3 Reasons Businesses Use Google Cloud Platform (GCP) for AI

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.

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Webinar: 6 Essential Tactics for your Data & Analytics Strategy

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

-Sam Tawfik, Sr Product Marketing Manager

Maximizing Cloud Data with Google Cloud Platform Services

If you’re trying to run your business smarter, not harder, utilizing data to gain insights into decision making gives you a competitive advantage. Cloud data offerings empower utilization of data in the cloud, and the Google Cloud Platform (GCP) is full of options. Whether you’re migrating data, upgrading to enterprise-class databases, or transforming customer experience on cloud-native databases – Google Cloud services can fit your needs.

Highlighting some of what Google has to offer

With so many data offerings from GCP, it’s nearly impossible to summarize them all. Some are open source projects being distributed by other vendors, while others were organically created by Google to service their own needs before being externalized to customers. A few of the most popular and widely used include the following.

  • BigQuery: Core to GCP, this serverless, scalable, multi-cloud, data warehouse enables business agility – including data manipulation and data transformation, and it is the engine for AI, machine learning (ML), and forecasting.
  • Cloud SQL: Traditional relational database in the cloud that reduces maintenance costs with fully managed services for MySQL, PostgreSQL, and SQL Server.
  • Spanner: Another fully managed relational database offering unlimited scale, consistency, and almost 100% availability – ideal for supply chain and inventory management across regions and between two databases.
  • Bigtable: Low latency, NoSQL, fully managed database for ML and forecasting, using very large amounts of data in analytical and operational workloads.
  • Data Fusion: Fully managed, cloud-native data integration tool that enables you to move different data sources to different targets – includes over 150 preconfigured connectors and transformers.
  • Firestore: From the Firebase world comes the next generation of Datastore. This cloud-native, NoSQL, document database lets you develop custom apps that directly connect to the database in real-time.
  • Cloud Storage: Object based storage can be considered a database because of all the things you can do with BigQuery – including using standard SQL language to query objects in storage.

Why BigQuery?

After more than 10 years of development, BigQuery has become a foundational data management tool for thousands of businesses. With a large ecosystem of integration partners and a powerful engine that shards queries across petabytes of data and delivers a response in seconds, there are many reasons BigQuery has stood the test of time. It’s more than just super speed, data availability, and insights.

Standard SQL language
If you know SQL, you know BigQuery. As a fully managed platform, it’s easy to learn and use. Simply populate the data and that’s it! You can also bring in large public datasets to experiment and further learn within the platform.

Front-end data
If you don’t have Looker, Tableau, or another type of business intelligence (BI) tool to visualize dashboards off of BigQuery, you can use the software development kit (SDK) for web-based front-end data display. For example, government health agencies can show the public real-time COVID-19 case numbers as they’re being reported. The ecosystem of BigQuery is so broad that it’s a source of truth for your reports, dashboards, and external data representations.

Analogous across offerings

Coming from on-prem, you may be pulling data into multiple platforms – BigQuery being one of them. GCP offerings have a similar interface and easy navigation, so functionality, user experience, and even endpoint verbs are the same. Easily manage different types of data based on the platforms and tools that deliver the most value.

BigQuery Omni

One of the latest GCP services was built with a similar API and platform console to various other platforms. The compatibility enables you to query data living in other places using standard SQL. With BigQuery Omni, you can connect and combine data from outside GCP without having to learn a new language.

Ready for the next step in your cloud journey?

As a Google Cloud Partner, 2nd Watch is here to be your trusted cloud advisor throughout your cloud data journey, empowering you to fuel business growth while reducing cloud complexity. Whether you’re embracing cloud data for the first time or finding new opportunities and solutions with AI, ML, and data science our team of data scientists can help. Contact Us for a targeted consultation and explore our full suite of advanced capabilities.

Learn more

Webinar: 6 Essential Tactics for your Data & Analytics Strategy

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

-Sam Tawfik, Sr Product Marketing Manager

AWS re:Invent Keynote Recap – Wednesday

I have been looking forward to Andy Jassy’s keynote since I arrived in Las Vegas. Like the rest of the nearly 50k cloud-geeks in attendance, I couldn’t wait to learn about all of the cool new services and feature enhancements that will be unleashed that can solve problems for our clients, or inspire us to challenge convention in new ways.

Ok, I’ll admit it. I also look forward to the drama of the now obligatory jabs at Oracle, too!

Andy’s 2017 keynote was no exception to the legacy of previous re:Invents on those counts, but my takeaway from this year is that AWS has been able to parlay their flywheel momentum of growth in IaaS to build a wide range of higher-level managed services. The thrill I once got from new EC2 instance type releases has given way to my excitement for Lambda and event-based computing, edge computing and IoT, and of course AI/ML!

AWS Knows AI/ML

Of all the topics covered in the keynote, the theme that continues to resonate throughout this conference for me is that AWS wants people to know that they are the leader in AI and machine learning. As an attendee, I received an online survey from Amazon prior to the conference asking for my opinion on AWS’s position as a leader in the AI/ML space. While I have no doubts that Amazon has unmatched compute and storage capacity, and certainly has access to a wealth of information to train models, how does one actually measure a cloud provider’s AI/ML competency? Am I even qualified to answer without an advanced math degree?

That survey sure makes a lot more sense to me following the keynote as I now have a better idea of what “heavy lifting” a cloud provider can offload from the traditional process.

Amazon has introduced SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. It integrates with S3, and with RDS, DynamoDB, and Redshift by way of AWS Glue. It provides managed Jupyter notebooks and even comes supercharged with several common ML algorithms that have been tuned for “10x” performance!

In addition to SageMaker, we were introduced to Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to analyze text. I personally am excited to integrate this into future chatbot projects, but the applications I see for this service are numerous.

After you’ve built and trained your models, you can run them in the cloud, or with the help of AWS Greengrass and its new machine learning inference feature, you can bring those beauties to the edge!

What is a practical application for running ML inference at the edge you might ask?

Dr. Matt Wood demoed a new hardware device called DeepLens for the audience that does just that! DeepLens is a deep-learning enabled wireless video camera specifically designed to help developers of all skill levels grow their machine learning skills through hands-on computer vision tutorials. Not only is this an incredibly cool device to get to hack around with, but it signals Amazon’s dedication to raising the bar when it comes to AI and machine learning by focusing on the wet-ware: hungry minds looking to take their first steps.

Andy’s keynote included much more than just AI/ML, but to me, the latest AI/ML services that were announced on Tuesday represent the signal of Amazon’s future of higher-level services which will keep them the dominant cloud provider into the future.

 

–Joe Conlin, Solutions Architect, 2nd Watch