IT Team Figures Out Easiest Way to Build Data Pipelines and Create ML Models
14130
Of your peers have already read this article.
5:15 Minutes
The most insightful time you'll spend today!
Building a strong brand in today’s hyper-competitive business environment takes vision. It also requires a flexible, easily managed approach to digital asset management (DAM), so marketing professionals and other stakeholders can easily share, store, track, and manipulate assets to build the brand.
Many of today’s leading companies, including JetBlue, Slack, TripAdvisor, Lyft, and HealthONE, rely on Brandfolder to deliver consistent, organized, and efficient brand experiences. Brandfolder provides an easy-to-use platform that can scale across an entire company with little end-user training, empowering customers to distribute digital assets wherever they are needed. Customers also gain much greater insight into how those assets are used, and how to use them more effectively in marketing campaigns and brand messaging.
“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform.”
—Ajay Rajasekharan, Head of Data Science, Brandfolder
Brandfolder is constantly advancing its development efforts to introduce new data-driven features without complicating the user experience. Big data, artificial intelligence (AI), and machine learning (ML) are key to meeting customers’ unique business needs, and essential for Brandfolder to compete in the fast-moving DAM industry. To enhance these capabilities, Brandfolder sought a public cloud provider that could help it scale its data pipeline cost effectively while providing access to advanced AI technologies.
After graduating from the Techstars startup accelerator program in 2013, Brandfolder tried two other cloud providers before standardizing on Google Cloud Platform (GCP).
“We saw a difference with Google Cloud from the very beginning because the interactions felt like a strategic relationship,” says Jim Hanifen, Head of Product at Brandfolder. “Google gave us startup credits and a lot of face-to-face support, which we hadn’t experienced with other cloud providers. We decided to move our entire infrastructure to Google Cloud Platform.”
Building an ML platform for brand intelligence
After performing an initial lift-and-shift migration of virtual machines (VMs) onto Compute Engine, Brandfolder built an ML platform using GCP managed services to seamlessly deliver its data products. The platform leverages Cloud SQL, Cloud Storage as the data lake, Cloud Dataproc for cloud-native Apache Spark computing clusters, Cloud Composer as the batch job scheduler, Cloud Pub/Sub as the backbone data pipeline, Container Registry to store Docker images, and Google Kubernetes Engine (GKE) as the application orchestrator. Cloud Dataflow brings data into the data lake and into BigQuery for analysis.
“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform,” says Ajay Rajasekharan, Head of Data Science at Brandfolder, who describes the architecture in a detailed blog. “We simply ingest raw application and event data on one end and output an ML service on the other.”
“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost. We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”
—Brett Nekolny, Head of Engineering, Brandfolder
For many general use cases, Brandfolder does not need to build custom ML models, and instead relies on pre-trained API models from GCP. For example, it uses Vision API and Video Intelligence API to auto-tag creative assets on import to enable fast, intuitive searches across images and videos. When more product- and brand-specific modeling is required to address unique customer use cases, Brandfolder builds and trains custom ML models using its GCP pipeline or Cloud AutoML, a suite of products built on Google transfer learning and neural architecture search technology. For example, if a Brandfolder customer makes different types of grills, Brandfolder can use AutoML Vision to train a model to recognize the different grills.
“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost,” explains Brett Nekolny, Head of Engineering at Brandfolder. “We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”
Industry-leading security and performance
Google Cloud’s security model helps Brandfolder give existing and prospective customers peace of mind that their data will be protected. Cloud Identity & Access Management (Cloud IAM) provides enterprise-grade access control, while Cloud Identity-Aware Proxy (Cloud IAP) enables remote users to work more securely without the hassles of a VPN client. GCP also isolates cloud resources into projects, making it easy to assign permissions and keep data and VMs organized and segregated.
“With Google Cloud, everything begins and ends with security, which makes things very easy for us,” says Jim. “If we’re under a security review, we can submit a Google security white paper. If a potential customer has security concerns, we tell them we are hosted on GCP, and those concerns go away.”
To give customers even better application performance for accessing their brand assets, Brandfolder uses Cloud Memorystore, an in-memory data store service for Redis, to cache data and provide sub-millisecond data access for production applications.
“It was much easier for us to use Cloud Memorystore versus running Redis on our compute instances,” says Brett. “The high availability, replication across zones, and automatic failover with no data loss are big for us.”
Global private network interconnects between Google Cloud and the Fastly content delivery network (CDN) dramatically reduce latency, allowing Brandfolder’s customers to deliver and update even very large creative assets quickly around the world.
“What’s beautiful about the relationship between Google and Fastly is that if one of our customers uploads a new version of an asset, we can propagate that out to Fastly, and the new version will automatically show up in all the places where it’s referenced,” says Brett.
“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter. Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”
—Jim Hanifen, Head of Product, Brandfolder
Improving employee and customer productivity
Brandfolder also uses Google solutions for real-time collaboration and productivity, using G Suite to connect employees with intuitive, cloud-based apps. Teams use Gmail, Calendar, Docs, Drive, Sheets, Slides, and Hangouts Meet every day to move the business forward. Many of Brandfolder’s customers are also G Suite users, and Brandfolder offers a plug-in that allows them to view their creative assets inside of Docs and pull images in as needed. Customers can also log into Brandfolder with their G Suite credentials, making the solution even easier to use.
“We’ve been using G Suite since the beginning, and it’s helped us collaborate efficiently to build a successful, growing company,” says Jim. “Our teams expect to have that kind of close collaboration, and everyone here enjoys the G Suite experience.”
Driving 99 percent annual business growth
With automated tagging and other innovative AI-based features, Brandfolder is helping customers locate and distribute assets faster. As a result, Brandfolder is building customer loyalty and increasing sales, growing its business by 99 percent year-over-year. Since moving to GCP, Brandfolder has been able to scale its analytics and data pipeline 50x without a corresponding increase in costs and has not had to expand its development team.
“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter,” says Jim. “Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”
5134
Of your peers have already watched this video.
1:47 Minutes
The most insightful time you'll spend today!
How adidas’s Marketing Team Benefits from Google Cloud Marketing Platform
We live in a world where the consumer journey is no longer linear, making it difficult to know when to deliver a brand message or a product message.
“We talk a lot about the consumer journey no longer being linear. It’s very difficult to know when the consumer is going to come in contact with your message. And because of that it makes it much harder to deliver the right message at the right time,” says Chris Murphy, Head of Digital Experience, adidas.
To solve the problem, adidas turned to the Google Cloud Marketing Platform to use audience insights to inform the art of storytelling and help teams, from brand marketing to retail to e-commerce, deliver relevant messages across channels.
“In a singular environment like Google Cloud Marketing Platform, when you’re able to see audience insights and information…we know when someone should be receiving a brand message versus a product message — when they’re thinking about buying or when they’re actually ready to purchase,” says Murphy.
It wasn’t easy at the start. “Being in brand marketing for so long, there was this initial pushback on data and the science behind it, because it was an art. But what we’re seeing now is the ability for data and insights to really inform the art of storytelling. It allows us to understand the impact that we’re having in real time,” says Kelly Olmstead, VP, Brand Activation for North America, adidas.
Today adidas’ marketing team depends more heavily on data and analysis to guide its strategy. “Starting every meeting with data and insights, that’s something that we want to take outside of just the marketing organization, and really infuse it across our culture here at adidas,” says Olmstead.
Watch how adidas benefits from using the Google Cloud Marketing Platform.
4370
Of your peers have already watched this video.
7:00 Minutes
The most insightful time you'll spend today!
Baking Gets Sweeter: Build ML Models that Help Predict the Best Recipe!
Baking recipes and ML models have one thing in common—they follow a pattern. Machine Learning is all about finding pattern in data sets, you can predict what you are baking based on the core ingredients and their respective amounts! Bread, cake or cookies, watch the video to make you make your baking experiences and learning with ML sweeter.
AutoML Tables, a no-code Google Cloud tool for ML models analyzes data from the databases and spreadsheets to help creates an automatic stats and dashboard with lists of ingredients and their values to predict a new recipe. Watch more episodes from Making with Machine Learning.
5144
Of your peers have already downloaded this article
7:30 Minutes
The most insightful time you'll spend today!
Sure, machine learning is becoming a business imperative, but how does it work in practice?
That’s the subject of a new step-by-step guide to solving business problems with artificial intelligence and ML, based on insights gathered by IDG Research Services.
Its publication comes at a time when technology leaders face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.
It has real-life examples such as a health services company that used ML to reduce support ticket-resolution time from 48 minutes to six.
In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.
The guide also includes concrete tips for new ML adopters, provided by the CIOs and other IT leaders who participated in IDG’s research. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.
Download the guide now!
Google and AI Researchers Work towards Building Data-centric AI
6096
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
AI researchers and engineers need better data to enable better AI solutions. The quality of an AI solution is determined by both the learning algorithm (such as a deep-neural network model) and the datasets used to train and evaluate that algorithm. Historically, AI research has focused much more on algorithms than datasets, despite their vital importance. As a result, many algorithms are freely available as starting points, but many important problems lack large, high-quality open datasets. Further, creating new datasets is expensive and error-prone.
Recently, the data-centric AI movement has emerged, which aims to develop new methodologies and tools for constructing better datasets to fix this problem. Conferences, workshops, challenges, and platforms are being launched to support improving data quality and to foster data excellence. Thought leaders such as Andrew Ng at Landing.AI and Chris Re at Stanford University are encouraging AI developers to focus more on iterative data engineering than they do tuning their learning algorithms. Our CHI-best-paper-award-winning paper, “Everyone wants to do the model work, not the data work” highlighted the significance of data quality in the practice of ML.
At Google, we are excited to contribute to data-centric AI. Today, Google Cloud is adding a new high value dataset to the Public Dataset Program, and Google researchers are announcing DataPerf, a new multi-organizational effort to develop benchmarks for data quality and data centric algorithms.
Google Cloud is committed to helping users improve their data quality, starting with supporting better public data. The Public Datasets program provides high quality datasets pre-configured on GCP for easy access. Google Cloud is adding a new high-value dataset developed by the MLCommons™ Association (which Google co-founded) to the Public Datasets program: The Multilingual Spoken Words Corpus: a rich audio speech dataset with more than 340,000 keywords in 50 languages with upwards of 23.4 million examples.
This new public dataset is aligned with the MLCommons Association vision for “open” datasets – accessible by all – that are “living” – continually being improved to raise quality and increase representation and diversity.
Google researchers, in collaboration with multiple organizations, are announcing the DataPerf effort at the NeurIPS Data-Centric AI workshop today, to develop benchmarks to improve data quality. Much like the the MLPerf™ benchmarking effort which is now the industry standard for machine learning hardware/software speed, DataPerf brings together the originators of prior efforts including: CATS4ML, Data-Centric AI Competition, DCBench, Dynabench, and the MLPerf benchmarks to define clear metrics that catalyze rapid innovation. DataPerf will measure the utility of training and test data for common problems, and algorithms for working with datasets such as: selecting core sets, correcting errors, identifying under-optimized data slices, and valuing datasets prior to labeling.
Together, supporting open, living datasets for core ML tasks, and the development of benchmarks to direct the rapid evolution of those datasets will empower the researchers and engineers who use Google Cloud to do even more amazing things – and we can’t wait to see what they create!
Acknowledgements: In collaboration with Lora Aroyo and Praveen Paritosh.
3142
Of your peers have already watched this video.
24:30 Minutes
The most insightful time you'll spend today!
How to Build a Global Marketing Data Hub
Data-driven marketing ensures that the right message is reaching the right customer at the right time through the right channel. However if an organization’s data resides in multiple systems and platforms it hampers its ability to deliver truly global data-driven campaigns, attribution analysis, and reporting.
See how Cloud Data Fusion with its growing plugins ecosystem can be leveraged to integrate multiple disparate data sources to build a comprehensive Marketing Data Warehouse. Learn how Cloud Data Fusion helped global CPG organizations build their Marketing DW.
Prateek Duble, Data Analytics Specialist, Google Cloud and Bhooshan Mogal, Product Manager, Cloud Data Fusion, Google Cloud, cover:
- How enterprises can build a robust global marketing data warehouse with Google Cloud including the business needs and technical challenge they encounter while building the holistic marketing data warehouses.
- How Cloud Data Fusion, Google Cloud Platform’s data ingestion and integration product, delivers critical capabilities to accelerate an enterprises’ journey to building marketing data warehouse.
- A reference architecture with Google Cloud products that shows how easily and quickly you can build marketing data warehouse in Google Cloud.
- A recent case study of a CPG business who built their marketing data warehouse with Google Cloud Platform.
This presentation also includes recent product launch announcements.
More Relevant Stories for Your Company
Lead Your Company Towards Data-Powered Innovation
In this talk, Lak Lakshmanan, Head of Analytics and AI Solutions, Google Cloud, presents a prescriptive, proven approach to foster the ability of your organization to innovate with data. This approach has helped other Google Cloud customers handle key challenges around data size, speed, and a (lack of) structure. This
ESG Report: Economic Advantages of Google BigQuery OnDemand Serverless Analytics
Traditional big data solutions require a significant upfront investment, ongoing maintenance of hardware and software, and manual provisioning to match compute and storage resources with demand. Google’s serverless analytics warehouse does away with all that extra work, helping businesses focus on what matters most: getting value from their data. Enterprise
AI and Machine Learning Get Marketers One Step Closer to Relevance at Scale
Artificial intelligence and machine learning are already transforming the technological landscape. From digital assistants to image-recognition software to self-driving cars, what was once the stuff of science fiction is now becoming a reality. But what exactly does it mean for marketing and advertising executives? It could get us closer to
IKEA’s AI-driven Personalized and Real-time Recommendations Up its Conversion Rates and Average Order Value
Background At IKEA we have multiple places in our customer journey in various channels where different kinds of personalization can deliver a superior customer experience. Product recommendations in the shopping basket, content recommendations in editorial sections, inspirational recommendations on product pages and more. After a while in the broader “recommendations”
