# MLOps & Deployment (Data Science & ML)

> MLflow, Docker, FastAPI, Streamlit, Prefect, feature stores, data pipelines, monitoring, ML business metrics, cloud deployment

- 24 interview questions
- Senior
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. What is the primary role of MLflow in an MLOps workflow?

**Answer**

MLflow is an open-source platform that manages the complete ML model lifecycle: experiment tracking (metrics, parameters, artifacts), model packaging, centralized registry and deployment. This enables experiment reproducibility and standardized model versioning.

## 2. Which command is used to log a parameter in MLflow?

**Answer**

The mlflow.log_param function records a hyperparameter (learning rate, epochs, batch size) associated with a run. These parameters are then visible in the MLflow UI and allow comparison of different training configurations.

## 3. What is the difference between mlflow.log_metric and mlflow.log_param?

**Answer**

log_param records fixed values defined before training (hyperparameters like learning_rate, epochs), while log_metric records values that change during or after training (accuracy, loss). Metrics can be logged multiple times with different steps to create curves.

## 21 more questions available

- What is the main advantage of using Docker to deploy an ML model?
- Why use a multi-stage Dockerfile for an ML application?

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## Other Data Science & ML interview topics

- [Python Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-basics.md): 25 questions, Junior
- [Python Object-Oriented Programming](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-oop.md): 20 questions, Junior
- [Python Data Structures](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-data-structures.md): 20 questions, Junior
- [Git Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/git-fundamentals.md): 18 questions, Junior
- [SQL Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-basics.md): 20 questions, Junior
- [NumPy Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/numpy-fundamentals.md): 22 questions, Junior
- [Pandas Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-basics.md): 22 questions, Junior
- [Jupyter & Google Colab](https://sharpskill.dev/en/technologies/data-science/interview-questions/jupyter-colab.md): 16 questions, Junior
- [SQL Joins & Advanced Queries](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-joins-advanced.md): 22 questions, Mid-Level
- [Advanced Pandas](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-advanced.md): 24 questions, Mid-Level
- [Visualization with Matplotlib & Seaborn](https://sharpskill.dev/en/technologies/data-science/interview-questions/matplotlib-seaborn.md): 20 questions, Mid-Level
- [Interactive Visualizations with Plotly](https://sharpskill.dev/en/technologies/data-science/interview-questions/plotly-interactive.md): 18 questions, Mid-Level
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-descriptive.md): 20 questions, Mid-Level
- [Inferential Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-inferential.md): 24 questions, Mid-Level
- [Web Scraping](https://sharpskill.dev/en/technologies/data-science/interview-questions/web-scraping.md): 18 questions, Mid-Level
- [BigQuery & Cloud Data](https://sharpskill.dev/en/technologies/data-science/interview-questions/bigquery-cloud.md): 18 questions, Mid-Level
- [Feature Engineering](https://sharpskill.dev/en/technologies/data-science/interview-questions/feature-engineering.md): 22 questions, Mid-Level
- [Supervised ML: Regression](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-regression.md): 24 questions, Mid-Level
- [Supervised ML: Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-classification.md): 24 questions, Mid-Level
- [Decision Trees & Ensembles](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-trees-ensembles.md): 24 questions, Mid-Level
- [Unsupervised ML](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-unsupervised.md): 22 questions, Mid-Level
- [ML Pipelines & Validation](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-pipelines-validation.md): 22 questions, Mid-Level
- [Time Series & Forecasting](https://sharpskill.dev/en/technologies/data-science/interview-questions/time-series-forecasting.md): 22 questions, Mid-Level
- [Deep Learning Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/deep-learning-fundamentals.md): 24 questions, Senior
- [TensorFlow & Keras](https://sharpskill.dev/en/technologies/data-science/interview-questions/tensorflow-keras.md): 22 questions, Senior
- [CNN & Image Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/cnn-image-classification.md): 24 questions, Senior
- [RNN & Sequences](https://sharpskill.dev/en/technologies/data-science/interview-questions/rnn-sequences.md): 22 questions, Senior
- [Transformers & Attention](https://sharpskill.dev/en/technologies/data-science/interview-questions/transformers-attention.md): 24 questions, Senior
- [NLP & Hugging Face](https://sharpskill.dev/en/technologies/data-science/interview-questions/nlp-huggingface.md): 24 questions, Senior
- [GenAI & LangChain](https://sharpskill.dev/en/technologies/data-science/interview-questions/genai-langchain.md): 24 questions, Senior

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