Working on hands-on projects sets candidates apart. Projects serve as opportunities to use what has been learned and to improve problem-solving abilities. Some great project ideas: You could guess stock prices with old data, sort pictures with some fancy computer vision, suggest movies people might like, or build a system to catch spam using language tricks.
Websites like Kaggle offer datasets and machine learning contests, allowing engineers to gain work experience. Putting your projects on GitHub is a great way to showcase what you can do with code and how effectively you collaborate with others. Knowing the popular tools is a must. TensorFlow and PyTorch? Great for machine learning. They both bring a lot to the table.
If you figure out how to use them, you can create and train some really complex models with deep learning. It’s also a good idea to become familiar with Jupyter Notebooks for experimenting with code.
Understanding Git is extremely helpful when collaborating on projects with others. Knowing a bit about cloud services, such as AWS or , will enable you to train and share larger models. These tools streamline development workflows and improve productivity in professional environments.
Engineers should also understand how implementing solutions works. Once a model is complete, it has to be put to work. If an engineer learns how to put models to work using APIs or web apps, that’s a great skill. Tools such as Flask or FastAPI help make machine learning models available online.
Containerization technologies, such as Docker, guarantee consistent performance across different environments.
