Machine learning (ML) has become an essential part of the modern world, with applications ranging from recommendation systems to image recognition. As a result, there is a growing demand for machine learning professionals in various industries. If you are an academic researcher looking to transition into the industry, it is essential to have experience with the right programming languages and libraries. In this article, we will discuss the different machine learning frameworks which allow developers to build and implement machine learning models and the frameworks that are good to have experience with when transitioning from academia to industry.
We spoke to ML industry experts who have provided us with their opinions on these frameworks and their insights into using them.
- Dr. Robert Singh Joseph Chin – Principal ML Engineer
- Dr. Manolis Vasileiadis – ML Engineering Manager
- Andrey Avtomonov – CTO & Co-founder
- Venkat Ganesh – VP Computer Vision and Cloud AI
- Laila Kramer – Head of People
- Dr. Thomas Wollmann, - VP Machine Learning Engineering
Python is undoubtedly the most popular language for machine learning in the industry. Venkat Ganesh who is a VP of Computer Vision and Cloud AI told us that “Python is a necessity as a machine learning language.” He also mentioned that C++ could be useful to learn but will mostly be more dominant in the robotics field.
Nearly all industry experts we spoke to said that Python is easy to learn and has a vast array of libraries and frameworks that make building machine learning models faster and more efficient. Laila Kramer, Head of People agrees stating, “python is a great language as it is easy to build and experiment with, that is why it is the most widely used in comparison to other languages.”
Python's popularity in the industry is due to its versatility, which allows it to be used for data pre-processing, model training, and deployment. Venkat Ganesh tells us that for the industry “Python is a necessity as a machine learning language.” Manolis Vasileiadis, ML Engineering Manager believes that “understanding 90% of Python is key to succeeding in machine learning languages.”
NumPy and Pandas
NumPy and Pandas are two essential libraries that every machine learning professional should know. Venkat Ganesh, “native python libraries can be built on to learn other frameworks such as NumPy and Pandas.” NumPy is a library for scientific computing that provides support for large multi-dimensional arrays and matrices. Pandas is a library for data manipulation and analysis that provides support for tabular data structures. Together, these libraries form the foundation of most machine learning workflows and are crucial for data pre-processing and cleaning.
Scikit-learn is another essential library for machine learning in Python. It provides a range of machine learning algorithms, including classification, regression, and clustering, as well as tools for model selection and evaluation. Scikit-learn is widely used in the industry and is an excellent choice for building simple machine learning models quickly.
TensorFlow is an open-source machine learning library developed by Google. It is widely used in the industry for building deep learning models, including neural networks, convolutional neural networks, and recurrent neural networks. TensorFlow is also highly optimised for performance, making it an excellent choice for large-scale machine learning applications.
Manolis Vasileiadis believes that “TensorFlow has been a great language for deep learning models but recently Pytorch has taken over as the go-to to use.” Dr. Robert Singh Joseph Chin, a Principal ML Engineer still sees TensorFlow’s uses mentioning “it is great for extracting using Keras.”
Keras is a high-level neural networks API, written in Python. It provides a user-friendly interface for building and training deep learning models and is built on top of TensorFlow. Keras is easy to use and is an excellent choice for beginners in deep learning.
PyTorch is an open-source machine learning library developed by Facebook. It is similar to TensorFlow in functionality but provides a more Pythonic interface. PyTorch is widely used in the industry for building deep learning models and is an excellent choice for those who prefer a more Pythonic way of doing things. Dr. Robert Singh Joseph Chin considers “Pytorch [as] a good base tool. It is straightforward to learn and more of an easier transition from Python”.
Andrey Avtomonov, CTO & Co-founder, warns us that “machine learning operators are now stricter on what languages are used, especially when collaborating with data science in large companies.” Having experience with the right machine learning languages and libraries is essential when transitioning from academia to industry.
However, is experience the only way to evolve in the industry? Dr. Thomas Wollmann, VP of Machine Learning Engineering, states “most frameworks can be learnt through a course, but mastering them needs hands-on experience. Moreover, for advanced use cases, deeper understanding of the theory and concepts behind them is necessary.
Python is the most popular language for machine learning in the industry, and NumPy and Pandas are essential libraries for data pre-processing and cleaning.
Scikit-learn is an excellent choice for building simple machine learning models, while TensorFlow and PyTorch are great for building deep learning models. Finally, Keras provides a user-friendly interface for beginners in deep learning. By having experience with these languages and libraries, you can increase your chances of success when transitioning from academia to industry.
If you are looking for resources to begin your ML career, Dr. Robert Singh Joseph Chin has given us some recommendations to help get you started.
“Kaggle is a wonderful place to learn from and see how people apply ML, and more importantly why not to apply certain ML algos”