Python is ideal for data science thanks to its ease of use, active user community, and extensive data science libraries. Python is highly capable of manipulating and analyzing data and has a broad range of tools for machine learning and natural language processing. In this article, we’ll explore what exactly makes Python development so practical and the role that Streamlit and Snowpark play in accelerating the pace of innovation in data science and machine learning.
Why is Python so Useful for Data Science?
Python is the most popular programming language for data science. Its unique blend of power and simplicity has placed it at the heart of many machine learning, data analysis, and other data science applications. Here are four reasons why Python development is the future of data science workflows.
1. Extensive functionality
Python is very easy to learn and apply in multiple use cases. Whether it's being used by multiple teams working in tandem on a complex machine learning project or by a single programmer to create a simple web app, Python can support projects of all sizes. It’s efficient, reliable, and incredibly flexible, making it ideal for projects of all sizes.
Extensive user community
2. Python is now more than 30 years old. Since its inception, its vibrant and dedicated community of users has grown. Documentation, guides, and video tutorials abound, providing easily accessible resources for everyone, from beginners to seasoned experts. The maturity of the Python development community has been enhanced significantly by Google’s adoption of Python in 2006, adding its credibility and resources to deepen the available knowledge base.
3. Vast number of libraries and frameworks
The extensive libraries and frameworks available for Python shorten the development cycle for programmers. These collections of prewritten code make it possible to optimize otherwise time-consuming tasks, including machine learning and other data science applications.
4. Ideal for machine learning applications
In addition to the extensive set of packages that streamline the data preparation, model development, and deployment process, python is an intuitive, easy-to-use-and-understand programming language, making it ideal for machine learning applications wherein the data scientists working on the models may come from backgrounds where computer programming skills were not essential. Python is perfectly suited for collaborative deployment, facilitating fast prototyping and product testing.
Developing Python Data Apps with Streamlit
Productionizing machine learning models requires gathering feedback from users. Streamlit provides data scientists with an easy way to build interactive applications, helping nontechnical users gain trust in the data and ML model to provide necessary feedback. This moves projects from proof of concept to a minimum viable product that is adopted by business users and can drive action from model results.
What is Streamlit and why is it so valuable for data science applications?
How does Streamlit simplify app development?
Streamlit simplifies the web app development process considerably, equipping your team to engage with data and models. During model development, data scientists have often already created many insight, evaluation, and prediction graphs. With Streamlit, migrating the code required to generate these visualizations is straightforward. And since it’s Python-based, the Python code that’s already been written can be reused.
Unlike data visualization tools that use other languages, Streamlit doesn’t require rewriting all of the code to create visualizations. Streamlit is built with efficiency in mind. Widgets are treated as variables, eliminating the need for callbacks. Built-in data caching allows data to remain performant even while loading data from the web, manipulating large data sets, or performing expensive computations. It’s compatible with most major Python libraries such as scikit-learn, Keras, PyTorch, SymPy(latex), NumPy, and pandas.
Streamlit is a versatile tool for moving machine learning models into production quickly and efficiently. Here are three ways this low-code platform makes data science outcomes accessible to those outside of the data science community.
1. Prediction serving
Streamlit applications can deliver model results and even enable nontechnical users to request predictions for new data. Within the same application, data scientists can show what data was used, how it was changed, and more to help nontechnical users better understand and interpret the logic and process behind an ML model. Integrating predictions into a web app typically requires the help of a front-end engineer and slows the pace of delivering model results in production. With Streamlit, data scientists can create a much faster feedback loop with end users, iterating on the product and visualizations independently.
2. Model monitoring
Before a model is deployed, data scientists and users must first agree on a set of evaluation metrics that the model will be measured against. Streamlit makes it easy to build meaningful monitoring visualizations with interactive components. These applications help develop a deeper understanding of how the model works while building trust in the predictions it produces.
3. Providing model insights
Streamlit’s dashboards can accommodate much more than predefined performance metrics. Supplementing the user-facing dashboard with additional details—such as the input data or features the model is making predictions on—and involving them in the debugging process not only fosters added engagement but can also speed up the feedback cycle, accelerating the pace of the machine learning pipeline.
Using Snowflake Snowpark to Accelerate Python Development
Streamlit isn’t the only tool that’s accelerating the pace of Python development. Using Python’s familiar syntax and thriving ecosystem of open-source libraries, Snowflake Snowpark empowers data engineers, developers, and data scientists to explore and process data where it lives. The Snowpark library is a simple, easy-to-use API for querying and processing data. It enables developers to build the data pipelines and machine learning models that can then be exposed in Streamlit applications to keep processing where the data is. Users can leverage their language of choice (including Python, Java, or Scala) with familiar DataFrame and custom function support to build powerful and efficient pipelines, machine learning (ML) workflows, and data applications while working inside Snowflake’s Data Cloud.
The Snowflake Data Cloud: Unlock Your Data’s True Potential
The past few years have seen rapid advances in Python development, including Snowflake’s acquisition of Streamlit in early 2022. Streamlit’s integration with Snowflake (in preview) makes it possible for developers to bring their data and ML models to life as secure, interactive applications. Paired with Snowpark’s ability to work with and process data all within Snowflake, developers can now build and run programs in the language of their choice, all within Snowflake. Build scalable, optimized pipelines, apps, and ML workflows with superior price/performance and near-zero maintenance, all powered by Snowflake's elastic performance engine.