If you're a junior or mid-level machine learning engineer or data scientist, this article is perfect for you. Once you've chosen your preferred machine learning library, such as PyTorch or TensorFlow, and mastered the model architecture, you can train the model to solve real-world problems.
In this blog post, I'll cover five Python libraries that I think every machine learning engineer and data scientist should be familiar with, and that will be a valuable addition to the skill set you've mastered. Make you a more competitive candidate by streamlining the machine learning development process.
1. MLFlow — Experiment and model tracking
Image credit: Author, example from https://mlflow.org
Imagine if you're a machine learning developer and you're building a project that predicts customer churn. You'll need to use a Jupyter notebook to explore the data, trying out different algorithms and hyperparameters. As the project progressed, Jupyter notebooks became more and more complex, full of code, results, and visualizations. This makes it increasingly difficult to track project progress and identify what works and what doesn't.
That's where MLflow comes in. MLflow is a platform that helps manage machine learning experiments from start to finish, ensuring traceability and reproducibility. It provides a centralized repository for code, data, and model components, as well as a traceability system that records all experimental content, including hyperparameters, metrics, and outputs.
MLflow helps you avoid the specific path of Jupyter notebook using traps:
- Centralized repository: MLflow keeps your code, data, and model artifacts organized and easily accessible, so you can quickly find the resources you need and avoid getting lost in the maze of your notebook.
- Experiment tracking: MLflow records every experiment, including the exact code, data, and hyperparameters used. This allows you to easily compare different experiments and identify the factors that led to the best results.
- Reproducibility: MLflow makes it possible to replicate the best model with the same code, data, and environment. This is critical to ensure the consistency and reliability of experimental results.
So, if you want to build effective machine learning models, ditch the clutter of Jupyter notebooks and embrace the power of MLflow.
2. Streamlit — Small, fast web application
Streamlit is the most popular front-end framework for data scientists. It is an open-source Python framework that allows users to quickly and easily create interactive data applications, which is especially beneficial for data scientists and machine learning engineers who don't have a basic knowledge of web development.
Use Streamlit. Developers can build and share engaging user interfaces and deploy models without requiring in-depth front-end experience or knowledge. The framework is free and open-source, making it possible to create shareable web applications in minutes.
If you have a small project that involves machine learning, you can add a user interface with Streamlit, there are many ready-made templates that can be front-ended in minutes without taking a long time. It's also very easy to share, and it's sure to be a highlight on your resume.
3. FastAPI — Deploy models easily and quickly
Image source: Author
Once the model has been trained and validated, it needs to be deployed so that it can be used by other applications, and that's where FastAPI comes in.
FastAPI is a high-performance web framework for building RESTful APIs, known for its simplicity, ease of use, and speed. That's why it's ideal for deploying machine learning models to production.
Here are some of the reasons why ML engineers and data scientists should learn FastAPI:
- Speed: FastAPI is very fast. It uses a modern asynchronous programming model and is able to efficiently handle multiple requests at the same time, which is critical for deploying machine learning models that need to process large amounts of data.
- Simplicity: FastAPI is easy to learn and use. Its syntax is clear and concise, making it easier to write clean and easy-to-maintain code, which is very important for ML engineers and data scientists who don't have a lot of experience as web developers.
- Ease of use: FastAPI offers a lot of features that make it easy to build and deploy APIs. For example, it has built-in support for automated archiving, data validation, and error handling, allowing ML engineers to focus on their core work—building and deploying models—saving time and effort.
- Production-ready: Designed for production with support for features such as multiple backends, encryption, and deployment tools, FastAPI becomes a reliable choice for deploying machine learning models.
In conclusion, FastAPI is a powerful and versatile tool for deploying machine learning models to production. Its ease of use, speed, and production readiness make it ideal for ML engineers and data scientists.
4. XGBoost — Predict tabular data quickly and well
Image credits: Author, source 1 and source 2
XGBoost is a powerful machine learning algorithm known for its accuracy, speed, and scalability. It is based on a gradient boosting framework that combines multiple weak learners into a single strong learner. In simple terms, using multiple small models, such as random forests, and combining them into one large model results in a faster model (compared to neural networks), but at the same time it is scalable and not easily overfitted.
Here are some of the reasons why ML engineers and data scientists should learn XGBoost:
- Accuracy: XGBoost is one of the most accurate machine learning algorithms, it has won many machine learning competitions and is consistently at the top of the list in a variety of tasks.
- Speed: XGBoost is very fast, enabling training and prediction on large datasets quickly and efficiently. This makes it a good choice for applications where speed is paramount, such as real-time fraud detection or financial modeling.
- Scalability: XGBoost is highly scalable. It can handle large datasets and complex models without sacrificing accuracy. This makes it the best choice for applications with large amounts of data or high model complexity.
If the task involves tabular data (such as predicting a room rate based on the number of rooms, or calculating the likelihood of a customer purchasing a product based on last purchase/account data), XGBoost is the first algorithm you should try before resorting to Keras or PyTorch's neural networks.
5. ELI5 — Make the model easier to interpret and transparent
Image Credits: Author, Source 1, Source 2
Once the model is trained, it can be deployed and used, and the model is more of a "black box" – you get what you put in and you get what you want. How exactly does the model work? It's numbers here, it's numbers there, and finally an answer comes to it.
If the customer/boss asks you, how did the model arrive at a particular answer? You simply can't know, you can't even know, which parameters are most important during the training process and which just add noise?
All of these questions can be answered using the ELI5. This library will make the model transparent, explainable, and easier to understand. Get more information about the model, data, training process, weight distribution, and input parameters. In addition to this, it is possible to "debug" the model and gain more insights into what architecture would work better and what problems exist with the current model.
ELI5 supports many libraries like Scikit-Learn, Keras, XGBoost, and many others. Models can classify image, text, and tabular data.
conclusion
We've explored five leading data science frameworks, and if you master these libraries, you'll gain multiple advantages:
- You'll have more opportunities to get a job than other data scientists, as you've gained several skills in all aspects of machine learning.
- You'll be able to work on full-stack projects because you can not only develop the model, but also deploy it using the FastAPI backend and let users interact with it through the Streamlit frontend.
- You won't get lost in the "Jupyter Notebook Hell" because all machine learning experiments will become traceable and reproducible with MLFlow, and all models will be properly versioned.
- Tabular data isn't a problem for you because you know how to use XGBoost to train scalable, fast, and accurate models.
- Most models are no longer a "black box" for you, as you can understand them more deeply, debug their thought processes, and interpret their predictions with ELI5.
All of these arsenals will make your life easier, adding many useful and important skills to your ammo arsenal. Happy coding!