A presentation at Cloud Expo Europe - Frankfurt in in Frankfurt, Germany by Horacio Gonzalez
Cloud and Artificial Intelligence industrialization Horacio González 2022-05-12
Who are we? Introducing ourselves and introducing OVH OVHcloud
Horacio Gonzalez @LostInBrittany Spaniard lost in Brittany, developer, dreamer and all-around geek Flutter
OVHcloud: A global leader Web Cloud & Telcom 30 Data Centers in 12 locations 1 Million+ Servers produced since 1999 Private Cloud 34 Points of Presence on a 20 TBPS Bandwidth Network 1.5 Million Customers across 132 countries Public Cloud 2200 Employees worldwide 3.8 Million Websites hosting Storage 115K Private Cloud VMS running 1.5 Billion Euros Invested since 2016 300K Public Cloud instances running P.U.E. 1.09 Energy efficiency indicator 380K Physical Servers running in our data centers 20+ Years in Business Disrupting since 1999 Network & Security
The many faces of AI And the people who work on it
We often identify two kinds of AI users
But there is a third one: DevOps/SRE
They speak different languages
But they need to work together
The challenge of integration Integrating AI/ML & DevOps/SRE teams, process, and tools
Many questions to answer…
Automate the end-to-end pipeline From idea to production
OVHcloud & AI Our answer to AI pipeline automation
AI solutions part of our data solutions range
Introducing OVHcloud AI Solutions AI Solutions are a complete set of managed services that provide a quick and easy way for your data scientists and developers to get ML models from idea to production
How to start with your AI pipeline?
How to start with your AI pipeline? ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency
How to start with your AI pipeline? ● ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency Clean and process your data with Data Processing solution, powered by Apache Spark
How to start with your AI pipeline? ● ● ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency Clean and process your data with Data Processing solution, powered by Apache Spark Develop your AI models with AI Notebooks, by instantly starting a Jupyter or VS Code notebook with GPU/CPU resources
How to start with your AI pipeline? ● ● ● ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency Clean and process your data with Data Processing solution, powered by Apache Spark Develop your AI models with AI Notebooks, by instantly starting a Jupyter or VS Code notebook with GPU/CPU resources Train your AI models with AI Training, without complex setup configuration, allowing CPU/GPU parallelization
How to start with your AI pipeline? ● ● ● ● ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency Clean and process your data with Data Processing solution, powered by Apache Spark Develop your AI models with AI Notebooks, by instantly starting a Jupyter or VS Code notebook with GPU/CPU resources Train your AI models with AI Training, without complex setup configuration, allowing CPU/GPU parallelization Deploy your AI models in production or pre-trained models or even applications very easily with AI Apps
How to start with your AI pipeline? ● ● ● ● ● Centralize your data within a data lake such as Object Storage, to benefit from high throughput with reduced latency Clean and process your data with Data Processing solution, powered by Apache Spark Develop your AI models with AI Notebooks, by instantly starting a Jupyter or VS Code notebook with GPU/CPU resources Train your AI models with AI Training, without complex setup configuration, allowing CPU/GPU parallelization Deploy your AI models in production or pre-trained models or even applications very easily with AI Apps These services are built on our trusted and secured Public Cloud based on European values & law and powered by Openstack open source cloud
Pay as you go, simple, and aggressive pricing Object storage what’s the cost of 50 hours of notebook with 1 x NVIDIA V100 GPU? Pay per GB, starting at: 0,01 € HT /month /GB to store + 0,01€ HT /GB traffic OUT 87 € 121 € 161 € GCP Azure 195 € (example: 10TB = 100€ HT/month) GRA + BHS AI Notebooks / AI Training Pay per GPU per minute, starting at: 1,75€ /hour /gpu (NVIDIA V100s 32GB) Pay per CPU per minute, starting at: 0,03€ /hour /cpu (Intel Xeon 1vCPU + 4GB) OVHcloud AI-standard 1 x V100S 32GB Standard_V100 Standard_NC6s_v3 1 x V100 16GB 1 x V100 16GB AWS P3.2xlarge 1 x V100 16GB Prices in EU datacenters, without storage attached, no period commitment.
An answer to many AI questions… We need to setup / use AI environments easily & quickly. Infrastructure? Not our job. Managed services, no setup cost, no sysadmin skills required Our team needs to collaborate and have guaranteed access to resources. Object storage sync & linked to GPUs or CPUs Our needs for resources are evolving. We need scalability and pay as we grow. Simple & predictive Pay as you Go Per minute How do we guarantee the privacy, security and compliance of our data and models in the Cloud? GDPR compliant, ISO 27K1, HDS certifications
OVHcloud & AI: sum-up 4 3 2 1 For everyone, everywhere Available anywhere in the world. Can be launched by anyone in self-service Made with communities Working closely with worldwide AI communities and partners. European sovereignty European legislation, powered by open source End-to-end AI offering Built on 20 years of cloud experience. With an open-source mindset
That’s all, folks! Thank you all!
Nowadays Artificial Intelligence (AI) and Machine Learning (ML) are part of our daily lives thanks to a multitude of applications: facial recognition, digital assistants, automatically generated subtitles …
One of the main challenges faced by companies that want to deploy AI and ML applications is to make two quite opposite worlds coexist. On the one hand, data scientists, experts in algorithms, mathematics and research methodology. On the other hand, DevOps, with its paradigm focused on the platform, automation, instrumentation and processes. These two worlds find it difficult to understand each other.
And it is in this context that a new generation of tools and services in the Cloud appears to bridge the gap between these two cultures, and automate end-to-end the pipeline of models and data.
In this talk we will talk about some of these tools and show examples of how to incorporate them into different cloud solutions from a DevOps point of view.
