Machine Learning PoC [Hersteld].png

cloud start: machine learning

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

During the machine learning Proof of Concept, 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.


key activities.png

key activities

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

  • Analyze data characteristics
  • Assess data quality, cleanliness, potential correlation, and patterns
  • Check for class imbalance
  • Validate hypothesis relative to data

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

  • Research existing strategies and white papers
  • Select an algorithm based on hypothesis, type of features, patterns in data
  • Document decisions related to algorithms

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

  • Use domain knowledge to identify features
  • Transform raw data into features
  • Define new features as needed, and remove redundant/duplicate features, as well as highly correlated features

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.

  • Define the right modeling strategy and choose the right ML algorithms
  • Select data set for training, test set, and validation
  • Develop initial model
  • Determine duration, and amount of data for initial experiment

iterate to improve model performance
Refine initial machine learning model to improve performance. The final model should meet performance targets agreed upon by Google and the customer.

  • Evaluate and visualize model result
  • Determine corrective actions to improve model
  • Iterate and improve model results


  • Basic knowledge of data analytics in Google Cloud Platform
  • Data to analyze
  • High involvement of customer's business experts

key takeaways

  • 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


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.



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

  • The amount of data engineering required
  • Iterations over the model

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