Planning a machine learning project is no small feat, especially when it comes to choosing your technology stack. Python is one of the most popular choices for AI/ML projects. However, before getting in too deep, it’s important to understand Python’s frameworks for data science, how it stacks up against alternative languages, and how to succeed in making ML-based apps with Python.
Why is Python Good for Machine Learning?
Being one of the simplest programming languages to use and learn, Python has a distinct advantage for machine learning development. It is highly versatile thanks to its wide range of integration options, such as web framework interfacing, databases, and APIs. Python has several data-centric libraries like Matplotlib and Plotly that can be leveraged for data pattern evaluation and model output as well.
Python’s code can also be extended to and embedded in other languages like C++. Additionally, the language is object-oriented, meaning that it facilitates the input of specific commands to give specific results in machine learning applications.
Open source with a vibrant community, Python makes the development cycle easier. Its unique features like easy-to-understand syntax, code maintainability and readability make it more comprehensible for machine learning projects. That’s why building apps with Python has become a good practice among product owners who want to add innovation to their products without unreasonable cost.
Python Frameworks for Machine Learning and Data Science
Instead of building solutions with Python from scratch, there are a number of helpful libraries that can help with ML software development. Let’s explore some of the various frameworks of Python used for machine learning and data science applications.
| Technology | Description | Domain | License* |
| TensorFlow | One of the most popular ML Python frameworks . Training DL models, inspection & model serialization. | Machine learning, deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| Keras | High-level, deep learning framework developed by Google for implementing neural networks. Fast experimentation with deep neural networks.. Focused on being user-friendly, modular and readable. | Deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| PyTorch | One of the fast-growing ML frameworks used for creating deep neural networks. Built for speeding up the process between research prototyping and deployment. PyTorch is great for its excellent support for GPUs. | Machine learning, deep learning, computer vision, natural language processing, etc. | BSD |
| scikit-learn | Focuses on data modeling, has a lot of built-in classification, regression, and clustering algorithms. This framework is well-documented and easy to use. Has excellent integration with other Python libraries, such as Pandas, NumPy, Plotly, etc. | Machine learning, data mining, data analysis, etc. | BSD |
| Pandas | One of the most popular frameworks for data manipulation and analysis. Assists with data reshaping and preprocessing, dataset joining, data filtration, alignment, & handles missing data, etc. | Data analysis, data manipulation, data visualization | BSD |
| NumPy | One of the main libraries for working with numerical data. It supports multi-dimensional arrays, and matrices, including a lot of other mathematical functions. It is a math powerhouse, supporting Fourier transforms, linear algebra, & more. | Scientific
computing, linear algebra, numerical analysis |
BSD |
| MXNet | Deep learning framework for training and deploying deep neural networks on a wide variety of platforms. MXNet is lightweight and can scale on multiple GPUs on multiple machines. Supports Java, C++, Scala, Go, R, & more. | Deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| NLTK | One of the most popular frameworks for working with textual data. It has an easy interface. NLTK provides tokenization, stemming, tagging, parsing, classification and more. | Natural language processing, text analysis, text preprocessing | Apache 2.0 |
| SpaCy | Framework for more advanced natural language processing. SpaCy provides pretrained pipelines, fine-tuning options, tagging, parsing and so on. It also supports tokenization and training for around 70+ languages. | Natural language processing, NLP deep learning models training, inference | MIT |
| SparkML | Framework for scaling machine learning pipelines. It also supports a wide range of popular ML features and models, but the main, primary difference - is division into slices & distributing computation across multiple machines. | Building, training, & deploying and scaling ML models and pipelines | Apache 2.0 |
| Plotly | Python library that is used for fancy interactive visualizations. It supports over 40 unique chart types in different areas such as financial, statistical, geographic and many more. Plotly can be used not only on Python, but also on R, JavaScript and Julia, etc. | Data visualization | MIT |
*The BSD and MIT licenses have minimal restrictions on the modification and distribution of the software code. Apache has more conditions and restrictions on this.
The table sourced from Python app development guide
Python vs Other Programming Languages for ML Projects
Although Python is one of the most popular programming languages for machine learning, it's important to consider alternatives that may work better for your project. Here are some other languages that are popular with ML development:
- R: Focusing on statistical computing and data analysis, R has robust tools utilized for machine learning. Although R has a stronger focus on data analysis, Python is more flexible and has a larger ecosystem of libraries and frameworks that can be used for more than just machine learning.
- Java: A general-purpose language used by enterprises around the world, Java has ML libraries like Weka and Deeplearning4j. Java’s strength lies in its performance and scalability, an important advantage to have when handling large amounts of data.
- C++: A powerful and efficient language for systems programming and performance-critical applications. Can implement ML libraries like TensorFlow and Caffe. Training models with large datasets or deploying models on resource-constrained devices is where C++ shines brightest in ML applications.
- Golang: Go’s open-source status and lightness of execution make it a decent choice for machine learning. It can easily include massive data sets, and does a great job in situations where performance, concurrency, and deployment considerations are critical.
As you can see, Python remains one of the most popular languages thanks to its extensive libraries and vibrant community. However, it’s important that you consider the alternatives and pick the technology stack that’s right for you, taking into account other aspects of your project such as load, performance, security requirements, etc.
How to Succeed in Building ML-based Apps with Python
Whether you’re working with Python or another language for machine learning development, the most important element in ensuring your success is the right team. Having access to a broad range of skills with regard to machine learning, data science, and programming with Python are essential. Work with developers that have these skills and share your vision for success.
The right team will be able to handle unexpected challenges with ease and navigate the complexities of your project quickly and efficiently. This team also needs to be agile, as your business needs may change or unforeseen technological or market obstacles may get in the way of your goals. Experienced Python engineers will help you take your vision and transform it into reality.
Reviewed by Anastasiia Molodoria, AI Team Lead at MobiDev