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Cloud Sprint: minimal viable model

we help you build a minimal viable machine learning model of a specific business case


Cloud Start Machine Learning is an intensive process in which we run through the necessary steps to build an initial machine learning model, as well as demonstrate the feasibility of the solution. This process involves identifying a hypothesis, analyzing a data set, determining the appropriate algorithm, segmenting and qualifying the data, and training the algorithm against the data.

 

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key activities

  • Data exploration
    Analyze available data sources to assess state of data and potential usefulness when applied in a machine learning model.

  • Algorithm selection
    Research modeling strategies to determine appropriate machine learning algorithm to address business problem.

  • Data pipeline and feature engineering
    Create machine learning model features based on raw data analysis and tests.

  • Develop initial ML model
    Develop an initial machine learning model that uses the data to solve the business problem. In this phase we will use both the selected machine learning algorithms, and the features created in the prior phase.

  • Iterate to improve model performance
    Refine initial machine learning model to improve performance. The final model should meet performance targets agreed upon by g-company and the customer.

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prerequisites
 

  • basic knowledge of data analytics in Google Cloud Platform

  • data to analyze

  • high involvement of customer's business experts

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deliverables
 

  • machine learning pipeline
    The infrastructure that surrounds a machine learning algorithm. Includes gathering the data from the front end, putting it into training data files, training one or more models.

  • Initial machine learning model(s)
    A trained algorithm that addresses the selected business problem.

  • Implementation plan
    A plan for the customer to use, on how to deploy the machine learning models in production.

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audience

As machine learning enables any organization to create deeper insights from data, and even predict future events, the PoC accommodates a diversity of use cases. Some examples:
An e-commerce marketeer or sales manager wants to predict upsells, a government needs to improve water management, whereas a farmer wants to analyze and improve the quality of his food, and a marketing manager expects to maximize the return from advertisement spending.

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duration

10 to 15 days over the period of two months.
Depending on:

  • the amount of data engineering required

  • iterations over the model

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scope and pricing

Pricing will be agreed upon by customer and g-company

 


 

Contact us

phone +31 30 711 0940  (NL) | +32 92 982045 (BE) or mail us at solutions@g.company

 
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