Advances in AI and HPC bring up significant challenges to efficiently handle huge volumes of data and convert to meaningful insights and actions. In this talk, we highlight the accelerated AI SW, packages and libraries that Intel optimizes and enables to create an efficient and scalable AI solution. Typical end-to-end AI pipelines comprises of many stages – data ingestion, data cleaning, data preprocessing, feature engineering, model training & inference deployment and visualization. Each stage must be highly performant and seamlessly integrate with each other to deliver an optimized solution for real world scenarios. Intel optimizations run the gamut across high performing python libraries and optimized Machine Learning (ML ) and Deep Learning (DL) frameworks to streamline data science pipelines across single and multiple nodes. We enable our new gen-to-gen hardware features, capability to work on large data sets in-memory, security and other features through Intel-optimized software packages such as Intel® Distribution of Python, Intel® Optimized Modin, Intel® Optimized Sklearn, Xgboost as well as DL Frameworks such as Intel® Optimized Tensorflow and Intel® Optimized Pytorch. These higher-level popular APIs and packages are built on top of a variety of oneAPI Intel libraries such as oneDAL, oneDNN, oneCCL etc., that enable better thread parallelism, higher cache reuse, suitable memory layouts, low-precision support (Bfloat16 and Int8 through Intel® DLBoost) and more. We will share key customer use cases and success stories on real-world Xgboost, End-to-End ML and DL workloads using our accelerated AI SW while improving performance. Learn these optimizations and packages through Intel AI Analytics toolkit, Intel AI container portal and realize performance and efficiency benefits. Meena Arunachalam, Principal Engineer, Intel Architecture, Graphics and Software at Intel Corporation