You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall when it comes time to elevate your experiments up to production scale. Suddenly, your concerns are more than just getting the highest accuracy score.

Sound familiar?

Developing production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. But that process can feel a bit daunting.

To make things a little easier for you, we’ve created the Prototype to Production video series, which covers all the foundational concepts you’ll need in order to build, train, scale, and deploy machine learning models on Google Cloud using Vertex AI.

Let’s jump in and see what it takes to get from prototype to production!

Getting started with Notebooks for machine learning

Episode one of this series shows you how to create a managed notebook using Vertex AI Workbench. With your environment set up, you can explore data, test different hardware configurations, train models, and interact with other Google Cloud services.

Storing data for machine learning

When working on machine learning problems, it’s easy to be laser focused on model training. But the data is where it all really starts.

If you want to train models on Vertex AI, first you need to get your data into the cloud. In episode 2, you’ll learn the basics of storing unstructured data for model training and see how to access training data from Vertex AI Workbench.

Training custom models on Vertex AI

You might be wondering, why do I need a training service when I can just run model training directly in my notebook? Well, for models that take a long time to train, a notebook isn’t always the most convenient option. And if you’re building an application with ML, it’s unlikely that you’ll only need to train your model once. Over time, you’ll want to retrain your model to make sure it stays fresh and keeps producing valuable results.

Manually executing the cells of your notebook might be the right option when you’re getting started with a new ML problem. But when you want to automate experimentation at scale, or retrain models for a production application, a managed ML training option will make things much easier.

Episode 3 shows you how to package up your training code with Docker and run a custom container training job on Vertex AI. Don’t worry if you’re new to Docker! This video and the accompanying codelab will cover all the commands you’ll need.

CODELAB: Training custom models with Vertex AI

How to get predictions from an ML model

Machine learning is not just about training. What’s the point of all this work if we don’t actually use the model to do something?

Just like with training, you could execute predictions directly from a notebook by calling model.predict. But when you want to get predictions for lots of data, or get low latency predictions on the fly, you’re going to need something more than a notebook. When you’re ready to use your model to solve a real world problem with ML, you don’t want to be manually executing notebook cells to get a prediction.

In episode 4, you’ll learn how to use the Vertex AI prediction service for batch and online predictions.

CODELAB: Getting predictions from custom trained models

Tuning and scaling your ML models

By this point, you’ve seen how to go from notebook code, to a deployed model in the cloud. But in reality, an ML workflow is rarely that linear. A huge part of the machine learning process is experimentation and tuning. You’ll probably need to try out different hyperparameters, different architectures, or even different hardware configurations before you figure out what works best for your use case.

Episode 5, covers the Vertex AI features that can help you with tuning and scaling your ML models. Specifically, you’ll learn about hyperparameter tuning, distributed training, and experiment tracking.

CODELAB: Hyperparameter tuning on Vertex AI
CODELAB: Distributed Training on Vertex AI

We hope this series inspires you to create ML applications with Vertex AI! Be sure to leave a comment on the videos if you’d like to see any of the concepts in more detail, or learn how to use the Vertex AI MLOps tools.

If you’d like try all the code for yourself, check out the following codelabs:

• Training custom models with Vertex AI
• Getting predictions from custom trained models
• Hyperparameter tuning on Vertex AI
• Distributed training on Vertex AI

Unstructured data such as images, speech and textual data can be notoriously difficult to manage, and even harder to analyze. The analysis of unstructured data includes use cases such as extracting text from images using OCR, sentiment analysis on customer reviews and simplifying translation for analytics. All of this data needs to be stored, managed and made available for machine learning.

The new BigQuery ML inference engine empowers practitioners to run inferences on unstructured data using pre-trained AI models. The results of these inferences can be analyzed to extract insights and improve decision making. This can all be done in BigQuery, using just a few lines of SQL.

In this blog, we’ll explore how the new BigQuery ML inference engine can be used to run inferences against unstructured data in BigQuery. We’ll demonstrate how to detect and translate text from movie poster images, and run sentiment analysis against movie reviews.

BigQuery ML’s new inference engine

Google Cloud is home to a suite of pre-trained AI models and APIs. The BigQuery ML inference engine can call these APIs and manage the responses on your behalf. All you have to do is define the model you want to use and run inferences against your data. All of this is done in BigQuery using SQL. The inference results are returned in JSON format and stored in BigQuery for analysis.

Why run your inferences in BigQuery?

Traditionally, working with AI models to run inferences required expertise in programming languages like Python. The ability to run inferences in BigQuery using just SQL can make generating insights from your data using AI simple and accessible. BigQuery is also serverless, so you can focus on analyzing your data without worrying about scalability and infrastructure.

The inference results are stored in BigQuery, which allows you to analyze your unstructured data immediately, without the need to move or copy your data. A key advantage here is that this analysis can also be joined with structured data stored in BigQuery, giving you the opportunity to deepen your insights. This can simplify data management and minimize the amount of data movement and duplication required.

Which models are supported?

For now, the BigQuery ML inference engine can be used with these pre-trained Vertex AI models:

• Vision AI API: This model can be used to extract features from images managed by BigQuery Object Tables and stored on Cloud Storage. For example, Vision AI can detect and classify objects, or read handwritten text.
• Translation AI API: This model can be used to translate text in BigQuery tables into over one hundred languages.
• Natural Language Processing API: This model can be used to derive meaning from textual data stored in BigQuery tables. For example, features like sentiment analysis can be used to determine whether the emotional tone of text is positive or negative.

So, how does this work in practice? Let’s look at an example using images of movie posters

1. We will define our pre-trained models for Vision AI, Translation AI and NLP AI in BigQuery ML.
2. We’ll then use Vision AI to detect the text from some classic movie posters images. 
3. Next, we’ll use Translation AI to detect any foreign posters and translate them to a language of our choosing – English in this case. 
4. Finally, we’ll combine our unstructured data with structured data in BigQuery.
   We’ll use the extracted movie titles from our movie posters to look up the viewer reviews from the BigQuery IMDB public dataset. We can then run sentiment analysis against these reviews using NLP AI.

Note: The BigQuery ML inference engine is currently in Preview. You will need to complete this enrollment form to have your project allowlisted for use with the BQML Inference Engine.

We’ll give examples of the BigQuery SQL needed to define your models and run your inferences. You’ll want to check out our notebook for a detailed guide on how to get this up and running in your Google Cloud project.

1. Define your AI Models in BigQuery

You will need to enable the APIs listed below, and also create a Cloud resource connection to enable BigQuery to interact with these services.

You can then run the CREATE MODEL query for each AI service to create your pretrained models, replacing the model_name as required.

CREATE OR REPLACE MODEL 
`{PROJECT_ID}.{DATASET_ID}.{VISION_MODEL_NAME}`
REMOTE WITH 
CONNECTION `{PROJECT_ID}.{REGION}.{CONN_NAME}`
OPTIONS ( remote_service_type = '<model_name>' );

2. Use the Vision AI API to detect text in images stored in Cloud Storage

You will need to create an object table for your images in Cloud Storage. This read-only object table provides metadata for images stored in Cloud Storage:

CREATE OR REPLACE EXTERNAL TABLE
`{PROJECT_ID}.{DATASET_ID}.{OBJECT_TABLE_NAME}`
WITH
CONNECTION `{REGION}.{CONN_NAME}`
OPTIONS (object_metadata = 'SIMPLE', uris = ['{BUCKET_LOCATION}/*']);

To detect the text from our posters, you can then use ML.ANNOTATE_IMAGE and specify the text_detection feature.

SELECT
       ml_annotate_image_result.full_text_annotation.text AS text_content,
       *
FROM
       ML.ANNOTATE_IMAGE(
         MODEL `{PROJECT_ID}.{DATASET_ID}.{VISION_MODEL_NAME}`,
         TABLE `{DATASET_ID}.{OBJECT_TABLE_NAME}`,
         STRUCT(['TEXT_DETECTION'] AS vision_features));

A JSON response will be returned to BigQuery that includes the text content and language code of the text. You can parse the JSON to a scalar result using the dot annotation highlighted above.

3. Use the Translation AI API to translate foreign movie titles 

ML.TRANSLATE can now be used to translate the foreign titles we’ve extracted from our images into English. You just need to specify the target language and the table of the movie posters for translation:

SELECT
text_content,
STRING(ml_translate_result.translations[0].detected_language_code)
as original_language,
STRING(ml_translate_result.translations[0].translated_text)
as translated_title
FROM
  ML.TRANSLATE(
MODEL `{PROJECT_ID}.{DATASET_ID}.{TRANSLATE_MODEL_NAME}`,
TABLE `{DATASET_ID}.image_results`,
STRUCT('TRANSLATE_TEXT' as translate_mode, "en" as target_language_code));

Note: The table column with the text you want to translate must be named text_content:

The table of results will include json that can be parsed to extract both the original language and the translated text. In this case, the model has detected that title text is in French and has translated it to English:

4. Finally, use natural language processing (NLP) to run sentiment analysis against movie reviews

You can easily join inference results from your unstructured data with other BigQuery datasets to bolster your analysis. For example, we can now join the movie titles we extracted from our posters with thousands of movie reviews stored in BigQuery’s IMDB public dataset `bigquery-public-data.imdb.reviews`.

You can use ML.UNDERSTAND_TEXT with the analyze_sentiment feature to run sentiment analysis against some of these reviews to determine whether they are positive or negative:

SELECT
   primary_title, start_year, text_content AS review,
   FLOAT64(ml_understand_text_result.document_sentiment.score) AS score,
   FLOAT64(ml_understand_text_result.document_sentiment.magnitude) AS magnitude,
FROM
   ML.UNDERSTAND_TEXT(
     MODEL `{PROJECT_ID}.{DATASET_ID}.{NLP_MODEL_NAME}`,
           (
           SELECT
             primary_title, start_year, review AS text_content
           FROM
             `bigquery-public-data.imdb.title_basics` titles
           JOIN
             `bigquery-public-data.imdb.reviews` reviews
           ON
             reviews.movie_id = titles.tconst
           WHERE
             UPPER(titles.primary_title) = 'THE LOST WORLD' AND
             start_year = 1925
           ),
      STRUCT("analyze_sentiment" AS nlu_option)) ;

Note: The table column with the text you want to analyze must be named text_content:

The JSON response will include a score and magnitude. The score indicates the overall emotion of the text while the magnitude indicates how much emotional content is present:

So, how did the Lost World compare with other movies that year?

To wrap up, we’ll compare the average review score of the 1925 Lost World movie to other movies released that year to see which was more popular. This can be done using familiar SQL analysis:

SELECT
 primary_title, start_year,
 AVG(FLOAT64(ml_understand_text_result.document_sentiment.score))AS av_score,
 AVG(FLOAT64(ml_understand_text_result.document_sentiment.magnitude)) AS av_magnitude
FROM
 ML.UNDERSTAND_TEXT(
   MODEL `{PROJECT_ID}.{DATASET_ID}.{NLP_MODEL_NAME}`,
         (
         SELECT
           primary_title, start_year, movie_id, review AS text_content
         FROM
           `bigquery-public-data.imdb.title_basics` titles
         JOIN
           `bigquery-public-data.imdb.reviews` reviews
         ON
           reviews.movie_id = titles.tconst
         WHERE
           start_year = 1925
         ),
   STRUCT("analyze_sentiment" AS nlu_option))
GROUP BY
   primary_title, start_year
ORDER BY
   av_score DESC;

It looks like The Lost World narrowly missed out on the top spot to Sally of the Sawdust!

Want to learn more?

Check out our notebook for a step by step guide on using the BQML inference engine for unstructured data in Google Cloud. You can also check out our Cloud AI service table-valued functions overview page for more details. Curious about pricing? The BQML Pricing page gives a breakdown of how costs are applied across these services.

Notified is a leading communications cloud for events, public relations, and investor relations to drive meaningful insights and outcomes. They provide communications solutions to effectively reach and engage customers, investors, employees, and the media.

One of Notified’s Public Relations solutions is the ‘Media Contact Database’ that allows customers to discover media and influencers in a unique media database powered by AI and human-curated research. 

The goal of the initiative is to expand the scope of the AI driven, dynamically discovered influencers, and analyze online news articles using AI/ML technologies to extract entities and classify content. The prior process to extract insights from news articles provided only 30-40% of the desired results, and there were accuracy and stability issues that resulted in a lot of manual intervention.

Journalist Beat

A key outcome of the AI driven process is to identify the ‘Journalist Beat’. A Journalist Beat essentially summarizes the individual’s area of focus such as a sports writer, financial journalist etc. 

Three options were evaluated for the AI/ML process to generate the Journalist Beats :

Option 1:  Topic ML

Unsupervised ML approach to determine the commonly used terms.

• Pro: Common approach to grouping documents and determine similar text
• Con: Unbounded list of text

Option 2: ML Classification

Build classification models (supervised) to map reference articles to ‘Beats’ 

• Pro: Aligns to ‘Research Analytics’ existing processes
• Con: Time to build and maintain ML models for hundreds of beats.

Option 3: GCP Context Classification

Leverage GCP’s Natural Language API for initial classification and as input to Notified single model

• Pro: Aligns to ‘Research Analytics’ without building ML models.

Ultimately the GCP Natural Language API solution was chosen because of the speed of execution and a high level of accuracy with the pretrained models. The Notified team was able to launch the product feature within a few weeks, without ever needing to do extensive data collection and train the models. 

Here is the high level process that was implemented for Journalist Beats.

Since Notified supports curated media contacts globally, news articles were instantly translated to English using GCP Translation API. GCP Natural Language API’s solution to classify text was used to analyze the translated text and generate the list of content categories.

Solution Architecture

Here is a sample solution architecture for the ‘Discovered Journalist’ process.

Three core principles guided the above architecture – Serverless & Fully Managed, Scalability & Elasticity for flexibility and to optimize costs, API led real-time processing.

In addition to the GCP Natural Language API and Translation API below are a few serverless GCP products that were part of the automated solution:

• BigQuery is Google Cloud’s fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time.
• Cloud Run is a fully managed serverless platform that can be used to develop and deploy highly scalable containerized applications.
• Cloud Tasks is a fully managed service that allows you to manage the execution, dispatch, and delivery of a large number of distributed tasks.

The powerful pre-trained models of the Natural Language API provide a comprehensive set of features to apply natural language understanding to applications such as sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. 

Notified looks ahead to super-scaling

In an effort to even further improve its best in class ‘Media Contact Database’, Notified looks to super scale the above AI driven Influencer Discovery process to the order of 100+ million news articles per month. It plans to expand the scope of entities extracted from the news articles and provide a news exploration service for its customers by performing intelligent entity-based searches.To watch your markets evolve, see how competitors add AI insights. To actually stay in the market, make AI the main driver of your product road maps. GCP Natural Language API accelerated our ability to adopt AI at scale.Thomas Squeo, CTO, Notified

Acknowledgments

We’d like to thank our collaborators at Google and Notified for making this blog post possible. Thanks to Arpit Agrawal at MediaAgility for contributing to this blog post.

To learn more about how Google Cloud Natural Language AI can help your enterprise, try out an interactive demo and take the next step, visit the product overview page here.

Businesses rely on processing an inflow of documents to drive processes and make decisions. Many such documents are combined into a single file. For example, a loan application may have a driver’s license, paystub, W2, bank statement, and other document types within a single file. The complexity of handling many document types within a single file makes it difficult for businesses to manage at scale. 

At Google Cloud, we’re committed to solving these challenges with continued investment in our Document AI solutions suite which offers machine learning products for document processing and insights. Document AI Workbench helps users quickly build ML models with world-class accuracy, trained for their specific use cases. In February 2023, we launched the Custom Document Extractor (CDE) in General Availability (GA) to help users extract structured data from documents in production use cases. In March 2023, we launched the Custom Document Classifier (CDC) in GA to help automatically classify document types. Today, we announce the newest feature of Document AI Workbench, Custom Document Splitter (CDS) in GA to help users automatically split and classify multiple documents within a single file. 

CDS provides tangible business value to customers by helping them sort and classify documents. For example, businesses can validate if they have all the needed documents from an applicant. Furthermore, individually classified documents enable businesses to better automate downstream processes, including selecting the proper storage, analysis, or processing steps based on the document type. The efficiencies enabled by CDS helps businesses lower their document processing time and cost.

Benefits of splitting and classification models in Document AI Workbench 

Document AI Workbench can save time and money by simplifying model training, from dataset management, to testing, to deployment. CDS helps businesses achieve higher automation rates to scale processes while lowering costs.

Sean Earley, VP of Delivery Services at Zencore said, “We completed a project for a large bank using Document AI Workbench to split, classify, and extract data from documents to automate Home Mortgage Disclosure Act reporting. Given the accuracy of the models we built, our client estimated increasing loan reporting coverage from 20% to 100% while eliminating thousands of errors per year, drastically reducing the operational cost of the bank’s compliance reporting procedures.”  

Fabian Beckmann, Manager Artificial Intelligence & Data at Deloitte Consulting GmbH said, “By leveraging Document AI’s Custom Document Splitter, our client, Commerzbank, a large european bank, can effortlessly segment customer submissions tailored to their back-office requirements, significantly diminishing the need for extra manual sorting or routing. This integration paves the way towards seamless automation within the Document AI pipeline, delivering substantial business benefits.“

According to Kaïs Albichari – ML Tribe Tech Lead, G Cloud at IT services firm Devoteam, “Custom Document Splitter (CDS) has helped one of our clients in the financial services industry save significant time and improve data accuracy. By identifying which parts of documents they can discard and which they retain for entity extraction, CDS has helped the company automate its document processing tasks. The implementation resulted in a more efficient and streamlined workflow, freeing employees to focus on other tasks. Devoteam’s G Cloud team helped the company implement CDS and achieve these benefits.”

Frank Neugebauer, a Google Cloud Insurance Solutions Consultant, worked with a Fortune 100 insurance company and used CDS to create a model to split and classify millions of insurance documents with up to 98% accuracy. With this information, the insurer can better understand the nature of their unstructured data to inform business strategy, including volume for specific document types to inform extraction work. The customer considers this level of insight unprecedented in their 200+ year history.  

How to use Custom Document Splitter

You can leverage a simple interface in the Google Cloud Console and a set of public APIs to prepare training data, create and evaluate models, deploy a model into production, and call an API endpoint to split and classify document types. You can follow the documentation for instructions to create, train, evaluate, deploy, and run predictions with models.

Import and prepare training data

To get started, import and label documents to train and evaluate an ML model. 

To quickly build a training dataset, import single documents, one document per file, and bulk label them with the relevant document type. You can import one folder or multiple folders at once and choose the correct document type per folder. As shown in the next image, one import could have a folder with 200 bank statements, another folder with 200 W2s, another folder with 200 paystubs, etc., all of which are labeled at once while imported. Up to 30,000 documents and 100,000 pages can be inputted for training. This way, you can build a training dataset with hundreds of labeled documents per class in minutes. As always, if documents are already labeled using other tools, simply import labels with JSON in the Document format.

You can initiate training with a click of a button. Once you have trained a model, you can use it  to automatically label documents added to your dataset, letting you quickly build robust test and training datasets to evaluate and improve model performance.

To accurately evaluate a CDS model, import files which contain multiple document types within the same file and assign them to the test dataset. Then, use a simple interface to define document boundaries and types.

The ground truth you label in the test dataset is used to evaluate splitting and classification predictions from the CDS model.

Going into production

Once a model meets accuracy targets, it’s time to deploy into production and call the API endpoint to split and classify document types.

Getting started with Document AI Workbench 

Custom Document Splitter is publicly available in GA and ready to help customers automate document splitting and classification. Learn more via our Document AI Workbench web page, Document AI Workbench documentation or try it out in the Google Cloud Console.

AI, deep learning, and image recognition is transforming the shopping experience. These technologies enable consumers to use product images or screenshots rather than text to search for similar products. This improves the customer experience and enables retailers with online and offline outlets to provide a genuine omnichannel experience.

The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.

—Renjie Yao, Data Platform Lead, ViSenze

Visual commerce provider ViSenze is helping some of the world’s leading retailers improve conversion rates through image-based search.

The business’s products also enable media companies to use the platform to turn images and videos into engagement opportunities—driving new and incremental revenues.

Created through NExT—a research center established by the National University of Singapore and Tsinghua University of China—ViSenze now operates in the United States, United Kingdom, India, China, and Singapore. The business is backed by Japan-based internet and ecommerce company Rakuten and cross-border investment specialist WI Harper Group.

Growth in SMB and Mobiles

Renjie Yao, Data Platform Lead at ViSenze, sees opportunities for growth in the small-to-medium business sector, where companies do not have the resources to build similar technologies, and with mobile device OEMs to integrate ViSenze natively on smartphones.

Phone owners can activate a “shopping lens” on camera and gallery apps to capture an image of a product. They then receive matching results from more than 800 partner merchants and retailers and can then click through to product pages on partner apps or mobile websites. Alternatively, they may use a photo to compare products sold on different sites or shop matching styles.

“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs.”

—Renjie Yao, Data Platform Lead, ViSenze

The ViSenze API analyzes the contents of a selected or clicked image and sends the information back to the organization’s visual commerce platform. The platform feeds back similar results based on that information.

The ViSenze offering also extends to image analysis for the tagging of product attributes—such as a white turtleneck cardigan with full sleeves—to provide an improved search experience.

Data Vital to ViSenze

Capturing and analyzing large volumes of data is integral to ViSenze. “We have to understand how consumers interact with our customers’ ecommerce websites and apps,” says Yao. “For example, we need to know who has looked at a particular pair of jeans on a website and whether that visit led to a conversion. We can then tell that customer whether they need to make more stock available.”

ViSenze also relies on data to provide high-quality training for its image recognition models and its domain-specific models for online retail.

Protect Customer Data

Data is vital to ViSenze—but customer privacy is most important. “All the data we collect is transparent to our customers, meaning they can decide what they do not want us to collect. In addition, all personal data processing complies with privacy protection regulations in each region, such as the General Data Protection Regulation in Europe.”

A Quick Move to the Cloud

ViSenze started operations using servers, storage, networking, and associated systems in an on-premises data center operated by NExT.

However, to support rapid growth, the business decided to move its workloads to the cloud. ViSenze opted for a multi-cloud architecture, using in part a Google Cloud data infrastructure.

“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs,” says Yao. “We could connect different components with the click of a mouse.” Further, the business found it could easily configure rules and pipelines to route data logs to relevant Google Cloud services.

The review found Google Cloud’s extensive managed services would also remove administration and maintenance tasks from ViSenze’s in-house technology team—freeing team members to focus on more valuable tasks.

In addition, Google Cloud provided the security features—including custom hardware running hardened operating systems and file systems and encryption of data at rest and in transit—needed to protect sensitive information. Finally, the location of Google Cloud regions in several countries would enable the business to meet regulatory and data sovereignty requirements.

A Three-Month Implementation

ViSenze opted to move to Google Cloud in mid-2017 and completed a three-month implementation using internal resources. “The process was very smooth and intuitive, and we had no problems building our entire data platform within Google Cloud,” says Yao.

The business now uses an architecture comprising Google Kubernetes Engine to manage and orchestrate Docker containers running in Google Cloud Platform; BigQuery to provide an analytics data warehouse, with Google Data Studio providing customizable visualization and reports; Stackdriver to monitor and manage virtual machine instances and services inside Google Cloud; Cloud SQL to manage its relational databases for real-time analytics; Compute Engine to provide compute resources; Cloud Storage to store files and objects; Cloud Pub/Sub to provide real-time messaging between applications; and Cloud Functions to build event-driven applications.

After collecting the request logs of users in virtual machine instances and Docker containers, ViSenze distributes them in three directions. “We export raw logs into Cloud Pub/Sub for indexing inside an Elasticsearch search engine, and to a BigQuery data warehouse for further analytics,” explains Yao. “We also use Cloud Functions-created applications to obtain the logs from Cloud Pub/Sub to perform some real-time calculations.”

“We are currently using Airflow workflow management on Compute Engine as our hosted ETL platform, but are likely to move to Cloud Composer in future.”

500 Million Records Per Day

With Google Cloud providing its data infrastructure, ViSenze is well positioned to meet internal and customer demands for more granular insights. The business is now processing 500 million records per day through BigQuery and saves up to one year’s aggregated data—excluding any personal data—in the data warehouse for analysis.

The nature of ViSenze’s business means most reports are generated for data processed on an hourly, daily, or monthly basis. “BigQuery is extremely stable and performance optimized, regardless of the volume of data it processes,” says Yao. “Across BigQuery and other Google Cloud Platform services, we’ve recorded 99.99% availability over the past year.”

The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.

“With BigQuery, we have saved the equivalent of two full-time engineers and now need only half of one person’s time to maintain our whole data platform,” says Yao.

“In addition, BigQuery integrates closely with Data Studio, enabling non-technical people in our product and business teams to create dynamic, detailed analysis dashboards. We now use Data Studio to create nearly 50 separate reports.”

The business has now grown to offer access to more than 1 billion users and a listing of more than 400 million purchasable products.

Next Steps

ViSenze is now researching the potential of the Cloud AutoML suite of machine learning products to improve the training of its models and run a fully managed NoSQL database through Cloud Datastore.

“A NoSQL database service is the only missing piece of our architecture for now, and using Cloud Datastore would enable us to focus almost exclusively on our business,” says Yao. “With Google Cloud Platform, we are ideally positioned to continue providing support to our business team and help them continue expanding into new markets.

“In addition, we can help retailers and consumers to unlock the potential of the web and apps to transform the purchasing experience.”