TechValidate Research on Google Cloud Cloud AI/ML

These pages present data that TechValidate has sourced via direct research with verified customers and users of Google Cloud Cloud AI/ML. TechValidate stands behind the authenticity of all published data. Learn more »



1 Published TechFact

6 Published Case Studies



Selected Research Highlights


AI Platform Pipelines Case Study

ZOZO Technologies

Introduction

This case study of ZOZO Technologies is based on a February 2021 survey of AI Platform Pipelines customers by TechValidate, a 3rd-party research service.

“We deploy our models with confidence knowing that there is a robust ML CI/CD pipeline that will deploy the model in the target environment reliably.”

“AI Platform Pipelines has connected us with GCP service entirely when we build an ML pipeline and relieved us from tedious work.”

Challenges

The business challenges that led the profiled company to evaluate and ultimately select AI Platform Pipelines:

  • Experienced the following pain points before using AI Platform Pipelines:
    • Maintaining model integrity
    • Moving models from prototype to production
    • Time-consuming model optimization for different target environments

Use Case

The key features and functionalities of AI Platform Pipelines that the surveyed company uses:

  • The following areas use AI Platform Pipelines:
    • ML Engineers
    • DevOps
  • Most valuable AI Platform Pipelines attributes/features to their organization:
    • Reusability (pipeline components)
    • Orchestration

Results

The surveyed company achieved the following results with AI Platform Pipelines:

  • Google Cloud AI Platform Pipelines helped them to develop their career in the following ways:
    • Increased velocity of pilots/published
    • Higher value/influence in organization
    • Increased demand in the market
  • Experienced the following benefits since using AI Platform Pipelines:
    • Their ML engineers spend less time finagling with resource provisioning, software packages, and target environment optimizations for the models deployed into production
    • They have more effective monitoring of models in production to ensure they don’t go stale
  • Amount of models their ML engineers manage:
    • Before AI Platform Pipelines: 1-10
    • After AI Platform Pipelines: 1-10

Google Cloud Case Study

Rakuten

Introduction

This case study of Rakuten is based on a November 2020 survey of Google Cloud customers by TechValidate, a 3rd-party research service.

“Auto ML and Natural Language API has brought us innovation, especially with the ability to utilize VOC data efficiently. Not just reducing our workloads, it also led us do new types of analysis with VOC data. Looking forward for further updates.”

“Very easy to use, and the outputs are clear to understand.”

Challenges

The business challenges that led the profiled company to evaluate and ultimately select Google Cloud:

  • Machine learning expertise (how to train and fine-tune the model)
  • Difficulties deploying a model in production
  • Finding the right data for model training

Use Case

The key features and functionalities of Google Cloud that the surveyed company uses:

  • Site online review
  • APP review
  • NPS free answer
  • Surveys
  • Customer reviews on products

Results

The surveyed company achieved the following results with Google Cloud:

  • AutoML NL allows customers to classify content based on their own criteria that is not possible with a generic approach
  • Easiness to train a ML model
  • Model performance meets my requirement
  • Enabled more clarity on VOC data analytics using Natural Language API
  • Reduced workloads by aggregating VOC data
  • Helped us become more user-centric minded
  • VOC Analyzer tool improved daily reporting capabilities
  • Reduced workloads (used to read VOC data one by one)
  • VOC data allowed us to make improvements in action planning
  • VOC data was not used back then, but everyone started to notice the importance of this data

Google Contact Center AI Customer Testimonial

Google Cloud CCAI has allowed us to deliver information without a call or personal touchpoint to our employees and customers. This enabled us to give accurate health information to field workers at a time when they needed it.

IT Director, Large Enterprise Healthcare Company



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