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Most AI projects start with a Python notebook running on a single laptop;however, one usually needs to go through a mountain of pains to scale it tohandle larger dataset (for both experimentation and production deployment).These usually entail many manual and error-prone steps for the data scientiststo fully take advantage of the available hardware resources (e.g., SIMDinstructions, multi-processing, quantization, memory allocation optimization,data partitioning, distributed computing, etc.). To address this challenge, wehave open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ underApache 2.0 license (combining the original BigDL and Analytics Zoo projects);using BigDL 2.0, users can simply build conventional Python notebooks on theirlaptops (with possible AutoML support), which can then be transparentlyaccelerated on a single node (with up-to 9.6x speedup in our experiments), andseamlessly scaled out to a large cluster (across several hundreds servers inreal-world use cases). BigDL 2.0 has already been adopted by many real-worldusers (such as Mastercard, Burger King, Inspur, etc.) in production.

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster