AI Engineer for Data Scientists

Take your models to the next level and learn state-of-the-art architectures for modeling text and images with deep learning.Discover how to build models for common computer vision tasks, including image classification, object recognition, image segmentation, and image generation. Build sophisticated Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), and utilize open-source pre-trained models available in torchvision.Discover how to build models for key NLP tasks, including text classification and text generation, using Recurrent Neural Networks (RNNs), transformer models, and pre-trained transformers.Training models to have real-world impact often requires a large amount of computational resources, and occasionally, specialized hardware like GPUs. You'll learn about how tools like PySpark and Hugging Face's accelerate library can help you make the most of the hardware that you have, by distributing computations!Finally, learn about how to take your models from notebook to production, a topic called Machine Learning Operations, or MLOps. You'll discover the key processes to operationalize a model into production, and also keep it there with robust monitoring.Continue your AI journey today!