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.

Objectives

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

Activities and Deliverables

ML overview session
Use case ideation workshop
High-level data qualification
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

Deliverables

Data exploration
Data pipeline and feature engineering
Build and iterate ML model
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

Start exploring your AI project email: ai@cloudypedia.com

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