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Google Cloud Platform (GCP) offers a wide scope of artificial intelligence (AI) and machine learning (ML) services fit for a range of industries and use cases. With more businesses turning to AI for data-based innovation and new solutions, GCP services are proving effective. See why so many organizations are choosing Google Cloud to motivate, manage, and make change easy.

1. Experimentation and cost savings

Critical to the success of AI and ML models are data scientists. The more you enable, empower, and support your data scientists through the AI lifecycle, the more accurate and reliable your models will be. Key to any successful new strategy is flexibility and cost management. Oneway GCP reduces costs while offering enterprise flexibility is with Google’s AI Platform Notebooks.

Managed JuptyerLab notebook instances give data scientists functional flexibility – including access to BigQuery, with the ability to add CPUs, RAM, and GPUs to scale – cloud security, and data access with a streamlined experience from data to deployment. Relying on on-prem environments, data scientists are limited by resource availability and a variety of costs related data warehousing infrastructure, hosting, security, storage, and other expenses. JuptyerLab notebooks and Big Query, on the other hand, are pay as you go and always available via the AI Platform Notebooks. With cost-effective experimentation, you avoid over provisioning, only pay for what you use and when you run, and give data scientists powerful tools to get data solutions fast.

2. Access and applications

AI and ML projects are only possible after unifying data. A common challenge to accomplishing this first step are data silos across the organization. These pockets of disjointed data across departments threaten the reliability and business outcomes of data-based decision making. The GCP platform is built on a foundation of integration and collaboration, giving teams the necessary tools and expansive services to gain new data insights for greater impacts.

For instance, GCP enables more than just data scientists to take advantage of their AI services, databases, and tools. Developers without data science experience can utilize APIs to incorporate ML into the solution without ever needing to build a model. Even others, who don’t have knowledge around data science, can create custom models that integrate into applications and websites using Cloud AutoML.

Additionally, BigQuery Omni, a new service from GCP, enables compatibility across platforms. BigQuery Omni enables you to query data residing in other places using standard SQL with the powerful engine of BigQuery. This innovation furthers your ability to join data quickly and without additional expertise for unobstructed applicability.

3. ML Training and labs

Google enables users with best practices for cost-efficiency and performance. Through its Quiklabs platform, you get free, temporary access to GCP and AWS, to learn the cloud on the real thing, rather than simulations. Google also offers training courses ranging from 30-minute individual sessions, to multi-day sessions. The courses are built for introductory users, all the way up to expert level, and are instructor-led or self-paced. Thousands of topics are covered, including AI and ML, security, infrastructure, app dev, and many more.

With educational resources at their fingertips, data teams can roll up their sleeves, dive in, and find some sample data sets and labs, and experience the potential of GCP hands-on. Having the ability to experiment with labs without running up a bill – because it is in a sandbox environment – makes the actual implementation, training, and verification process faster, easier, and cost-effective. There is no danger of accidentally leaving a BigQuery system up and running, executing over and over, with a huge cost to the business.

Next steps

If you’re contemplating AL and ML on Google Cloud Platform, get started with Quiklabs to see what’s possible. Whether you’re the one cheerleading AI and ML in your organization or the one everyone is seeking buy-in from, Quiklabs can help. See what’s possible on the platform before going full force on a strategy. Google is constantly adding new services and tools, so partner with experts you can trust to achieve the business transformation you’re expecting.

Contact 2nd Watch, a Google Cloud Partner with over 10 years of cloud experience, to discuss your use cases, level of complexity, and our advanced suite of capabilities with a cloud advisor.

Learn more

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Webinar:  Building an ML foundation for Google BigQuery ML & Looker

-Sam Tawfik, Sr Product Marketing Manager

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