Self-service ML platform architecture in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the main component responsible for model training in a self-service ML platform.

MLOps

class [1]:
    def train(self, data):
        # Training logic here
        pass

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AModelTrainer

BDataLoader

CModelRegistry

DFeatureStore

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a component that handles data loading or feature storage instead of training.

The ModelTrainer class is typically responsible for training ML models in a self-service ML platform.

2fill in blank

medium

Complete the code to initialize the feature store client in the platform.

MLOps

feature_store_client = [1]()

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AModelTrainer

BDataLoader

CFeatureStoreClient

DModelRegistry

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing with model training or data loading components.

The FeatureStoreClient is used to interact with the feature store in a self-service ML platform.

3fill in blank

hard

Fix the error in the code that registers a model in the model registry.

MLOps

model_registry = ModelRegistry()
model_registry.[1](model_name, model_object)

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Aregister_model

Bload_model

Ctrain_model

Ddelete_model

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods that do not add the model to the registry.

The correct method to add a model to the registry is register_model.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps feature names to their types for features with numeric types.

MLOps

numeric_features = {feature[1]: feature_type[2] for feature, feature_type in features.items() if feature_type in ['int', 'float']}

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A:

B,

C.

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using commas or dots instead of colons in key-value pairs.

In dictionary comprehensions, key-value pairs are separated by a colon :.

5fill in blank

hard

Fill all three blanks to filter models with accuracy above 0.9 and create a dictionary with model names and their accuracies.

MLOps

high_accuracy_models = { [1]: [2] for [3] in models.items() if [2] > 0.9 }

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Amodel_name

Baccuracy

Cmodel_name, accuracy

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names or not unpacking items properly.

The dictionary comprehension uses model_name as key, accuracy as value, and iterates over model_name, accuracy pairs.