Learn to design real machine learning systems with the help of several open-ended machine learning problems.

Become a Machine Learning Engineer

Learn to design real machine learning systems with the help of several open-ended machine learning problems commonly asked during interviews at big tech companies. You will start by identifying the problem statement, understanding scale and latency requirements and defining metrics. Then you will come up with the architecture, select models, gather data and finally execute and evaluate the models offline and online.

Grokking the Machine Learning Interview

The architectural components diagram for entity linking is shown below. It consists of two paths:

1. Model generation path (training flow)
2. Model execution path (prediction flow)



> 📝 The terms *entity mention* and *recognized entity* are used interchangeably.

# Model generation path

Model generation is responsible for training models for entity linking task. Let's look at the components of this path.

## Training data generation

You will begin by gathering training data for entity linking through open-source datasets and manual labelling/linking of text. These methods will be detailed in the next lesson.

You will pass the training data to the named entity recognition (NER) model trainer and the named entity disambiguation (NED) model trainer.

## NER

NER is responsible for building a machine learning model to recognize entities, such as a person, organization, etc., for a given input text.

## NED
The disambiguation component will receive the output of the NER for linking. The disambiguation process has two phases:

1. Candidate generation

   The first phase of disambiguation is candidate generation. It finds potential matches for the entity mentions, by reducing the size of the knowledge base to a smaller subset of *candidate documents/entities*. This saves us from running the linking model on the entire knowledge base for each entity mention.

Go over the architectural components of the entity linking unit.

Architectural Components