“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation and efficient operational growth.

Whether they’re trying to extract insights from data, create faster and more efficient workflows via intelligent automation, or build innovative customer experiences, leaders at today’s tech companies and startups know that proficiency in AI and ML is more important than ever.

AI and ML technologies are often expensive and time-consuming to develop, and the demand for AI and ML experts still largely outpaces the existing talent pool. These factors put pressure on tech companies and startups to allocate resources carefully when considering bringing AI/ML into their business strategy. In this article, we’ll explore four tips to help tech companies and startups accelerate innovation and reduce costs with AI and ML.

4 tips to accelerate innovation and reduces costs with AI and ML

Many of today’s most innovative companies are creating services or products that couldn’t exist without AI—but that doesn’t mean they’re building their AI and ML infrastructure and pipelines from scratch. Even for startups whose businesses don’t directly revolve around AI, injecting AI into operational processes can help manage costs as the company grows. By relying on a cloud provider for AI services, organizations can unlock opportunities to energize development, automate processes, and reduce costs.

1. Leverage pre-trained ML APIs to jumpstart product development

Tech companies and startups want their technical talent focused on proprietary projects that will make a difference to the business. This often involves the development of new applications for an AI technology, but not necessarily the development of the AI technology itself. In such scenarios, pre-trained APIs help organizations quickly and cost-effectively establish a foundation on which higher-value, more differentiated work can be layered.

For example, many companies building conversational AI into their products and services leverage Google Cloud APIs such as Speech-to-Text and Natural Language. With these APIs, developers can easily integrate capabilities like transcription, sentiment analysis, content classification, profanity filtering, speaker diarization, and more. These powerful technologies help organizations focus on creating products rather than having to build the base technologies.

See this article for examples of why tech companies and startups have chosen Google Cloud’s Speech APIs for use cases that range from deriving customer insights to giving robots empathetic personalities. For an even deeper dive, see

• our AI product page to explore other APIs, including Translation, Vision, and more;
• and the Google Cloud Skills Boost for ML APIs.

2. Use managed services to scale ML development and accelerate deployment of models to production

Pre-trained models are extremely useful, but in many cases, tech companies and startups need to create custom models to either derive insights from their own data or to apply new use cases to public data. Regardless of whether they’re building data-driven products or generating forecasting models from customer data, companies need ways to accelerate the building and deployment of models into their production environments.

A data scientist typically starts a new ML project in a notebook, experimenting with data stored on the local machine. Moving these efforts into a production environment requires additional tooling and resources, including more complicated infrastructure management. This is one reason many organizations struggle to bring models into production and burn through time and resources without moving the revenue needle.

Managed cloud platforms can help organizations transition from projects to automated experimentation at scale or the routine deployment and retraining of production models. Strong platforms offer flexible frameworks, fewer lines of code required for model training, unified environments across tools and datasets, and user-friendly infrastructure management and deployment pipelines.

At Google Cloud, we’ve seen customers with these needs embrace Vertex AI, our platform for accelerating ML development, in increasing numbers since it launched last year. Accelerating time to production by up to 80% compared to competing approaches, Vertex AI provides advanced end-to-end ML Ops capabilities so that data scientists, ML engineers, and developers can contribute to ML acceleration. It includes low-code features, like AutoML, that make it possible to train high performing models without ML expertise.

Over the first half of 2022, our performance tests found that the number of customers utilizing AI Workbench increased by 25x. It’s exciting to see the impact and value customers are gaining with Vertex AI Workbench, including seeing it help companies speed up large model training jobs by 10x and helping data science teams improve modeling precision from the 70-80% range to 98%.

If you are new to Vertex AI, check out this video series to learn how to take models from prototype to production. For deeper dives, see

• this article about Vertex AI’s role in an ambitious project to measure climate change with AI;

• BigQuery has built-in Machine Learning (ML) and Analytics that you can use to create no-code predictions using just SQL queries.
• this blog about how Vertex AI and BigQuery work together to make data analysis easier and more powerful;
• and this blog about Example-based explanations, one of our most recent updates to make model iteration more intuitive and efficient.

3. Harness the cloud to match hardware to use cases while minimizing costs and management overhead

ML infrastructure is generally expensive to build, and depending on the use case, specific hardware requirements and software integrations can make projects costly and complicated at scale. To solve for this, many tech companies and startups look to cloud services for compute and storage needs, attracted by the ability to pay only for resources they use while scaling up and down according to changing business needs.

At Google Cloud, customers share that they need the ability to optimize around a variety of infrastructure approaches for diverse ML workloads. Some use Central Processing Units (CPUs) for flexible prototyping. Others leverage our support for NVIDIA Graphics Processing Units (GPUs) for image-oriented projects and larger models, especially those with custom TensorFlow operations that must run partially on CPUs. Some choose to run on the same custom ML processors that power Google applications—Tensor Processing Units (TPUs). And many use different combinations of all of the preceding.

Beyond matching use cases to the right hardware and benefiting from the scale and operational simplicity of a managed service, tech companies and startups should explore configuration features that help further control costs. For example, Google Cloud features like time-sharing and multi-instance capabilities for GPUs — as well as features like Vertex AI Training Reduction Server — are built to optimize GPU costs and usage.

Vertex AI Workbench also integrates with the NVIDIA NGC catalog for deploying frameworks, software development kits and Jupyter Notebooks with a single click—another feature that, like Reduction Server, speaks to the ways organizations can make AI more efficient and less costly via managed services.

4. Implement AI for operations

Besides using pre-trained APIs and ML model development to develop and deliver products, startup and tech companies can improve operational efficiency, especially as they scale, by leveraging AI solutions built for specific business and operational needs, like contract processing or customer service.

Google Cloud’s DocumentAI products, for instance, apply ML to text for use cases ranging from contract lifecycle management to mortgage processing. For businesses whose customer support needs are growing, there’s Contact Center AI, which helps organizations build intelligent virtual agents, facilitate handoffs as appropriate between virtual agents and human agents, and generate insights from call center interactions. By leveraging AI to help manage operational processes, startups and tech companies can allocate more resources to innovation and growth.

Next steps toward an intelligent future

The tips in this article can help any tech company or startup find ways to save money and boost efficiency with AI and ML. You can learn more about these topics by registering for Google Cloud Next, kicking off October 11, where you’ll hear Google Cloud’s latest AI news, discussions, and perspectives—in the meantime, you can also dive into our Vertex AI quickstarts and BigQuery ML tutorials. And for the latest on our work with tech companies and startups, be sure to visit our Startups page.

Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery

In the field of producing engaging video content such as ads, many marketers ignore the power of data to improve their creative efforts to meet the consumers’ need for personalized messages. The demand for creative tech to efficiently personalize is real as marketers need personalized video Ads to reach their audience with the right message at the right time. Data, Insights and Technology are the main ingredients to deliver this value while ensuring security and privacy requirements are met. The Connected-Stories team partnered with Google Cloud to build a platform for Ad personalization. Google Data Cloud and BigQuery are at the forefront to assimilate data, leverage ML models, create personalized ads, and capitalize on real-time intelligence as the core features of the Connected-Stories NEXT platform.

Connected-Stories NEXT is an end-to-end creative management platform to develop, serve, and optimize interactive video and display ads that scale across any channel. The platform ingests first-party data to create custom ML models, measure numerous third-party data points to help brands develop unique customer journeys and create videos that their data signals can drive. An intelligent feedback loop passes real-time data back, enabling brands to make data-driven and actionable video ads that take the brand’s campaigns to the next level.

The core use case of the NEXT platform revolves around collecting user’s interaction data and optimizing for precision and speed to create an actionable Ad experience that is personalized for each user. The platform processes complex data points to create interactive data visualizations that allow for accurate analysis. The platform uses Vertex AI to access managed tools, workflows, and infrastructure to build, deploy, and scale ML models that have improved the accuracy to identify segments for further analysis.

The platform ingests 200M data events with peaks and valleys of activity. These events are processed to generate dashboards that enable users to visualize metrics based on filters in real-time. These dashboards have high performance requirements in terms of a responsive user interface under constantly changing data dimensions.

Google Cloud’s serverless stack coupled with limitless data cloud infrastructure has been the core to the NEXT platform’s data-driven innovation. The growing volume of data ingested, streamed and processed were scaled uniformly across the compute, storage and analytical layers of solution. A lean development team at Connected-Stories were able to focus all-in on the solution, while the serverless stack scaled, lowered attack service in terms of security and optimized the cost footprint through pay-as-you-go features.

BigQuery has been the backbone to support the vast amounts of data spreading over multiple geos resulting in workloads running at petabyte scale. BigQuery’s fully managed serverless architecture, real-time streaming, built-in machine learning and rich business intelligence capabilities distinguishes itself from a cloud data warehouse. It is the foundation needed to approach data and serve users in an unlimited number of ways. For an application with zero tolerance for failure, given its fully managed nature, BigQuery handles replication, recovery, data distributed optimization and management.

The platform’s requirements include the need for low maintenance, constantly ingesting and refreshing data and smart-tuning of aggregated data. These capabilities can be implemented by BigQuery’s materialized views feature. Materialized views are useful for precomputed views that regularly cache query results for better performance. These views possess the innate feature to read only the delta change from base tables and calculate the up-to-date aggregations. Materialized views impart faster outputs and consume fewer resources while reducing the cost footprint.

Some key considerations in using Google cloud and focusing on the Serverless stack include: quick onboarding to development, prototyping in short sprints and ease of preparing data in a rapidly changing environment. Typical considerations around low code / no code include data transformation, aggregation and reduced deployment time. These considerations are fulfilled through using serverless capabilities within Google Cloud such as PubSub, Cloud Storage, Cloud Run, Cloud Composer, Dataflow and BigQuery as described in the Architecture diagram below. The use of each of these components and services are described below.

1. Input/Ingest: At a high-level, microservices hosted in Cloud Run collect and aggregate incoming Ads events.
2. Enrichment: The output of this stage is a Pub-Sub message enriched with more attributes based on a pre-configured campaign.
3. Store: a Cloud Dataflow streaming job to create text files in Cloud Storage buckets.
4. Trigger: Cloud Composer triggers the spark jobs based on text files to process and group them to produce desired output as one record per impression, a logical group of events.
5. Deploy: Cloud Build is then used to automate all deployments.

Thus far, all Google cloud managed services work together to ingest, store and trigger the orchestration, all of which are scalable based on configurations including autoscaling capabilities.

1. Visualization: A visualization tool reads data from BigQuery to compute pre-aggregations required for each dashboard.
2. Data Model Evolution considerations: Though the solution served the purpose of creating pre-aggregations, as the data model evolved by adding a column or creating a new table, it led to recreating pre-aggregations and querying the data again. Alternatively, creating aggregate tables as an extra output of current ETLs seemed like a viable option. However, this would increase the cost and complexity of jobs. A similar situation to reprocess or update aggregated tables would occur as data is updated.

Precomputed views of data that is periodically cached are critical to reach the audience with the right message at the right time.

1. Performance: In order to increase the performance of the platform, we need to have regularly precomputed views of the data, cached .
2. Materialized Views: Consumers of these views needed faster response times, to consume fewer resources and output only the changes in comparison to a base table. BigQuery Materialized views were used to solve this very requirement. Materialized views have been highly leveraged to optimize the design resulting in lesser maintenance and access to fresh data with high performance with a relatively low technical investment in creating and maintaining SQL code.
3. Dashboards: Application dashboards pointing to the Materialized views are highly performant and provide a view into fresh data.
4. Custom Reports with Vertex AI Notebooks: Vertex AI notebooks directly read data from BigQuery to produce custom reports for a subset of customers. Vertex AI has been hugely beneficial to data analysts, where an environment with pre-installed libraries simplifies the readiness to use. Vertex AI Workbench notebooks are used to share these reports within the team allowing them to work always on the cloud without having the need to download data at any time. Besides, it increases the velocity to develop and test ML models faster.

The NEXT platform has yielded benefits such as customers having the ability to create unique consumer journeys powered by AI / ML personalization triggers, using first-party data and business intelligence tools to capitalize on real-time creative intelligence, which is a dashboard to measure campaign performance for cross-functional teams to analyze the impact of Ad content experience at a granular level. All of these while ensuring controlled access to data to enrich data without moving across clouds. The NEXT platform can keep up with increased demands for agility, scalability and reliability through the underlying usage of Google Cloud.

Partnering with Google, in the context of the Google Built with BigQuery program has surfaced the differentiated value in areas of creating interactive personalized Ads by using real-time data. In addition, by sharing this data across organizations as assets, ML models have fueled higher levels of innovation. Connected-Stories plan to deepen the penetration into the entire spectrum of services offered in the AI/ML area to enhance core functionality and provide newer capabilities to the platform.

Click here to learn more about Connected-Stories NEXT Platform capabilities.

The Built with BigQuery Advantage for ISVs

Through Built with BigQuery, launched in April ‘22 as part of Google Data Cloud Summit, Google is helping tech companies like Connected-Stories co-innovate in building applications that leverage Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:

• Get started fast with a Google-funded, pre-configured sandbox.
• Accelerate product design and architecture through access to designated technical experts from the ISV Center of Excellence who can share insights from key use cases, architectural patterns, and best practices encountered in the field.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

The Google Data Cloud spectrum of products and specifically BigQuery give ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge and expanding partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in and exercise choice.

We thank the Google Cloud and Connected-Stories team members who co-authored the blog: Connected-Stories: Luna Catini, Marketing Director, Google: Sujit Khasnis, Cloud Partner Engineering

Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.

Today, we’re excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.

Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.

Customers using Agent Assist for Chat have been able to manage up to 28% more conversations concurrently, while also driving up customer satisfaction by 10%. Additionally, we’ve seen them respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.

Agent Assist provides two key components to help agents manage conversations better: 

• Smart Reply provides response suggestions to agents so they can quickly and appropriately respond to customer messages. These suggestions can be taken from your top performing agents as well as modified even further to ensure suggestions properly reflect the tone and voice of your brand. Agent Assist learns when and what recommendations to make by building a custom model that’s trained on your (and only your) data.
• Knowledge Assist unlocks the power of your knowledge base to provide articles and FAQ suggestions to agents in real-time as the conversation progresses. When using Knowledge Assist, agents no longer need to make the customer wait while they navigate multiple applications and data to find the resolution to the customer’s issue — the answer is delivered right to them.  

“We’ve been very impressed by the chat capabilities of Agent Assist,” said Chris Smith, Vice President of Digital Service at Optus, one of the largest telecommunications companies in Australia

Optus has been using CCAI Dialogflow CX to send queries to virtual agents and sees great potential to use Agent Assist to provide recommendations to their customer support representatives. They expect Agent Assist to help minimize repetitive tasks by providing response and typeahead suggestions, helping improve the efficiency of their agents and the quality and consistency of service they provide.

Another customer, LoveHolidays, is using Agent Assist to support their agents and customers in the travel industry. 

“Agent Assist has been a beneficial aid to agents and our customers alike… It gives us the power to flex our contact center staff levels in hours not weeks,” said Eugene Neale, Director of CX Engineering & Business IT at LoveHolidays

Analysts say online chat is becoming one of the most popular ways to reach out to businesses for customer support. IDC research finds that single-function contact centers worldwide are increasingly rare — in 2020, although phone/voice is still responsible for most interactions (at around 18%); email is responsible for around 13% of interactions, and live chat (without automation) is responsible for around 8% of interactions, according to IDC, Toward the AI-Powered Contact Center, Doc # EUR147017320, December 2020.

Deploying CCAI with Agent Assist for Chat

As part of Google’s Contact Center AI suite, Agent Assist provides a seamless handoff from chats managed by your Dialogflow CX virtual agents. If a conversation or customer requires a live agent, Agent Assist will help your team pick it up quickly and drive it to a satisfying resolution. 

Historically, when managers saw contact center volumes increase they had two choices: allow customers to wait longer to speak to someone (lowering customer satisfaction) or bring on more agents (increasing cost to serve).  Deploying CCAI provides contact center leaders with a third choice: equip agents with tools like, Agent Assist for Chat, to efficiently manage customer interactions while maintaining high quality service.

Global CCAI partners support Agent Assist for Chat

Agent Assist for Chat is a set of public APIs that your engineering team can integrate directly into an agent desktop to control the agent experience from end-to-end. For a more out-of-the-box solution, we have partnered with LivePerson and 247.ai to build Agent Assist directly into their agent desktops.

“Integrating our Conversational Cloud directly with Agent Assist means agents can leverage cutting-edge productivity AI to build even further on the massive ROI of conversational commerce, from reduced agent effort and time-to-respond to increased customer satisfaction and revenue,” said Alex Spinelli, CTO of LivePerson. 

More Agent Assist resources

To learn more, check out the Agent Assist webpage. Give Agent Assist a try by training a model and then testing it using the Agent Assist simulator.

It’s no secret that many organizations and job seekers find the hiring process exhausting. It can be time consuming, costly, and somewhat risky for both parties. Those are just some of the experiences we wanted to change when we started Gyfted, a pre-vetted talent marketplace for people who complete tech training or degree programs and are looking for the right career move. At the same time, we’re helping businesses save time and improve recruiting outcomes with our automated candidate screening and sourcing tools.

Our vision is clear: To take a candidate through one structured hiring process, and then put them in front of thousands of companies. It’s similar to the common app system in higher education. Sounds simple, but it is a herculean technical and UX task. To succeed we had to combine advanced psychometric testing, machine learning, the latest in behavioral design, and develop the highest quality structured, relational dataset to represent candidate and manager profiles and preferences on our network. Fortunately, we were cofounded by world-leading experts in these areas including Dr. Michal Kosinski, one of the world’s top computational psychologists, and Adam Szefer, a gifted young technologist. We’ve been joined by a group of equally talented employees, most of whom work remotely in Poland, US, Switzerland, UK, Israel, and Ukraine.

The influence of dating platforms and matching

When seeking inspiration, we were influenced by the success of dating platforms, especially Bumble with its focus on commitment. These platforms have done a great job using design to match people together.

We like to think we’re doing the same for recruiters and candidates in terms of not only role and culture fit matching, but also through a fundamental feature of Gyfted, which is that our job-seekers are anonymous. This helps recruiters meet one of their goals today, which is to minimize bias in the hiring process and enable objective, diversity-oriented recruiting.

Another of our unique selling points is that we conduct candidate screening that is gamified and automated, with a structured interview, where the interview remains with the candidate’s profile. We roughly estimate that just for the 15 million open jobs on LinkedIn, if companies fill in 50% of those via external recruiting, and conduct a screening interview with 10 candidates per job filled at $50/hour paid to an employee to do the screening, that’s $3.75 billion and 75 million hours in direct costs. This is on top of applying for jobs, selecting CVs, and coordinating the process, which takes an even bigger financial and time toll on both applicants and recruiters. Instead, it would be better to take one interview for 1000 companies. The impact of what we want to achieve with our vision is enormous.

We also offer career discovery and career search tools for job candidates. This includes free, personalized feedback for every job-seeker. Right now, we’re aiming the service at students, bootcamp graduates and juniors, helping them to land jobs in tech and the creative industry at large. Next, we’ll expand into mid and senior roles. In the long run we want to reshape how recruiting happens through a common app that saves everyone in the market significant time and resources, helping people find jobs not only faster, but jobs that truly fit them.

Developing advanced AI applications with Google Cloud

We obtained our original funding from angel and institutional investors, and we were selected into the Fall 2021 batch of StartX, the non-profit start-up accelerator and founder community associated with Stanford University. But like most startups our budgets are tight, and we need to find ways to operate as efficiently as possible, especially when building out our technology stack and developer environment.

That’s where Google Cloud comes in. It’s a lot more affordable and flexible than competing solutions, and our developers love it. We use Google App Engine for the hosting and development of our applications giving us enormous flexibility. Vertex AI enables us to build, deploy, and scale machine learning models faster, within a unified artificial intelligence platform. On top of that we use Google Vertex AI Workbench as the development environment for the data science workflow, which allows us to have everything that we need to host and develop innovative AI-based applications.

BigQuery, Google Cloud’s serverless data warehouse, is another stand-out solution for us. We use it to crunch big data from all our systems and the UX is very intuitive and easy to use, allowing us to use it across the business and get insights from a wide range of employees, not just technical experts.

Above all, Google Cloud helps us solve the main platform challenges facing Gyfted including scalability and identity management, so we are perfectly positioned for growth. Right now, we handle about 2 million candidate interactions, a volume we expect to grow exponentially. As that number grows, we rely on Google Cloud to help us scale securely and with reliability.

Eliminating bias from the hiring process

Our technology partners have also been integral to helping us get to an advanced stage of our beta program. MongoDB on Google Cloud takes the data burden off our teams and reduces time to value of our applications. We can stay nimble and can scale database capacity at the push of a button.

Our collaboration with the Google team has been fantastic. Our Startup Success Manager is an expert when it comes to Google Cloud solutions, and he also understands our business from his own experience as an entrepreneur and an investor. It’s great to have an internal point of contact who can help us navigate all of Google’s resources.

I’d also stress the extent to which Google Cloud values align with ours. For example, a key benefit for our customers is the ability to strip unconscious bias out of the hiring process. Google Cloud tools support this commitment to diversity, especially when we are building out our AI models.

On a team level, we also appreciate the support that Google Cloud has shown through its Google Support Fund for Start-ups in Ukraine. This has helped many Ukrainian businesses to continue to operate at a very challenging time, including startups with remote, distributed teams in Poland where most of us stem from.

If I had to sum up Google Cloud and our collaboration with Google for Startups in a phrase, I’d say that it adds enormous value to our business while removing much of the risk when scaling up a start-up. We’ve seen the addition of many new tools and features in the past two years and our Google mentors are always looking at the best way these can be integrated with Gyfted’s own roadmap. That means that we can continue to transform recruiting and hiring processes with the support of one of the world’s most advanced tech companies as a strategic growth partner.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.