12 AI Frameworks and Libraries Every Software Engineer Should Know
As the demand for AI and machine learning continues to surge, software engineers looking to enter the era of AI smoothly need to familiarize themselves with key frameworks and tools. Mastery of these AI frameworks for software engineering, and other emerging tools, not only enhances your skillset but also opens up a world of opportunities in data science and AI.
So let’s take a look at some of the most critical AI frameworks and tools that software engineers should consider learning as AI continues to further enter the field of software engineering.
Machine Learning AI Frameworks for Software Engineering
Scikit-learn is a popular open-source machine learning library in Python. It is known for its simplicity and efficiency, which makes it a popular framework for beginners interested in machine learning. It provides a range of supervised and unsupervised learning algorithms, along with tools for model fitting, data preprocessing, and evaluation.
The reason software engineers should use Scikit-learn is that it offers a gentle introduction to machine learning with a straightforward API, making it ideal for beginners in AI.
XGBoost (eXtreme Gradient Boosting) is a powerful ensemble learning method primarily used for structured or tabular data. It is renowned for its speed and performance in competitions, such as Kaggle, due to its advanced tree boosting techniques. It’s a bit more advanced, but it’s still a great framework for software engineers to learn as there are often times when they will find themselves working on projects that require high accuracy and fast execution times, particularly in predictive modeling tasks.
LightGBM, developed by Microsoft, is another gradient boosting framework that excels in speed and efficiency, particularly with large datasets. It is designed to be highly scalable and supports parallel and GPU learning. LightGBM’s ability to handle large-scale data with lightning speed makes it a valuable tool for engineers working with high-dimensional data.
Caffe is a deep learning framework focused on speed, modularity, and expression. It’s particularly popular for image classification and convolutional neural networks CNNs. Software engineers interested in deep learning applications, especially those involving computer vision, can benefit from Caffe’s highly optimized code, which allows for rapid deployment.
Speaking of deep learning frameworks, let’s now explore some of the most popular deep learning AI frameworks for software engineering choices out there.
Deep Learning Frameworks
Developed by Google, TensorFlow is a leading open-source deep learning framework known for its flexibility and scalability. It supports a broad range of neural network architectures and is used for various applications, from research to production. TensorFlow’s extensive community and robust documentation make it a go-to framework for software engineers exploring deep learning. As more and more applications seek to utilize the benefits of AI, TensorFlow among others is becoming more and more important.
PyTorch, developed by Facebook’s AI research lab, is highly popular among researchers due to its dynamic computation graph, which allows for more flexibility and ease of use. It’s also one of the first frameworks that software engineers become familiar with due to its vast documentation and ease of use when it comes to integration.
Another reason PyTorch is popular is due to its intuitive interface and strong support for GPU acceleration make it ideal for prototyping and experimenting with new models.
Keras is a high-level neural networks API that runs on top of TensorFlow, providing a more straightforward and user-friendly interface. It allows software engineers to quickly prototype models without diving into the complexities of the underlying framework. Keras is ideal for those new to deep learning, offering a simplified path to building and deploying models; especially if they’re interested in mixing the benefits of models with APIs.
Other Essential Tools for Software Engineers
One of the most popular frameworks in Python, Pandas is a powerful data manipulation and analysis tool that provides data structures like DataFrames, which are essential for handling and analyzing data. For software engineers, mastering Pandas is crucial as it simplifies the process of cleaning and preparing data. This is a fundamental step in any project that may use predictive modeling as clean data is critical.
NumPy is the fundamental package for numerical computing in Python. It provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions. Software engineers should be well-versed in NumPy as it underpins most data science and machine learning libraries.
OpenCV is an open-source computer vision library that offers a wide range of tools for image and video processing. It’s a must-know for software engineers working on applications involving image recognition, object detection, or facial recognition, as these features are becoming more widely used in applications both on iPhones and Android.
Hugging Face provides a suite of tools and libraries for NLP, including pre-trained models and transformers. It’s a crucial tool for software engineers diving into NLP, making it easier to implement complex models for tasks like text classification, translation, and summarization. If you’re a software engineer who is looking for a place to start with AI and machine learning frameworks, Hugging Face provides a solid platform where someone can learn and experiment with a live community of users.
Open Neural Network Exchange, or ONNX, is an open format designed to represent deep learning models, facilitating interoperability between different frameworks. This is another platform where software engineers are able to move models between PyTorch, TensorFlow, and other frameworks seamlessly. This is especially useful for optimizing models for production environments.
Conclusion on AI Frameworks for Software Engineering
Familiarity with these AI frameworks for software engineering and tools is essential for any software engineer looking to break into the AI machine learning space. Not only do they provide the foundation for developing robust AI solutions, but they also equip engineers with the skills needed to tackle a wide range of challenges in the industry.
To dive deeper into the latest AI frameworks for software engineering, you should attend ODSC West. At West, you’ll not only see how these frameworks will continue to influence software engineering as a whole, but learn the skills you need in order to utilize these frameworks effectively.
The best place to learn about the fusion of AI and software engineering will be at ODCS West this October 29th-31st, specifically in the AI engineering track.
In this track, you’ll learn AI engineering from some of the world’s leading experts and top companies pioneering the AI engineering landscape. Acquire essential skills and learn the tools and frameworks to build and orchestrate AI workflows, optimizing them for enhanced efficiency, robust scalability, and effective deployment. See first-hand how AI is transforming software development.
Confirmed sessions include:
- Unlocking the Potential of People Analytics with Data
- Chronon — Open Source Data Platform for AI/ML
- Open Source For AI-Assisted Programming: Cody and Llama 3
- Creating APIs That Data Scientists Will Love with FastAPI, SQLAlchemy, and Pydantic
- Using APIs in Data Science Without Breaking Anything
- Gen AI in Software Development. What should you be looking for?
Originally posted on OpenDataScience.com
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