When it comes to the adoption of artificial intelligence (AI), we have reached a tipping point. Technologies that were once accessible to only a few are now broadly available. This has led to an explosion in AI investment. However, according to research firm McKinsey, for AI to make a sizable contribution to a company’s bottom line, they “must scale the technology across the organization, infusing it in core business processes” — and based on conversations with our customers, we couldn’t agree more.

While investments in pure data science continue to be essential for many, widespread adoption of AI increasingly involves a category of applications and services that we call AI agents. These are technologies that let customers apply the best of AI to common business challenges, with limited technical expertise required by employees, and include Google Cloud products like Document AI and Contact Center AI. Today, at Google Cloud Next ‘22, we’re announcing new features to our existing AI agents and a brand new one with Translation Hub.

“AI is becoming a key investment for many companies’ long term success. However, most companies are still in the experimental phases with AI and haven’t fully put the technology into production because of long deployment timelines, IT staffing needs, and more,” said Ritu Jyoti, group vice president, worldwide AI and automation research practice global AI research lead, at IDC. “Organizations need AI products that can be immediately applied to automate processes and solve business problems. Google Cloud is answering this problem by providing fully managed, scalable AI agents that can be deployed fast and deliver immediate results.”

Translation Hub: An enterprise-scale translation AI agent

At I/O this year, we announced the addition of 24 new languages to Google Translate to allow consumers in more locations, especially those whose languages aren’t represented in most technology, to help reduce communication barriers through the power of translation. Businesses strive for the same goals, but unfortunately it is often out of reach due to the high costs that come with scaling translation.

That’s why today, we are announcing Translation Hub, our AI agent that provides customers with self-service document translation. With 135 languages, Translation Hub can create impactful, inclusive, and cost-effective global communications in a few clicks.

With Translation Hub, now researchers are able to share their findings instantly across the world, goods and services providers can reach underserved markets, and public sector administrators can reach more members of their communities in a language they understand — all of which ultimately help make for a more connected, inclusive world.

Translation Hub brings together Google Cloud AI technology, like Neural Machine Translation and AutoML, to help make it easy to ingest and translate content from the most common enterprise document types, including Google Docs and Slides, PDFs, and Microsoft Word. It not only preserves layouts and formatting, but also provides granular management controls such as support for post-editing human-in-the-loop feedback and document review.

“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”

Document AI: A document processing AI agent to automate workflows

Every organization needs to process documents, understand their content, and make them available to the appropriate people. Whether it’s during procurement cycles involving invoices and receipts, contract processes to close deals, or for general increases in efficiency, Document AI simplifies and automates various document processing. With two new features launching today, Document AI can allow employees to focus on higher impact tasks and better serve their own customers.

For example, payments provider Libeo used Document AI to uptrain an invoice parser with 1,600 documents and increase its testing accuracy from 75.6% to 83.9%. “Thanks to uptraining, the Document AI results now beat the results of a competitor and will help Libeo save ~20% on the overall cost for model training over the long run,” said Libeo chief technology officer, Pierre-Antoine Glandier.

Today, we’re announcing these new features to our existing Document AI agent:

• Document AI Workbench can remove the barriers around building custom document parsers, helping organizations extract fields of interest that are specific to their business needs. Relative to more traditional development approaches, it requires less training data and offers a simple interface for both labeling data and one-click model training.
• Document AI Warehouse can eliminate the challenges that many enterprises face when tagging and extracting data in documents by bringing Google’s Search technologies to Document AI. This feature can make it simpler and easier to search for and manage documents like workflow controls to accommodate invoice processing, contracts, approvals, and custom workflows.

Contact Center AI: A contact center AI agent to improve customer experiences

Scaling call center support can be expensive and difficult, especially when implementing AI technologies to support representatives. Contact Center AI is an AI agent for virtually all contact center needs, from intelligently routing customers, to facilitating handoffs between virtual and human customer support representatives, to analyzing call center transcripts for trends and much more.

Just days ago, we announced that Contact Center AI Platform is now generally available to provide additional deployment choice and flexibility. With this addition to Contact Center AI, we are furthering our commitment to providing an AI agent that can assist organizations to quickly scale their contact centers to improve customer experiences and create value via data-driven decisions.

Dean Kontul, division chief information officer at KeyBank, had this to say about powering their contact center with Contact Center AI from Google Cloud: “With Google Cloud and Contact Center AI, we will quickly move our contact center to the Cloud, supporting both our customers and agents with industry-leading customer experience innovations, all while streamlining operations through more efficient customer care operations.”

Start delivering business results with AI agents, today!

If you’re ready to get started with Translation Hub, this Next ‘22 session has the details, including a deeper dive into Avery Dennison’s use of the AI agent.

To learn more about our Document AI announcements, check out our session with Commerzbank, “Improve document efficiency with AI,” as well as “Transform digital experiences with Google AI powered search and recommendations.”

And, to explore Contact Center AI Platform, watch “Delight customers in every interaction with Contact Center AI,” featuring more insight into KeyBank’s use case.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job. Also, end to end use cases require Spark to be used along with technologies like TensorFlow, and programming languages like SQL and Python. Today, these operate in silos, with Spark on unstructured data lakes, SQL on data warehouses, and TensorFlow in completely separate machine learning platforms. This increases costs, reduces agility, and makes governance extremely hard; prohibiting enterprises from making insights available to the right users at the right time.

Announcing Spark on Google Cloud, now serverless and integrated

We are excited to announce Spark on Google Cloud, bringing industry’s first autoscaling serverless Spark, seamlessly integrated with the best of Google Cloud and open source tools, so you can effortlessly power ETL, data science, and data analytics use cases at scale. Google Cloud has been running large scale business critical Spark workloads for enterprise customers for 6+ years, using open source Spark in Dataproc. Today, we are furthering our commitment by enabling customers to:

1. Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
2. Enable data users of all levels: Connect, analyze, and execute Spark jobs from the interface of users’ choice including BigQuery, Vertex AI or Dataplex, in 2 clicks, without any custom integrations.
3. Retain flexibility of consumption: No one size fits all. Use Spark as serverless, deploy on Google Kubernetes Engine (GKE), or on compute clusters based on the requirements.

With Spark on Google Cloud, we are providing a way for customers to use Spark in a cloud native manner (serverless), and seamlessly with tools used by data engineers, data analysts, and data scientists for their use cases. These tools will help customers on their way to realize the data platform redesign they have embarked on.

“Deutsche Bank is using Spark for a variety of different use cases. Migrating to GCP and adopting Serverless Spark for Dataproc allows us to optimize our resource utilization and reduce manual effort so our engineering teams can focus on delivering data products for our business instead of managing infrastructure. At the same time we can retain the existing code base and knowhow of our engineers, thus boosting adoption and making the migration a seamless experience.”—Balaji Maragalla, Director Big Data Platform, Deutsche Bank

“We see serverless Spark playing a central role in our data strategy. Serverless Spark will provide an efficient, seamless solution for teams that aren’t familiar with big data technology or don’t need to bother with idiosyncrasies of Spark to solve their own processing needs. We’re excited about the serverless aspect of the offering, as well as the seamless integration with BigQuery, Vertex AI, Dataplex and other data services.” —Saral Jain, Director of Engineering, Infrastructure and Data, Snap Inc.

Dataproc Serverless for Spark

Per IDC, developers spend 40% time writing code, and 60% of the time tuning infrastructure and managing clusters. Furthermore, not all Spark developers are infrastructure experts, resulting in higher costs and productivity impact. With serverless Spark, developers can spend all their time on the code and logic. They do not need to manage clusters or tune infrastructure. They submit Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job. Furthermore, while Spark users today pay for the time the infrastructure is running, with serverless Spark they only pay for the job duration.

Spark through BigQuery

BigQuery, the leading data warehouse, now provides a unified interface for data analysts to write SQL or PySpark. The code is executed using serverless Spark seamlessly, without the need for infrastructure provisioning. BigQuery has been the pioneer for serverless data warehousing, and now supports serverless Spark for Spark-based analytics.

Spark through Vertex AI

Data scientists no longer need to go through custom integrations to use Spark with their notebooks. Through Vertex AI Workbench, they can connect to Spark with a single click, and do interactive development. With Vertex AI, Spark can easily be used together with other ML frameworks like TensorFlow, Pytorch, Sci-kit learn, and BigQuery ML. All the Google Cloud security, compliance, and IAM are automatically applied across Vertex AI and Spark. Once you are ready to deploy the ML models, the notebook can be executed as a Spark job in Dataproc, and scheduled as part of Vertex AI Pipelines.

Spark through Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale. Now, you can use Spark on distributed data natively through Dataplex. Dataplex provides a collaborative analytics interface, with 1-click access to SparkSQL, Notebooks, or PySpark, and the ability to save, share, search notebooks and scripts alongside data.

Flexibility of consumption

We understand one size does not fit all. Spark is available for consumption in 3 different ways based on your specific needs. For customers standardizing on Kubernetes for infrastructure management, run Spark on Google Kubernetes Engine (GKE) to improve resource utilization and simplify infrastructure management. For customers looking for Hadoop style infrastructure management, run Spark on Google Compute Engine (GCE). For customers, who’re looking for no-ops Spark deployment, use serverless Spark! 

ESG Senior Analyst Mike Leone commented, “Google Cloud is making Spark easier to use and more accessible to a wide range of users through a single, integrated platform. The ability to run Spark in a serverless manner, and through BigQuery and Vertex AI will create significant productivity improvement for customers. Further, Google’s focus on security and governance makes this Spark portfolio useful to all enterprises as they continue migrating to the Cloud.”

Getting started

Dataproc Serverless for Spark will be Generally Available within a few weeks. BigQuery and Dataplex integration is in Private Preview. Vertex AI workbench is available in Public Preview, you can get started here. For all capabilities, you can request for Preview access through this form.

You can work with Google Cloud partners to get started as well.

“We are excited to partner with Google Cloud as we look to provide our joint customers with the latest innovations on Spark. We see Spark being used for a variety of analytics and ML use cases. Google is taking Spark a step further by making it serverless, and available through BigQuery, Vertex AI and Dataplex for a wide spectrum of users.” —Sharad Kumar, Cloud First data and AI Lead at Accenture

For more information, visit our website or the watch announcement video and our conversation with Snap at Next 2021.

We’re partnering with Coursera, one of the largest online learning platforms in the world, on a new ML Academy to help you sharpen your machine learning (ML) skills and learn about the latest ML technologies from Google Cloud at no-cost. The academy has three core components for you to take advantage of in July and August:

Join the ML Academy webinar to get started

Our July 22 webinar will kick off the ML Academy. During the webinar, Audrey Holmes, Senior Data Scientist at Coursera, will discuss the current market for individuals with ML skills. We will discuss key challenges ML practitioners are facing and how Google Cloud’s products and solutions are addressing these challenges. Doug Kelly, Google Cloud’s Head of AI Learning Services Portfolio, will discuss the best ways to gain ML skills and share a demo of how to train and serve a custom TensorFlow model on Vertex AI, Google Cloud’s machine learning model training and development platform. You’ll have an opportunity to ask questions throughout the webinar. 

After the webinar, you’ll receive an email with a one month free* offer from Coursera for the new Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate. Can’t attend the webinar live? You can watch the webinar on-demand after July 22 and the offer can be claimed until August 31, 2021. 

Keep growing your ML skills with Coursera

There are nine courses in the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate. The courses will cover ML fundamentals, introduction to TensorFlow, production ML systems, and more to help you prepare for the Google Cloud Professional Machine Learning Engineer certification exam. The courses will include labs so you can apply the concepts you’re learning and gain real world experience. 

Demonstrate your skills with badges 

To access even more labs and gain skill badges along the way to demonstrate your knowledge, sign up for the skills challenge. When you select the ML and AI skills challenge track, you’ll get 30 days free access to labs that will walk you through BigQuery, AI Platform, ML APIs, Explainable AI, and more. 

To earn a skill badge, you’ll need to complete the labs and take a final assessment to test your skills. You’ll have the chance to earn three skill badges through the challenge: Perform Foundational Data, ML, and AI tasks; Integrate with Machine Learning APIs; and Explore Machine Learning Models with Explainable AI. 

Ready to join the ML Academy? Sign up for the July 22 webinar here to get started. 

^{*This offer is only available for those who have never previously paid for Coursera. A credit card is required to activate your first month free. After the first month is over, your subscription will auto-renew to a $49 monthly charge until you cancel your Coursera subscription.}