Google Cloud’s top AI blog posts from 2021
Google Cloud Content & Editorial
Artificial intelligence (AI) remained in the spotlight over the last year, as the gap continued to grow between organizations that merely possess data and those that can use data to leverage the power of AI to generate actionable insights or improve customer experiences.
At Google Cloud, helping you turn AI investments into real-world results is one of our foremost goals—and we kept our foot on the gas in 2021, launching a variety of new solutions, research, and tutorials. But don’t fret if you might have missed anything along the way. Whether you’re a seasoned data scientist or someone looking to solve problems with AI for the first time, Google Cloud offers platforms, tools, and best practices for all levels of expertise—and to close out the year, we’ve collected some of our top 2021 AI blog posts to help you kick off 2022 on the right foot.
Vertex AI: one platform for all your ML tools
Unveiled in May, Vertex AI was one of our most significant AI announcements of the year. A managed machine learning (ML) platform, Vertex AI supports your data teams more quickly building, deploying and maintaining ML models. Compared to competing platforms, it requires almost 80% fewer lines of code to train a model, helping your organization to implement Machine Learning Operations (MLOps) across all levels of expertise. Whether you’re newly adopting Vertex AI in 2022 or have been using it for months, here are a variety of articles to help you take full advantage of its powerful feature:
- What is Vertex AI? Developer advocates share more
- AI Simplified: Managing ML data sets with Vertex AI
- Use Vertex AI Pipelines to build an AutoML classification end-to-end workflow
- Build a reinforcement learning recommendation application using Vertex AI
- Vertex AI Matching Engine: Blazing fast and massively scalable nearest neighbor search
- PyTorch on Google Cloud: How To train and tune PyTorch models on Vertex AI
- Announcing Vertex AI Pipelines general availability
- Vertex AI NAS: higher accuracy and lower latency for complex ML models
- Coca-Cola Bottlers Japan collects insights from 700,000 vending machines with Vertex AI
- Google demonstrates leading performance in latest MLPerf Benchmarks
CCAI: reimagining customer experiences with the power of AI
Google Cloud’s Contact Center AI (CCAI) platforms make ML-powered language models more accessible and impactful by helping even companies with limited AI expertise to uncover insights in customer and partner interactions, and deploy virtual agents that can chat naturally with customers.
CCAI is built specifically to help call centers deliver excellent customer service on demand, even when human agents are unavailable, and we added powerful new features throughout the year, including Speaker ID, which lets customers authenticate themselves with just a few spoken words; Agent Assist, which provides human agents with continuous insight into customer intent during calls and chats; and CCAI Insights, which uses AI to mine raw contact center interactions for insights, regardless of whether the data originated with a virtual or human agent.
To learn more about how customers are leveraging CCAI, don’t miss our post about HSBC’s use of Dialogflow, a part of CCAI, to ease the call burden on its policy experts.
DocAI: unlocking the value in unstructured data
Our Document AI (DocAI) platform eliminates the guesswork and manual labor involved with document processing, helping your teams to better understand the data captured in documents and to streamline workflows. So your business can move even faster from implementation to value, we’ve introduced use case-specific additions to the platform, including Lending DocAI, Procurement DocAI, and Contract DocAI. To dive into how our customers are putting DocAI solutions to work, be sure to check out these articles:
How Mr. Cooper is using AI to increase speed and accuracy for mortgage processing
Going global: Workday uses Google Cloud AI to accelerate document processing
Industry solutions: smarter decision making, from retail to manufacturing
Delivering the right information to customers in the right context is crucial, so we’re pleased to see the great results that retail customers like IKEA and Bazaarvoice have enjoyed with our Recommendations AI solutions, achieving 30% and 60% increases in click-through rates, respectively. We published several blogs in 2021 to help you do more with this solution, including:
- Recommendations AI data ingestion
- Recommendations AI modeling
- Serving predictions & evaluating Recommendations AI
- How to get better retail recommendations with Recommendations AI
In addition to our use case-oriented DocAI solutions and our Recommendations AI work with retailers, we’ve also been digging into other specific industries and challenges, highlighted by the following:
Visual Inspection AI: a purpose-built solution for faster, more accurate quality control
New research reveals what’s needed for AI acceleration in manufacturing
Translation: customer connections without language barriers
Translation is one of the fastest growing AI use cases, and we rolled out a wide range of feature updates to help you connect with customers, highlighted by the ability to translate business documents across more than 100 languages. If faster translation workflows are among your 2022 resolutions, don’t miss our post about best practices for translating websites with Translation API or our article about how the city of San Jose uses AI translation to ensure critical services reach the community.
We’re just getting started: training and recognition
At Google Cloud, we see AI continuing to impact business and continuing to become easier to implement and leverage—so the preceding 2021 are just the tip of the iceberg. We’re looking forward to helping your organization do even more with AI in 2022, but in the meantime, here is a collection of blogs highlighting some of our additional research, recommendations, and plaudits, as well as training resources to help you do more with AI, faster:
