Build Smarter, Faster

Build Smarter,
Faster

Build Smarter, Faster

Harness tools that streamline end-to-end data science on Intel® Xeon® Scalable processors. Then, plug and play with the domain-specific accelerators and innovative technologies you need from Intel’s comprehensive lineup. They're built on a common open standard (oneAPI) to minimize switching costs – making it easier and faster to build and deploy smarter models into every ...application. Streamline Data Science Software incompatibility can be a key slowdown issue, leading Intel to find solutions that fit a range of developer needs. Data practitioners can harness more data with less hassle by using software that streamlines data science from ingest to deployment on Intel® Xeon® Scalable processors, and further simplify AI and analytics with access to exclusive Intel® Optane™ technology. Analytics Zoo is a unified analytics and AI platform for distributed TensorFlow, Keras, and PyTorch on Apache Spark/Flink & Ray. This platform offers seamless scaling to thousands of nodes and up to terabytes of memory per node. Analytics Zoo in the Data Pipeline: AI ANALYTICS TOOLKIT If you prefer a Python-based data science environment, you’ll want to download the Intel® AI Analytics Toolkit, which supports terabytes of memory per node and TensorFlow/PyTorch acceleration. The AI kit enables infinite scaling from your laptop to the cloud. It also accelerates popular XGBoost and scikit-learn ML libraries and performs auto-quantization of models to int8/bfloat16 for inference deployment. Intel® AI Analytics Toolkit in the Data Pipeline: Last but not least, if you’re a developer looking for a simple way to deploy pre- trained models in your apps with high performance from edge to cloud, then the OpenVINO™ toolkit is your new best friend. Deploy Anywhere easily with OpenVINO™ toolkit Customer Spotlight – Burger King Burger King, a global fast-food leader, streamlined development of a deep learning-based fast food recommender system using Intel’s Analytics Zoo toolkit. Their goal: improve the customer experience and sales. Burger King developed customized recommendations that considered two types of information. The first was guest ordering behavior. The second was context information like weather, time, and location. For example, you wouldn’t want to feature ice cream during a winter storm in Alaska at night. Their recommender system integrates Spark data processing and distributed MXNet training, using Ray, in a unified pipeline on a single Intel® Xeon® processor cluster. This approach eliminates the overhead of costly accelerators and managing a mixed environment. Customer Spotlight – SK Telecom SK Telecom, the largest mobile operator in South Korea, manages more than 400,000 cell towers and handles 1.4 million records every 1 second . To analyze the massive amount of data, SK Telecom and Intel engineers built an end-to-end network AI pipeline for network quality prediction using Analytics Zoo and FlashBase. The entire pipeline (from FlashBase to Spark DataFrames to TensorFlow) runs on a unified Intel® Xeon® Scalable processor-based server cluster, with Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and Intel® Deep Learning Boost. This AI pipeline outperforms SKT’s legacy GPU-based implementation 2 by up to four times, and six times for deep-learning training and inference respectively. This performance enables SK Telecom to more quickly forecast and detect degradation and abnormal changes in network quality. Customer Spotlight – HYHY The quick spread of COVID-19 represented a sudden and very demanding challenge to the rapid diagnostic capabilities of medical institutions. Huiyi Huiying Medical Technology (HYHY) specializes in developing computer Read the full Build Smarter, Faster.