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ML Building Blocks: Services and Terminology

AWS Certified MLS | 01 Nov 2019



  1. Training
    • Refers to how machine uses historical data sets to build its prediction algorithms.
  2. Model
    • Model is what your machine creates after it’s been trained and refines over time as it learns.
  3. Prediction
    • Prediction is machine’s best estimate of what the outcome of specific input or set of inputs would be. It’s sometimes called the Inference of a Model.


In Training Process, Data is split into:

Process / ML Workflow

Goal of Machine Learning model is to provide solution to a Business Problem. This happens through prediction. Prediction is not accurate and improves over time through provided feedback.

ML Problem Framing

Classification Problems

Problem Definition

Data Collection / Integration

Data Preparation

Data Cleaning

Shuffling Training Data

Makes data order not important and improves the results in certain algorithms.

Test-Validation-Train Split

Cross Validation

Data Visualization & Analysis

Helps us understand the data better, refine the data, clean the outliers. This will result in better features leading to better models.

Feature Engineering

Process of converting raw data into more useful features.

Model Training

Parameters are the knobs used to tune our Machine Learning Algorithm.

Parameter Turning

Model Evaluation

Business Goal Evaluation

Feature and Data Augmentation

Increases the complexity of the training data set by deriving features from internal / external data.