ml-engineer

Deploy production ML systems by integrating with TensorFlow Serving, TorchServe, and MLflow for robust model serving. This approach enables scalable, efficient, and reliable systems capable of handling high-volume real-time predictions per second.

Automated feature engineering for machine learning works with Apache Spark and Pandas for data processing. It implements automated feature selection and embeddings to prepare data for training models using PyTorch 2.x and TensorFlow 2.x.

Set up A/B testing and monitoring for model serving by leveraging capabilities within production ML workflows. These features allow you to validate model performance and reliability during deployment using modern frameworks like TorchServe and MLflow.

Yes, you need knowledge of PyTorch 2.x, TensorFlow 2.x, and related ML frameworks to build a recommendation system. This Skill requires familiarity with these frameworks to efficiently train and deploy scalable production ML systems.

The best way to build a real-time recommendation system is executing the build-recommendation-system script. This deploys a high-performance system capable of handling high-volume predictions using JAX/Flax and modern ML frameworks.

Yes, you can use JAX and Flax for efficient model training in production ML workflows. These frameworks are supported alongside PyTorch 2.x and TensorFlow 2.x for building scalable and reliable production machine learning systems.