You are what you eat. We’ve all been told this, but the truth is what we eat is often more complex than we are – genetically at least. Take a grain of rice. The plant that produces rice has 40,000 to 50,000 genes, double that of humans, yet we know far more about the composition of the human genome than of plant life. We need to close this knowledge gap quickly if we are to answer the urgent challenge of feeding 8 billion people, especially as food security around the globe is likely to worsen with climate change.

For this reason, AI company InstaDeep has teamed up with Google Cloud to train a large AI model with more than 20 billion parameters on a dataset of reference genomes for cereal crops and edible vegetables, using the latest generation of Google’s Tensor Processing Units (Cloud TPU v4), which is particularly suited for training efficiency at scale. Our aim is to improve food security and sustainable agriculture by creating a tool that can analyze and predict plants’ agronomic traits from genomic sequences. This will help identify which genes make some crops more nutritious, more efficient to grow, and more resilient and resistant to pests, disease and drought.

Genomic language models for sustainable agriculture

Ever since farming began, we have been, directly or indirectly, trying to breed better crops with higher yields, better resilience and, if we’re lucky, better taste too. For thousands of years, this was done by trial and error, growing crops year-on-year while trying to identify and retain only the most beneficial traits as they naturally arise from evolutionary mutations. Now that we have access to the genomic sequences of plants, we hope to directly identify beneficial genes and predict the effect of novel mutations.

However, the complexity of plant genomes often makes it difficult to identify which variants are beneficial. Revolutionary advances in machine learning (ML) can help to understand the link between DNA sequences and molecular phenotypes. This means we now have precise and cost-effective prediction methods to help us close the gap between genetic information and observable traits. These predictions can help identify functional variants and accelerate our understanding of which genes link to which traits – so we can make better crop selections.

Moreover, thanks to the vast library of available crop genetic sequences, training large models on hundreds of plant genomes means we can transfer the knowledge from thoroughly-studied species to those that are less understood but important for food production – especially in developing countries. And by doing this digitally, AI can quickly map and annotate the genomes of both common and rare crop variants.

One of the major limitations of traditional ML methods for plant genomics has been they mostly rely on supervised learning techniques. They need labeled data. Such data is scarce and expensive to collect, severely limiting these methods. Recent advances in natural language processing (NLP), such as Transformer architectures and BERT-style training (Bidirectional Encoder Representations from Transformers), allow scientists to train massive language models on raw text data to learn meaningful representations. This unsupervised learning technique changes the game. Once learned, the representations can be leveraged to solve complex regression or classification tasks – even when there is a lack of labeled data.

InstaDeep partners with Google Cloud to train the new generation of AI models for genomics on TPUs

Researchers have demonstrated that large language models can be especially effective in proteomics. To understand how this works, imagine reading amino acids as words and proteins as sentences. The treasure trove of raw genomics data – in sequence form – inspired InstaDeep and Google Cloud to apply similar technologies on nucleotides, this time reading them as words and chunks of genomes as sentences.

Moreover, the representations that the system learned improved in line with the size of the models and datasets, NLP research studies showed. This finding led InstaDeep researchers to train a set of increasingly larger language models on genomics datasets ranging from 1 billion to 20 billion parameters.

• Models of 1 billion and 5 billion parameters were trained on a dataset comprising the reference genomes for several edible plants, including fruit, cereal and vegetables for a total of 75 billion nucleotides.
• The training dataset must increase in the same proportion as the model capacity, recent work has shown. Thus, we created a larger dataset gathering all reference genomes available on the National Center for Biotechnology Information (NCBI) database including human, animal, non-edible plant and bacteria genomes. This dataset, which we used to train a 20-billion-parameter Transformer model, comprised 700 billion tokens, exceeding the size of most datasets typically used for NLP applications, such as the Common Crawl or Wikipedia dataset.
• Both teams announced that the 1 billion-parameter model will be shared with the scientific community to further accelerate plant genomics research.

The compact and meaningful representations of nucleotide sequences learned by these models can be used to tackle molecular phenotype prediction problems. To showcase their ability, we trained a model to predict the gene function and gene ontology (i.e. a gene’s attribute) for different edible plant species.

Early results have demonstrated that this model can predict these characteristics with high accuracy – encouraging us to look deeper at what these models can tell us. Based on these results, we decided to annotate the genomes of three plant species with considerable importance for many developing countries: cassava, sweet potato, and yam. We are working on making these annotations freely available to the scientific community and hope that these will be used to further guide and accelerate new genomic research.

Overcoming scaling challenges with massive models and datasets with Cloud TPUs

The compute requirement for training our 20 billion-parameter model with billions of tokens is massive. While modern accelerators offer impressive peak performance per chip, to utilize this performance often requires tightly coupled hardware and software optimizations. Moreover, maintaining this efficiency when scaling to hundreds of chips presents additional system design challenges. The Cloud TPU’s tightly-coupled hardware and software stack is especially well suited to such challenges. The Cloud TPU software stack is based on the XLA Compiler which offers out-of-the-box optimizations (such as compute and communication overlap) and an easy programming model for expressing parallelism.

We successfully trained our large models for genomics by leveraging Google Tensor Processing Units (TPUv4). Our code is implemented with the JAX framework. JAX provides a functional programming-based approach to express computations as functions that can be easily parallelized using JAX APIs powered by XLA. This helped us to scale from a single host (four chips) configuration to a multi-host configuration without having to tackle any of the system design challenges. The TPU’s cost-effective inter- and intra-communication capabilities led to an almost linear scaling between the number of chips and training time. This allowed us to train the models quickly and efficiently on a grid of 1024 TPUv4 cores (512 chips).

Conclusion

Ultimately, our hope is that the functional characterization of genomic variants predicted by deep learning models will be critical to the next era in agriculture, which will largely depend on genome editing and analysis. We envisage that novel approaches, such as in-silico mutagenesis – the assessment of all possible changes in a genomic region by a computer model – will be invaluable in prioritizing mutations that improve plant fitness and guiding crop improvements. Attempting similar work in wet-lab experiments would be difficult to scale and nearly impossible in nature. By making our current and future annotations available to the research community, we also hope to help democratize breeding technologies so that they can benefit all of global agriculture.

Further Reading

To learn more about the unique features of Cloud TPU v4 hardware and software stack we encourage readers to explore Cloud TPU v4 announcement. To learn more about scaling characteristics, please see this benchmark and finally we recommend reading PJIT Introduction to get started with JAX and SPMD parallelism on Cloud TPU.

This research was made possible thanks to the support of Google’s TPU Research Cloud (TRC) Program which enabled us to use the Cloud TPUv4 chips that were critical to this work.

Over one third of organizations believe that data analytics and machine learning have the most potential to significantly alter the way they run business over the next 3 to 5 years. However, only 26% of organizations are data driven. One of the biggest reasons for this gap is that a major portion of the data generated today is unstructured, which includes images, documents, and videos. It is estimated to cover roughly up to 80% of all data, which has so far remained untapped by organizations.

One of the goals of Google’s data cloud is to help customers realize value from data of all types and formats. Earlier this year, we announced BigLake, which unifies data lakes and warehouses under a single management framework, enabling you to analyze, search, secure, govern and share unstructured data using BigQuery.

At Next ‘22, we announced the preview of object tables, a new table type in BigQuery that provides a structured record interface for unstructured data stored in Google Cloud Storage. This enables you to directly run analytics and machine learning on images, audio, documents and other file types using existing frameworks like SQL and remote functions natively in BigQuery itself. Object tables also extend our best practices of securing, sharing and governing structured data to unstructured, without needing to learn or deploy new tools.

Directly process unstructured data using BigQuery ML

Object tables contain metadata such as URI (Uniform Resource Identifier), content type, and size that can be queried just like other BigQuery tables. You can then derive inferences using machine learning models on unstructured data with BigQuery ML. As part of preview, you can import open source TensorFlow Hub image models, or your own custom models to annotate the images. Very soon, we plan to enable this for audio, video, text and many other formats, and pre-trained models to enable out-of-the box analysis. Check out this video to learn more and watch a demo.

Create an object table

CREATE EXTERNAL TABLE my_dataset.object_table
WITH CONNECTION us.my_connection
OPTIONS(uris=["gs://mybucket/images/*.jpg"],
object_metadata="SIMPLE", metadata_cache_mode="AUTOMATIC");
​
# Generate inferences with BQML
SELECT * FROM ML.PREDICT(
MODEL my_dataset.vision_model,
(SELECT ML.DECODE_IMAGE(data) AS img FROM my_dataset.object_table)
);

By analyzing unstructured data natively in BigQuery, businesses can

• Eliminate manual effort as pre-processing steps such as tuning image sizes to model requirements are automated
• Leverage the simple and familiar SQL interface to quickly gain insights
• Save costs by utilizing existing BigQuery slots without needing to provision new forms of compute

Adswerve is a leading Google Marketing, Analytics and Cloud partner on a mission to humanize data. Twiddy & Co. is Adswerve’s client – a vacation rental company in North Carolina. By combining structured and unstructured data, Twiddy and Adswerve used BigQuery ML to analyze images of rental listings and predict the click-through rate, enabling data-driven photo editorial decisions.

“Twiddy now has the capability to use advanced image analysis to stay competitive in an ever changing landscape of vacation rental providers – and can do this using their in-house SQL skills.” said Pat Grady, Technology Evangelist, Adswerve

Process unstructured data using remote functions

Customers today use remote functions (UDFs) to process structured data for languages and libraries that are not supported in BigQuery. We are extending this capability to process unstructured data using object tables.

Object tables provide signed URLs to allow remote UDFs running on Cloud Functions or Cloud Run to process the object table content. This is particularly useful for running Google’s pre-trained AI models, including Vision AI, Speech-to-Text, Document AI, open source libraries such as Apache Tika, or deploying your own custom models where performance SLAs are important.

Here’s an example of an object table being created over PDF files that are parsed using an open source library running as a remote UDF.

SELECT uri, extract_title(samples.parse_tika(signed_url)) AS title<br>FROM EXTERNAL_OBJECT_TRANSFORM(TABLE pdf_files_object_table,<br>["SIGNED_URL"]);

Extending more BigQuery capabilities to unstructured data

Business intelligence – The results of analyzing unstructured data either directly in BigQuery ML or via UDFs can be combined with your structured data to build unified reports using Looker Studio (at no charge), Looker or any of your preferred BI solutions. This allows you to gain more comprehensive business insights. For example, online retailers can analyze product return rates by correlating them with the images of defective products. Similarly, digital advertisers can correlate ad performance with various attributes of ad creatives to make more informed decisions.

BigQuery search index – Customers are increasingly using the search functionality of BigQuery to power search use cases. These capabilities now extend to unstructured data analytics as well. Whether you use BigQueryML to produce inference on images or use remote UDFs with Doc AI to produce document extraction, the results can now be search indexed and used to support search access patterns.

Here’s an example of search index on data that is parsed from PDF files:

CREATE SEARCH INDEX my_index ON pdf_text_extract(ALL COLUMNS);
​
SELECT * FROM pdf_text_extract WHERE SEARCH(pdf_text, "Google");

Security and governance – We are extending BigQuery’s row-level security capabilities to help you secure objects in Google Cloud Storage. By securing specific rows in an object table, you can restrict the ability of end users to retrieve the signed URLs of corresponding URIs present in the table. This is a shared responsibility security model, for which administrators need to ensure that end users don’t have direct access to Google Cloud Storage, and use signed URLs from object tables as the only access mechanism.

Here’s an example of a policy for PII images that are secured to be first processed through a blur pipeline:

CREATE ROW ACCESS POLICY pii_data ON object_table_images
GRANT TO ("group:admin@example.com")
FILTER USING (ARRAY_LENGTH(metadata)=1 AND
metadata[OFFSET(0)].name="face_detected")

Soon, Dataplex will support object tables, allowing you to automatically create object tables in BigQuery and manage and govern unstructured data at scale.

Data sharing – You can now use Analytics Hub to share unstructured data with partners, customers and suppliers while not compromising on security and governance. Subscribers can consume the rows of object tables that are shared with them, and use signed URLs for unstructured data objects.

Getting Started

Submit this form to try these new capabilities that unlock the power of your unstructured data in BigQuery. Watch this demo to learn more about these new capabilities.

Special thanks to engineering leaders Amir Hormati, Justin Levandoski and Yuri Volobuev for contributing to this post.

For more than 10 years, NMK Textile Mills has manufactured bed linens for major retailers in the United States and Canada. As e-commerce exploded, the company’s co-founder saw an opportunity to grow the business. So, in addition to manufacturing bed linens wholesale for retail customers, NMK Textile Mills reworked its complete supply chain to manufacture products for California Design Den, which became an e-commerce retailer selling its own fashion-forward products directly to consumers online.

With California Design Den’s push into e-commerce, it became apparent the SMB company (with about 250 global employees) faced the same tough supply chain questions as large retail customers, including maintaining enough inventory to meet customer demand in a timely, efficient way.

California Design Den depended upon a myriad of systems to track its complex forecasting and reordering processes. Team members typically planned inventory manually using desktop spreadsheet software, which could lead to excess inventory. Accurately forecasting demand and supply was essential to the company’s financial success—but it was also a challenge.

A couple years ago, California Design Den partnered with Pluto7, a technology solutions provider that offers a software as a service (SaaS) called Planning In A Box. Leveraging Google Cloud Platform machine learning and artificial intelligence, Planning In A Box intelligently helps predict demand and balances it with supply.

But that was just the beginning. “Along the way, we realized that to compete with larger retailers, make quicker decisions, and move faster, we needed to go further,” says Deepak Mehrotra, Co-founder and Chief Adventurer at California Design Den.

With guidance from Pluto7, California Design Den began migrating its database to Google Cloud Platform. Using Google BigQuery, Google Compute Engine, Google Cloud SQL, and Google Cloud Storage, and experimenting with Google Cloud Vision and Google Cloud AutoML, the company is reducing inventory carryovers by more than 50%, improving the accuracy of demand planning quarter over quarter, and gaining granular insights into how individual SKUs are performing.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels. With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

No need for army of data scientists

“Using Google Cloud Platform machine learning and AI was essential for California Design Den if it was to compete successfully with larger retailers,” Deepak says.

For example, tastes and fashions in bed linens can change quickly and consumer prices fluctuate. With more than 2,500 SKUs, it wasn’t possible for California Design Den’s team to continuously monitor product demand and experiment with competitive pricing in real time.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels,” says Deepak. “With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

By integrating all its data onto Google Cloud Platform, California Design Den’s team gains deeper insights into product sales over time, which in turn helps the company improve demand planning by better determining which styles to manufacture and sell in the future.

“When experienced employees leave, their knowledge leaves with them. By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Merging visuals with data

Before Google Cloud Platform, team members had to dig through spreadsheets and run scenarios to get a sense of how particular products had sold. The next step was to perform keyword searches across the company’s photo library in the cloud to find each product’s image. From there, a team member would insert the product images into a presentation, along with relevant data points, to provide a report for stakeholders on how particular styles performed.

Today, California Design Den, with the help of Pluto7, is integrating its entire product image library with its database on Google Cloud Platform. Experimenting with Google Cloud Vision and Google Cloud AutoML, California Design Den is moving towards a day when team members can run sales scenarios and get deep background data on individual product performance while viewing images of the relevant products.

Merging product visuals with data will help designers and team members better understand sales patterns over time and in context. In the past, making correlations between things like which sheet colors sold well in California, compared to how the same sheet colors performed on the East Coast, was something that California Design Den employees primarily did in their heads.

“When experienced employees leave, their knowledge goes with them,” says Manjunath Devadas, Founder and CEO at Pluto7. “By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Reimagining supply-demand balancing

Pluto7’s mission statement is to democratize supply demand balancing with machine learning and AI. California Design Den is a case in point, as the combination of Planning In A Box and Google Cloud Platform gives the company greater control over its destiny.

“Big retailers used to tell us what to manufacture and how much they would pay for it,” says Deepak. “That was our primary business, and if we didn’t accept the terms, a competitor would.” Today, in addition to continuing to make products for retailers, California Design Den can design, make, and sell a variety of designs for itself, including custom and limited-edition products, thanks to Google Cloud Platform and Pluto7 software offerings. The payoff is not only in having a more diversified business. California Design Den also receives more favorable profit margins by selling its own products.

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything,” Deepak says. “We can make smaller batches. We can connect directly with consumers. We can identify the missing pieces—what should we produce next, when, and how much? We otherwise couldn’t afford the level of talent it would take to do this.”

Cutting through the noise

Google and Pluto7 software helped California Design Den reduce inventory carryovers by more than 50%. Inventory tracking and distribution, along with insights and visibility into product sales, are faster, more efficient, and accurate. Google Cloud Platform flexible pricing, speed, reliability, security, and scalability enable California Design Den to stay relevant and be more competitive.

In addition to benefiting from Google Cloud Platform, California Design Den relies on G Suite—also part of Google Cloud—to enhance collaboration among its global teams. Previously, the company’s email server would sometimes crash, due to the heavy load of sharing product photos and other data. “Gmail and Google Drive handle the everyday demands on the business effortlessly and reliably,” Deepak says.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important. We can focus on analytics to guide us to success today and in the future.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

The company is exploring additional ways to leverage Google Cloud Platform in the near future. For example, one possibility is to import customer reviews from sites where products are sold into Google BigQuery, and to use that data to perform sentiment analysis via Google Cloud Natural Language. It could provide another valuable data source to help California Design Den’s team decide where to focus future designs.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important,” Deepak says. “We can focus on analytics to guide us to success today and in the future.”