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.

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

3 Types of Employees That Can Use AI Offerings on Google Cloud

The Google Cloud Platform (GCP) comes with a number of services, databases, and tools to operationalize company-wide data management and analytics. With the insights and accessibility provided, you can leverage data into artificial intelligence (AI) and machine learning (ML) projects cost-efficiently. GCP empowers employees to apply their ideas and experience into data-based solutions and innovation for business growth. Here’s how.

1. Developers without Data Science Experience

With GCP, developers can connect their software engineering experience with AI capabilities to produce powerful results. Using product APIs, developers can incorporate ML into the product without ever having to build a model.

Let’s take training videos for example – Your company has thousands of training videos varying in length and across subjects. They include everything from full-day trainings on BigQuery, to minutes-long security trainings. How do you operationalize all that information for employees to quickly find exactly what they want?

Using Google’s Cloud Video Intelligence API, the developer can transcribe not only every single video, word-for-word, but also document the start and end time of every word, in every video. The developer builds a search index on top of the API, and just like that, users can search specific content in thousands of videos. Results display both the relevant videos and timestamps within the videos, where the keyword is found. Now employees can immediately find the topic they want to learn more about, without needing to sift through what could be hours of unrelated information.

Additional APIs include, Cloud Natural Language, Speech-to-Text, Text-to-Speech, Cloud Data Loss Prevention, and many others in ML.

2. Everyone without Data Science Experience, who isn’t a Developer

Cloud AutoML enables your less technical employees to harness the power of machine learning. It bridges the gap between the API and building your own ML model. Using AutoML, anyone can create custom models tailored to your business needs, and then integrate those models into applications and websites.

For this example, let’s say you’re a global organization who needs to translate communications across dialects and business domains. The intricacies and complexities of natural language require expensive linguists and specialist translators with domain-specific expertise. How do you communicate in real time effectively, respectfully, and cost-efficiently?

With AutoML Translation, almost anyone can create translation models that return query results specific to your domain, in 50 different language pairs. It graphically ingests your data from any type of Sheet or CSV file. The input data necessary is pairs of sentences that mean the same thing in both the language you want to translate from, and the one you want to translate to. Google goes the extra mile between generic translation and specific, niche vocabularies with an added layer of specificity to help the model get the right translation for domain-specific material. Within an hour, the model translates based on your domain, taxonomy, and the data you provided.

Cloud AutoML is available for platform, sight, structured data, and additional language capabilities.

3. Data Scientists

Data scientists have the experience and data knowledge to take full advantage of GCP AI tools for ML. One of the issues data scientists often confront is notebook functionality and accessibility. Whether its TensorFlow, PyTorch, or JupyterLab, these open source ML platforms require too many resources to run on a local computer, or easily connect to BigQuery.

Google AI Platform Notebooks is a managed service that provides a pre-configured environment to support these popular data science libraries. From a security standpoint, AI Platform Notebooks is attractive to enterprises for the added security of the cloud. Relying on a local device, you run the risk of human error, theft, and fatal accidents. Equipped with a hosted, integrated, secure, and protected JupyterLab environment, data scientists can do the following:

  • Virtualize in the cloud
  • Connect to GCP tools and services, including BigQuery
  • Develop new models
  • Access existing models
  • Customize instances
  • Use Git / GitHub
  • Add CPUs, RAM, and GPUs to scale
  • Deploy models into production
  • Backup machines

With a seamless experience from data to a deployed ML model, data scientists are empowered to work faster, smarter, and safer. Contact Us to further your organization’s ability to maximize data, AI, and ML.

Here are a few resources for those who wish to learn more about this subject:

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

5 Best Practices for Managing the Complexities of a Hybrid Cloud Strategy

Hybrid cloud strategies require a fair amount of effort and knowledge to construct, including for infrastructure, orchestration, application, data migration, IT management, and potential issues related to silos. There are a number of complexities to consider to enable seamless integration of a well-constructed hybrid cloud strategy. We recommend employing these 5 best practices as you move toward a multi-cloud or hybrid cloud architecture to ensure a successful transition.

Utilize cloud management tools.

Cloud management providers have responded to the complexities of a hybrid strategy with an explosion of cloud management tools. These tools can look at your automation and governance, lifecycle management, usability, access and more, and perform many tasks with more visibility.

Unique tooling for each cloud provider is especially important. Some partners may recommend a single pane of glass for simplicity, but that can be too simple for service catalogues and when launching new resources. The risk with going too simplistic is missing the opportunity to take advantage of the best aspects of each cloud.

Complete a full assessment of applications and dependencies first.

Before you jump into a hybrid cloud strategy, you need to start with a full assessment of your applications and dependencies. A common misstep is moving applications to the public cloud, while keeping your database in your private cloud or on-prem datacenter. The result is net latency drag, leading to problems like slow page loads and videos that won’t play.

Mapping applications and dependencies to the right cloud resource prior to migration gives you the insight necessary for a complete migration with uninterrupted performance. Based on the mapping, you know what to migrate when, with full visibility into what will be impacted by each. This initial step will also help with cloud implementation and hybrid connect down the line.

Put things in the right place.

This might sound obvious, but it can be challenging to rationalize where to put all your data in a hybrid environment. Start by using the analysis of your applications and dependencies discussed above. The mapping provides insight into traffic flows, networking information, and the different types of data you’re dealing with.

A multi-cloud environment is even more complex with cost implications and networking components. On-prem skills related to wide area network (WAN) connectivity are still necessary as you consider how to monitor the traffic – ingress, egress, east, and west.

Overcome silos.

Silos can be found in all shapes and sizes in an organization, but one major area for silos is in your data. Data is one of the biggest obstacles to moving to the cloud because of the cost of moving it in and out and accessing it. The amount of data you have impacts your migration strategy significantly, so it’s critical to have a clear understanding of where data may be siloed.

Every department has their own data, and all of it must be accounted for prior to migrating. Some data silo issues can be resolved with data lakes and data platforms, but once you realize silos exist, there’s an opportunity to break them down throughout the organization.

An effective method to breaking down silos is by getting buy-in from organizational leaders to break the cultural patterns creating silos in the first place. Create a Cloud Center of Excellence (CCoE) during your cloud transformation to understand and address challenges within the context of the hybrid strategy across the organization.

Partner with proven experts.

Many companies have been successful in their hybrid cloud implementation by leveraging a partner for some of the migration, while their own experts manage their internal resources. With a partner by your side, you don’t have to invest in the initial training of your staff all at once. Instead, your teams can integrate those new capabilities and skills as they start to work with the cloud services, which typically increases retention, reduces training time, and increases productivity.

Partners will also have the knowledge necessary to make sure you not only plan but implement and manage the hybrid architecture for overall efficiency. When choosing a partner, make sure they’ve proven the value they can bring. For instance, 2nd Watch is one of only five VMware Cloud on AWS Master Services Competency holders in the United States. That means we have the verified experience to understand the complexities of running a hybrid VMware Cloud implementation.

If you’re interested in learning more about the hybrid cloud consulting and management solutions provided by 2nd Watch, Contact Us to take the next step in your cloud journey.

-Dusty Simoni, Sr Product Manager, Hybrid Cloud

3 Reasons to Consider a Hybrid Cloud Strategy

If there’s one thing IT professionals can agree on, it’s that hybrid cloud computing isn’t going away. Developed in response to our growing dependence on data, the hybrid cloud is being embraced by enterprises and providers alike.

What is Hybrid Cloud Computing?

Hybrid cloud computing can be a combination of private cloud, like VMware, and public cloud; or it can be a combination of cloud providers, like AWS, Azure and Google Cloud. Hybrid cloud architecture might include a managed datacenter or a company’s own datacenter. It could also include both on-prem equipment and cloud applications.

Hybrid cloud computing gained popularity alongside the digital transformation we’ve witnessed taking place for years. As applications evolve and become more dev-centric, they can be stored in the cloud. At the same time, there are still legacy apps that can’t be lifted and shifted into the cloud and, therefore, have to remain in a datacenter.

Ten years ago, hybrid and private clouds were used to combat growth, but now we’re seeing widespread adoption from service providers to meet client needs. The strategy has range from on-prem up to the cloud (VMware Cloud (VMC) on AWS), to cloud-down (AWS Outposts), to robust deployment and management frameworks for any endpoint (GCP Anthos).

With that said, for many organizations data may never entirely move to the cloud. A company’s data is their ‘secret sauce,’ and despite the safety of the cloud, not everything lends itself to cloud storage. Depending on what exactly the data is –mainframes, proprietary information, formulas – some businesses don’t feel comfortable with service providers even having access to such business-critical information.

1. Storage

One major reason companies move to the cloud is the large amount of data they are now storing. Some companies might not be able to, or might not want to, build and expand their datacenter as quickly as the business and data requires.

With the option for unlimited storage the cloud provides, it is an easy solution. Rather than having to forecast data growth, prioritize storage, and risk additional costs, a hybrid strategy allows for expansion.

2. Security

The cloud is, in most cases, far more secure than on-prem. However, especially when the cloud first became available, a lot of companies were concerned about who could see their data, potential for leaks, and how to guarantee lockdown. Today, security tools have vastly improved, visibility is much better, and the compliance requirements for cloud providers include a growing number of local and federal authorities. Additionally, third party auditors are used to verify cloud provider practices as well as internal oversight to avoid a potentially fatal data breach. Today, organizations large and small, across industries, and even secret government agencies trust the cloud for secure data storage.

It’s also important to note that the public cloud can be more secure than your own datacenter. For example, if you try to isolate data in your own datacenter or on your own infrastructure, you might find a rogue operator creating shadow IT where you don’t have visibility. With hybrid cloud, you can take advantage of tools like AWS Control Tower, Azure Sentinel, AWS Landing Zone blueprints, and other CSP security tools to ensure control of the system. Similarly, with tooling from VMware and GCP Anthos you can look to create single policy and configuration for environment standardization and security across multiple clouds and on-prem in a single management plane.

3. Cost

Hybrid cloud computing is a great option when it comes to cost. On an application level, the cloud lets you scale up or down, and that versatility and flexibility can save costs. But if you’re running always-on, stagnant applications in a large environment, keeping them in a datacenter can be more cost effective. One can make a strong case for a mixture of applications being placed in the public cloud while internal IP apps remain in the datacenter.

You also need to consider the cost of your on-prem environment. There are some cases, depending on the type and format of storage necessary, where the raw cost of a cloud doesn’t deliver a return on investment (ROI). If your datacenter equipment is running near 80% or above utilization, the cost savings might be in your favor to continue running the workload there. Alternately, you should also consider burst capacity as well as your non-consistent workloads. If you don’t need something running 24/7, the cloud lets you turn it off at night to deliver savings.

Consistency of Management Tooling and Staff Skills

The smartest way to move forward with your cloud architecture – hybrid or otherwise – is to consult with cloud computing experts. 2nd Watch helps you choose the most efficient strategy for your business, aids in planning and completing migration in an optimized fashion, and secures your data with comprehensive cloud management. Contact Us to take the next step in your cloud journey.

-Dusty Simoni, Sr Product Manager, Hybrid Cloud