But underpinning all ML advancements is a common challenge: fast-tracking the building and deployment of ML models into production, and abstracting the most technically complex processes into unified platforms that open ML to more users.

Our mission is to remove every barrier in the way of deploying useful and predictable ML at scale. This is why, in May 2021, we announced the general availability of Vertex AI, a managed ML platform designed specifically to accelerate the deployment and maintenance of ML models. Leveraging Vertex AI, data scientists can speed up ML development and experimentation by 5x, with 80% fewer lines of code required.

In the year since the launch, customers across diverse industries have successfully accelerated the deployment of machine learning models in production with Vertex AI.

In fact, through Vertex AI and BigQuery, we have seen 2.5 times more machine learning predictions generated in 2021 compared to the previous year. Additionally, customers are seeing great value in Vertex AI’s unified data and AI story. This is best represented by the 25x growth in active customers we have seen for Vertex AI Workbench over the last six months.

Let’s take a look at how some of these organizations are using Vertex AI today.

Accelerating ML in retail: ML at Wayfair, Etsy, Lowe’s and Magalu

Our research of over 100 global retail executives identified that AI and ML-powered applications have the potential to drive $230-515 billion in business value.  Whether the use cases involve optimizing inventory or bettering customer experience, retail is among the industries where ML adoption has been strongest.

For example, online furniture and home goods retailer Wayfair has been able to run large model training jobs 5-10x faster by leveraging Vertex AI.

“We’re doing ML at a massive scale, and we want to make that easy. That means accelerating time-to-value for new models, increasing reliability and speed of very large regular re-training jobs, and reducing the friction to build and deploy models at scale,” said Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning at Wayfair, in a Forbes article. Vertex AI helps the company to “weave ML into the  fabric of how we make decisions,” he added.

Elsewhere, Etsy estimates it  has reduced the time it takes to go from ideation to a live ML experiment by about 50%.

“Our training and prototyping platform largely relies on Google Cloud services like Vertex AI and Dataflow, where customers can experiment freely with the ML framework of their choice,” the company notes in a blog post. “These services let customers easily leverage complex ML infrastructure (such as GPUs) through comfortable interfaces like Jupyter Notebooks. Massive extract transform load (ETL) jobs can be run through Dataflow while complex training jobs of any form can be submitted to Vertex AI for optimization.”

Forecasting in particular is a major retail use case that can be significantly bettered with the power of ML. Vertex AI Forecast is already helping Lowe’s with a range of models at the company’s more than 1,700 stores, according to Amaresh Siva, senior vice president for Innovation, Data and Supply Chain Technology at Lowe’s.

“Using Vertex AI Forecast, Lowe’s has been able to create accurate hierarchical models that balance between SKU and store-level forecasts. These models take into account our store-level, SKU-level, and region-level inventory, promotions data and multiple other signals, and are yielding more accurate forecasts,” said Siva.

Brazilian retailer Magalu has similarly deployed Vertex AI to reduce inventory prediction errors.

With Vertex AI, “four-week live forecasting showed significant improvements in error (WAPE) compared to our previous models,” said Fernando Nagano, director of Analytics and Strategic Planning at Magalu. “This high accuracy insight has helped us to plan our inventory allocation and replenishment more efficiently to ensure that the right items are in the right locations at the right time to meet customer demand and manage costs appropriately.”

From memory to manufacturing to mobile payments: ML at Seagate, Coca Cola Bottlers Japan, and Cash App

Retail is not the only industry leveraging the power of AI and ML. According to our research, 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

Google joined forces with Seagate, our HDD original equipment manufacturer (OEM) partner for Google’s data centers, to leverage ML for improved prediction of frequent HDD problems, such as disk failure. The Vertex AI AutoML model generated for the effort achieved a precision of 98% with a recall of 35%, compared to precision for 70-80% and recall of 20-25% for the competing custom ML model.

Coca Cola Bottlers Japan (CCBJ) is also ramping up its ML efforts, using Vertex AI and BigQuery to process billions of data records from 700,000 vending machines, helping the company to make strategic decisions about when and where to locate products.

“We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” said Minori Matsuda,

Data Science Manager / Google Developer Expert at CCBJ, in a blog post.  “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

Turning to finance, Cash App, a platform from the U.S.-based financial services company Square, is leveraging products from Google Cloud and NVIDIA to achieve a roughly 66% improvement in completion time for core ML processing workflows.

“Google Cloud gave us critical control over our processes,” said Kyle De Freitas, a senior software engineer at Dessa, which was acquired by Cash App in 2020. “We recognized that Compute Engine A2 VMs, powered by the NVIDIA A100 Tensor Core GPUs, could dramatically reduce processing times and allow us to experiment much faster. Running NVIDIA A100 GPUs on Google Cloud’s Vertex AI gives us the foundation we need to continue innovating and turning ideas into impactful realities for our customers.”

Driving toward an ML-fueled future: ML at Cruise and SUBARU

In the automotive space, manufacturers throughout the world have invested billions to digitize operations and invest in AI to both optimize design and enable new features.

For instance, self-driving car service Cruise has millions of miles of autonomous travel under its belt, with Vertex AI helping the company to quickly train and update ML models that power crucial functions like image recognition and scene understanding.

“After we ingest and analyze that data, it’s fed back into our dynamic ML Brain, a continuous learning machine that actively mines from the collected data to automatically train new models that exceed the performance of the older models,” explained Mo Elshenawy, Executive Vice President of Engineering at Cruise, in a blog post. “This is done with the help of Vertex AI, where we are able to train hundreds of models simultaneously, using  hundreds of GPU years every month!”

Meanwhile, SUBARU is turning to ML to eliminate fatal accidents caused by its cars. SUBARU Lab uses Google Cloud to analyze images from the company’s EyeSight  stereo cameras, for example. The team uses a combination of NVIDIA A100 GPUs and Compute Engine for processing muscle, with data scientists and data engineers using Vertex AI to build models.

“I chose Google Cloud from many platforms because it had multiple managed services such as Verex AI, the managed notebooks option, and Vertex AI Training that were useful for AI development. It was also fascinating to have high-performance hardware that could handle large-scale machine learning operations,” said Thossimi Okubo, Senior Engineer of AI R&D at SUBARU.

Working together to accelerate ML deployment

We are very encouraged by the adoption of Vertex AI, and we are excited to continue working with key customers and partners to expand our thinking around the challenges data scientists face in accelerating deployment of ML models in production. Watch our Google Cloud Applied ML Summit session with Smi-tha Shyam, Director of Engineering for Uber AI, and Bryan Goodman, Director of AI and Cloud at Ford, to get a sense of how we’re working with partners and customers in this journey.

To learn more, check out additional expert commentary at our Applied ML Summit, peruse our latest Vertex AI updates, or visit our Data Science on Google Cloud page to learn more about our unified data and AI story.