Getting started with AI

Getting started with Artificial Intelligence

From the smallest startups to the largest enterprises alike, organisations are using Artificial Intelligence and Machine Learning to make the best, fastest, most informed decisions to overcome their biggest business challenges.

But with AI/ML complexity spanning infrastructure, operations, resources, modelling and compliance and security, while constantly innovating, many organizations are left unsure how to capture their data and get started on delivering AI technologies and methodologies.

Now is the time to take the plunge. Whether on-prem or in the cloud, you can establish an AI strategy that connects to your business case, forming a scalable AI solution that is focused on your particular data streams.

Whitepaper highlights:

  • Key concepts in AI/ML
  • Factors to consider and pitfalls to avoid
  • Roles, skill sets and expertise needed for success
  • Infrastructure and applications for multi-cloud operations for the full AI stack
  • Building a readiness plan to deliver AI insights powered by your data: discovery, assessment, design, implementation and operation and feedback

To view the whitepaper complete the form below:

Ubuntu cloud

Ubuntu offers all the training, software infrastructure, tools, services and support you need for your public and private clouds.

Newsletter signup

Select topics you’re
interested in

In submitting this form, I confirm that I have read and agree to Canonical’s Privacy Notice and Privacy Policy.

Related posts

Lessons learned from 100+ private cloud builds

Building a private cloud based on OpenStack has typically been a complex process with uncertain build costs based on time and materials requiring specialised...

Open Infrastructure Summit Shanghai 2019: the highlights

The Canonical team is getting back from the Open Infrastructure Summit Shanghai 2019 with a lot of excitement and a fresh view on the key projects from the...

Canonical collaborates with NVIDIA to accelerate enterprise AI adoption in multi-cloud environments and at the edge

Enterprises currently face the challenge of how to adopt and integrate AI and ML into their operations effectively, at scale and with minimum complexity. In...