The world's enterprise is awakening to the power of AI.
What AI and machine learning applications will the world's enterprises need?
How will today's enterprise computing environment accommodate the new model of computing – scale-out and microservices?
For the rest of my talk, I will explore some of these.
One of the most important AI models is the recommender – it is the engine of e-commerce, digital ads, social media, news readers, music and
movie services, user generated content platforms, and even search.
Learning from data, a recommender will learn your explicit and implicit preferences, to recommend the trillions of physical and digital items in the world to billions of people.
Recommenders are extreme data processing and machine learning systems.
Large scale services can be collecting petabytes of data a day from interacting with hundreds of millions of users and connecting them to
the continuous stream of new news, new video, and new goods.
Data engineering teams process the data into data frames.
Data scientists engineer from structured data or using deep learning to learn features from unstructured data that may be predictive of your preferences.
The MLOps teams deploy the models at-scale.
New model generates new interactions that will be used to update the model.
Meanwhile, new features are being engineered and new AI models are being explored.
The more predictive, the greater the click-through-rate. A 1% improvement in accuracy can translate to billions more sales or better customer retention.
The recommender system consumes entire data centers.
Iteration loops can be weeks. Companies understandably want it to be hours.
NVIDIA Merlin is the first end-to-end accelerated recommender system ever created.
Merlin is superfast and scalable.
Scalable to as large as you like, it is plumbed from data load, to training, to inference. And with v0.2, we can load data directly from
AWS, GCP, Azure, and Apache Hadoop File System.
We've gotten great feedback on Merlin.
Tencent used Merlin to train a recommender for their video platform.
Their cycle time went from a day to three hours.
The increased cycles of learning allowed them to increase their model accuracy.
Merlin is now in Open Beta.
The core engine of Merlin is NVIDIA RAPIDS, and is fast and scalable.
RAPIDS is the fastest ETL engine on the planet – and it supports multi-GPU and multi-node.
Its API is modelled after Pandas, XGBoost, and ScikitLearn, the hugely popular data science frameworks, so RAPIDS is easy to pick up.
On the industry standard data processing benchmark, running 30 complex database queries on a 10TB dataset, a 16 DGX cluster ran 20 times
faster than the fastest CPU server and is 1/7th the cost and 1/3rd the power.
RAPIDS has been downloaded nearly 500,000 times, with this year nearly 3 times the download over last year.
Adobe is using RAPIDS as the data engine for their intelligent marketing service.
Capital One is using RAPIDS for their critical business operations like fraud detection and chatbot.
Data processing is foundational to all enterprises in the data-driven era.
While some companies are just starting to use deep learning, nearly every company is doing data processing with exponentially growing scale.
Industry leading data science and machine learning platforms and cloud services are integrating RAPIDS.
Cloudera is a leading enterprise data analytics software company. Their platform runs on over 400,000 data center CPU nodes.
Over 2,000 enterprise customers use Cloudera globally.
They serve 8 of the top 10 financial services companies, all 10 of the top 10 telcos, and all 10 of the top 10 healthcare institutions globally.
Cloudera is a hybrid-cloud data platform that lets you manage, secure, analyze, and learn predictive models from your data – the entire lifecycle of your data.
Today we are announcing Cloudera will accelerate their data platform with NVIDIA RAPIDS, NVIDIA AI, and NVIDIA-accelerated Spark.
It will be called Cloudera Data Platform powered by NVIDIA.
Several thousand engineering years of work will instantly be available to their over 2000 enterprise customers.
Notice the lower right box of the Cloudera Data Platform – the Private Cloud.
There are two types of computing environments – bare metal for scale-out and virtualized multi-tenant.
Bare metal is essential to achieve multi-GPU, multi-node scale-out needed for large data processing or training.
Scale-out and virtualized computing cannot share one infrastructure today.
This creates two distinct infrastructures that are managed and operated differently, not sharable, and more difficult to secure.
There is a solution.
NVIDIA and VMware are announcing a second partnership.
We are going to do some serious computer science to create a data center platform that can support GPU-acceleration for all three major
domains of computing today – virtualized, distributed scale-out, and composable microservices – all NVIDIA accelerated.
Enterprises running VMware will be able to enjoy NVIDIA GPU and AI computing in any computing mode.
This is a massively exciting project and every bit as exciting as when VMware first virtualized the data center.
VMware and NVIDIA are going to bring AI to the world's enterprises.