushering them towards ABP.

For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.

And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.

Today’s retail landscape has surfaced both existing and new challenges for specialty and department store retailers. Approximately 88% of purchases previously occurred within a store environment. Now, it’s closer to 59%, with the remainder done online or through other omni methods. 

With such constant change and upheaval, it can feel like the mantra now is ABP: always be pivoting. 

The big question isn’t just how to maintain constant momentum and agility—it’s how to do it without sapping your workforce, your inventory, or your profits in the process. The pivot is now a given. What matters is how you do it.

Adapting requires a flexible base of technology that allows retailers to shift and scale seamlessly with the needs of the moment. 

They need to be able to leverage real-time insights and enhance customer experiences rapidly, online and in the real world (not to mention the growing hybridization that’s AR and VR). They need to modernize their stores to power engaging consumer and associate experiences. They need to enhance operations for rapid scaling between full operations and digital-only offerings.

To help retailers achieve these goals and more, Google Cloud is honing a trio of essential innovations: demand forecasting that harnesses the power of data analytics and artificial intelligence; enhanced product discovery to improve conversion across channels; and the tools to help create the modern store experience.

In other words, here’s some of the biggest ways we’re ready to help you pivot.

Pivot point 1: Harnessing data and AI for demand forecasting with Vertex AI

One of the greatest challenges for retailers when building organizational flexibility is managing inventory and the supply chain. 

We are in the midst of one of the worst global supply chain crises, stemming from soaring demand and logistics issues brought on by the pandemic. This crisis has only heightened the challenge retailers face when assessing demand and product availability. Even in normal times, mismanagement of inventory can add up to a trillion-dollar problem, according to IHL Group (costing $634 billion in lost sales worldwide each year, while overstocks result in $472 billion in lost revenues due to markdowns). 

On the flipside, optimizing your supply chain can lead to greater profits. For instance, McKinsey predicts that a 10% to 20% improvement in retail supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues.

Some of the challenges related to demand forecasting include:

  • Low accuracy leads to excess inventory, missed sales, and pressure on fragile supply chains.
  • Real drivers of product demand are not included, because large datasets are hard to model using traditional methods.
  • Poor accuracy for new product launches and products that have sparse or intermittent demand.
  • Complex models are hard to understand, leading to poor product allocation and low return on investment on promotions.
  • Different departments use different methods, leading to miscommunication and costly reconciliation errors.

AI-based demand forecasting techniques can help. Vertex AI Forecast supports retailers in maintaining greater inventory flexibility by infusing machine learning into their existing systems. Machine learning and AI-based forecasting models like Vertex AI are able to digest large sets of disparate data, drive analytics and automatically adjust when provided with new information. 

With these machine learning models, retailers can not only incorporate historical sales data, but also use close to real-time data such as marketing campaigns, web actions like a customer clicking the “add to cart” button on a website, local weather forecasts, and much more. 

Pivot point 2: Enhanced product discovery through AI-powered search and recommendations

If customers can’t easily find what they are looking for, whether online or at the store, they will turn to someone else. That’s a simple statement, but one with profound impacts.

In research conducted by The Harris Poll and Google Cloud, we found that over a six month period, 95% of consumers received search results that were not relevant to what they were searching for on a retail website. And roughly 85% of consumers view a brand differently after an unsuccessful search, while 74% say they avoid websites where they’ve experienced search difficulties in the past.

Each year, retailers lose more than $300 billion dollars from search abandonment, or when a consumer searches for a product on a retailer’s website but does not find what they are looking for. Our product discovery solutions help you surface the right products, to the right customers, at the right time. These solutions include: 

  • Vision Product Search, which is like bringing the augmented reality of Google Lens to a retailer’s own branded mobile app experience. Both shoppers and retail store associates can search for products using an image they’ve photographed or found online and receive a ranked list of similar items.
  • Recommendations AI, which enables retailers to deliver highly personalized recommendations at scale across channels.
  • Retail Search, which provides Google-quality search results on a retailer’s own website and mobile applications.

All three are powered by Google Cloud, leveraging Google’s advanced understanding of user context and intent, utilizing technology to deliver a seamless experience to every shopper. With these combined capabilities, retailers are able to reduce search abandonment and improve conversions across their digital properties. 

Pivot point 3: Building the modern store

Stores are no longer places for just browsing and buying. They must be flexible operation centers, ready to pivot to address changing circumstances. The modern store must be multiple things at once: a mini-fulfillment and return center, a recommendation engine, a shopping destination, a fun place to work, and more. 

Just as retail companies had to embrace omnichannel, stores are now becoming omnichannel centers on their own, mixing the digital and physical into a single location. Retailers can use physical stores as a vehicle to deliver superior customer experiences. This will demand heightened levels of collaboration and cooperation between stores, digital, and tech infrastructure teams, building on the agile ways they have worked together.

In many ways, it’s about allowing our physical spaces to function more like digital ones. Google Cloud can help by bringing the scalability, security, and reliability of the cloud to the store, allowing physical locations to upgrade infrastructure and modernize their internal and customer-facing applications. 

Think of it as when a new OS gets released for your phone. It’s the same small, hard box, but the experience can feel radically different. Now, extend that same idea to a digitally enabled store. With the right displays, interfaces, and tools at a given retail location, the team only needs to send an over-the-air update to create radically fresh experiences, ranging from sales displays to fulfillment or employee engagement.

Such an approach can enable streamlined experiences for both customers and store associates. For instance, when it comes to the modern store’s evolving role as a fulfillment or return center, cloud solutions can help drive efficiency in stores through automation of ordering, replenishment, and fulfillment of omnichannel order selection.  

Similar tools for personalized product discovery online can be applied to customers in the store, helping them to browse and explore, or even create a tailored shopping experience. 

The impact of store associates can be maximized by equipping them with technology to provide expertise that drives value-added customer service, as well as increasing productivity in stores by streamlining operations, thus lowering overhead cost. At the register, customers should be able to enjoy frictionless checkout while ensuring reliable, accurate, secure transactions.

Google Cloud can help retailers transform

The ability to adapt and pivot to meet today’s changing consumer needs requires that retailers rely on modern tools to obtain operational flexibility. We believe that every company can be a tech company. That every decision is data driven. That every store is physical and digital all at once. That every worker is a tech worker. 

Google Cloud works with retailers to help them solve their most challenging problems. We have the unique ability to handle massive amounts of unstructured data, in addition to advanced capabilities in AI and ML. Our products and solutions help retailers focus on what’s most important—from improving operations to capturing digital and omnichannel revenue.

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Satellites Can Help Map Carbon Emissions By Looking at Images of Power Plants!

Did you know that Satellites can now help track power plants and determine if they are on or off? And did also know that compute processing that classifies over 59 trillion bytes of data from over 11,000 sensors from 300 satellites is done on on Google Cloud?

Google Cloud’s sustainability initiatives along with its cutting-edge, powerful technology and products are leveraged worldwide by organizations, non-profits and governments to keep a check on greenhouse gas emissions and build self-reporting, power monitoring platforms. Climate TRACE, a collaborative data sharing project with 50+ organizations is dedicated towards this cause by building a visual and meaningful way on a web app. In this video you can learn how generate geo-spatial model that can view the images of power plants gathered from satellite called Sentinel-2 to assess emissions from power plants. Watch the video to learn Google Cloud’s vehement role in funding and staffing special initiatives with Googlers who are experts in AI/ML, UX, data analytics and more!

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Pytorch at Scale on Cloud TPUs

Cloud TPU Pods are machine learning supercomputers that enable enterprises to train large machine learning models at a scale otherwise difficult or sometimes impossible.

They deliver business value through solving many machine learning problems including image search, neural architecture search and large-scale language models both for Google and Google Cloud customers.

However, until a few months ago TPU devices and pods were limited to Tensorflow models. Much awaited support for Pytorch was announced in Pytorch conference 2019.

In this session, Vaibhav Singh, ML Specialist, Cloud Customer Engineer, Google Cloud and Taylan Bilal, Software Engineer, Google Cloud demystify the art and science of training your Pytorch models on TPUs. They take you through a step-by-step walkthrough using an example, then explain how it works, and finally how to scale up Pytorch training with Cloud TPU Pods.

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Case Study

Google Cloud’s ML-based Image Classification App: A Key to Global Wildlife Conservation

Wildlife provides critical benefits to support nature and people. Unfortunately, wildlife is slowly but surely disappearing from our planet and we lack reliable and up-to-date information to understand and prevent this loss. By harnessing the power of technology and science, we can unite millions of photos from [motion sensored cameras] around the world and reveal how wildlife is faring, in near real-time…and make better decisions

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New Technology: The Projected Total Economic Impact™ Of Google Cloud Contact Center AI

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