AI Solutions

SMB friendly AI and ML services

What is Google Cloud AI Platform Consulting Services?

This service utilizes Google Cloud AI Platform tools and infrastructure such as Auto ML and Google APIs to build an end to end an AI enhanced applications. This consulting service also helps you explore your data to identify meaningful use cases, validates assumptions and helps integrate to production.

Benefits of Google Cloud AI Platform

  • Leadership: Google created TensorFlow in 2014 and open source .
  • Cost effectiveness: Flexible on demand compute options that are up to 70% cheaper than competition
  • Prepackaged yet powerful AI solutions for Enterprises new on the AI path with very limited resources
  • Cloud AutoML helps businesses with limited ML expertise to start building their own high-quality custom models
  • For advanced enterprises on AI journey who have data scientists and ML experts inhouse to train ML models using TensorFlow, Cloud TPUs, GPUs and ML engine

Benefits of Cloudypedia Services

  • Cloud data engineering certified team
  • In house data scientists ready to help SMB teams
  • Fast and proven use-case validation methodology
  • Domain expertise in Financial Services , Retail, Manufacturing, Healthcare, Sales, Marketing

Google Cloud AI platform meets you where you are

Google Cloud AI Platform Projects Process

Cloud AI Explore

Cloud Explore: This service helps you understand machine learning (ML) concepts and identify and qualify potential business problems that can be addressed using ML. Ideal for determining if ML is right for your business and which use cases are realistic and achievable.


  • Gain new competitive advantages with ML
  • Identify potential ML use cases
  • Address targeted business problems
  • Survey the foundation for ML potential

Activities and Deliverables

  1. ML overview session
  2. Use case ideation workshop
  3. High-level data qualification
  4. Analysis and recommendations

Cloud AI Validate

Cloud AI validate service helps you take the use case identified in Cloud Explore from theoretical to practical by developing an ML model. This model will prove the value of the use case and its ML solution to stakeholders prior to investing more in the next phase of build out.

  • Implements steps 2–7 in the ML life-cycle
  • Delivers a minimum viable ML model or feasibility study
  • Delivers 12 days of work effort in a 6-weeks calendar window

Engagement activities

  • Develop an ML solution model
  • Perform exploratory data analysis
  • Explore the right set of features to include
  • Present results of the model that shows its business value


  1. Data exploration
  2. Data pipeline and feature engineering
  3. Build and iterate ML model
  4. Present results to stakeholders

In Scope of Cloud AI Validate

  • Data exploration
  • Algorithm selection
  • Data pipeline
  • Feature engineering
  • Development of initial ML model
  • Iteration to improve performance of ML model

Out of scope of Cloud AI Validate

  • Building a complete data pipeline
  • Deploying the model into production
  • Converting the model into an API

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