Name: How to build and deploy your first AI/ML model on Ubuntu
Start: 2018-10-17T16:00:00Z
End: 2018-10-17T16:00:48.000Z
Location: BrightTALK
Rating: 4

Get the most in depth information about the Ubuntu technology and services from Canonical. 

Learn why Ubuntu is the preferred Linux platform and how Canonical can help you make the most out of your Ubuntu environment.

Ensure data security and privacy in AI applications that employ Large Language Models (LLMs).

About the Webinar
As generative AI becomes increasingly vital for enterprises – especially in applications such as chatbots utilizing Retrieval-Augmented Generation (RAG) systems – ensuring the security and confidentiality of data within these frameworks is essential.

Our upcoming genAI security webinar will address the significant challenges related to data security and privacy in AI applications that employ Large Language Models (LLMs).

During this webinar, we will introduce confidential computing as a method for safeguarding data, with a specific focus on its application within RAG systems for securing data during usage or processing. Additionally, we will outline best practices for implementing confidential computing in AI environments, ensuring that data remains protected while still enabling advanced AI capabilities.

GenAI security with confidential computing

Join our technical demo where Andreea Munteanu, AI/ML Product Manager and Kimonas Sotirchos, Kubeflow Software Engineer, will:

* Talk about key considerations when building machine learning pipelines
* Learn how to build ML pipelines using Kubeflow
* Give insight of some best practices when using Kubeflow pipelines

More information about the webinar: 

Machine Learning Operations is a new practice that ensures machine learning projects run in an automated and reproducible manner. It is often described as DevOps for machine learning, since it brings together ML development and the operations that are required afterwards to maintain any ML initiative. MLOps has three core components: data pipelines, model pipelines and applications pipelines, which developers are often looking to optimize.

What are ML pipelines?

ML pipelines refer to the pipelines of all three components of the machine learning lifecycle and enable developers to automate ML workloads, streamlining the process of taking models to production. They are a foundational aspect to scaling machine learning projects and running them in a more effective manner.

There are multiple ways to build ML pipelines. Open source tools such as Kubeflow are an easy option, as they have already built the engine to perform such tasks. Kubeflow Pipelines are one of Kubeflow’s components which can be used to build ML pipelines.

Join our webinar to explore how to build machine learning pipeline with Kubeflow.

Build your machine learning pipeline with Kubeflow

Join is for a deep dive into group replication, clusterSet, and multi-cloud database architectures.

About the Webinar

Public cloud providers offer convenient services for database deployments. However, depending solely on a single provider's database-as-a-service can result in vendor lock-in and reduce your system’s resilience against widespread outages affecting the provider.
Instead, explore our approach to deploying MySQL across multiple clouds. A multi-cloud database strategy enables you to retain control of your infrastructure while enhancing overall resiliency and leveraging the unique strengths of each cloud provider.

Register to our webinar and learn more about:
MySQL Group replication
MySQL ClusterSet technology  
Our reference architecture for multi-cloud MySQL deployments

Navigating multi-cloud database deployments with MySQL for maximum uptime

Automotive is a highly regulated industry going through important changes towards software-defined vehicles (SDVs). Innovation is vital to meeting the new challenges and demands of the automotive industry as it evolves around software-defined vehicles – but this innovation cannot come at the expense of vehicle safety and cybersecurity. 

Canonical’s teams of support engineers, software developers and customer success professionals, have allowed us unique insights into the challenges that modern automotive leaders face. Join our upcoming webinar to learn from our experiences in supporting and enabling automotive software security and innovation.

Spotlight on automotive: Ensuring business continuity and long-term customer success

We’ve all heard the anecdotes of data scientists spending more time on tooling than ML models. There is no smoke without fire, right? The truth is AI/ML practitioners struggle with their environments for various reasons, including tooling fragmentation, package dependencies and access to computing power.

Data Science Stack (DSS) is an out-of-the-box solution for data scientists and machine learning engineers, published by Canonical. It is a ready-made environment for ML enthusiasts that enables them to develop and optimize models without spending time on the necessary underlying tooling. It is designed to run on any AI workstation that runs Ubuntu, maximizing the GPU’s capability and simplifying its usage.

Join us for this webinar to learn more about data science tools, with a focus on DSS and its capabilities. During the webinar, Michal Hucko, MLOps engineer at Canonical and Andreea Munteanu, AI & MLOps Product Manager, will talk about:

- Key considerations when getting started with data science
- Data science through the open source lens
- Deep dive into Data science stack (DSS)
- Demo of the DSS
- Prepare your questions and join us live to get insights into how DSS improves the developer experience for data science and ML projects on Ubuntu.

Introducing Canonical Data Science Stack GA: set up an ML environment with 3 commands on Ubuntu

Are you facing challenges delivering and maintaining secure and compliant desktop environments to employees worldwide on your AWS environment? Traditional VDI solutions can be complex and expensive. Ubuntu Desktop on Amazon WorkSpaces offers a streamlined, cost-effective alternative. By logging into a desktop from any device, you can ensure consistent access to your applications and data, while maintaining the highest security standards. 

Ubuntu on WorkSpaces benefits from Ubuntu Pro which include access to Canonical’s Landscape solution. Landscape is a powerful management tool that simplifies the administration of Ubuntu Pro instances on Amazon WorkSpaces at scale. It automates critical tasks like configuration management, patching, auditing, access control, and compliance, enabling your systems to remain secure, compliant and up to date. Whether you're dealing with well-connected or air-gapped environments, Landscape offers flexible deployment options.

In this webinar, we will explore how Landscape can help streamline the management capabilities required to support a distributed, remote workforce utilizing Ubuntu on Amazon WorkSpaces.

Simplifying Ubuntu Desktop Management on Amazon WorkSpaces with Canonical Landscape

Developing an AAOS-based system comes with challenges like hardware dependencies, logistical delays and the need for vehicle and OEM specific customisation. Join us for an exclusive webinar where we explore how Anbox Cloud is solving these challenges and transforming the world of Android Automotive development. Designed to streamline and enhance the development process, Anbox Cloud enables developers to efficiently deploy, test, and scale Android apps across various configurations, eliminating the traditional hardware dependencies that slow down the development process.

Unleashing Android Automotive development with Anbox Cloud

OpenSearch is an open source search and analytics suite that developers use to build solutions for search, data observability, data ingestion, Security Event and Information Management (SIEM), vector databases, and more. It is designed for scalability, offering powerful full-text search capabilities and supporting various data types, including structured and unstructured data.
This webinar is designed to introduce participants to the basics of OpenSearch, a powerful and flexible search engine, and OpenSearch Dashboards, the associated analytics and visualisation platform.

By the end of this webinar, you will be able to:
-- Understand the basic architecture and core concepts of OpenSearch
-- Set up and configure an OpenSearch cluster
-- Install and navigate OpenSearch Dashboards
-- Create and customise dashboards to visualise data effectively
-- OpenSearch in production best practices
-- How to make it easier for you to manage OpenSearch in production

This webinar has been made for:
-- Developers who want to implement search functionality in their applications
-- Data Analysts looking to create visualisations and gain insights from large datasets
-- System Administrators responsible for managing and scaling search clusters
-- Business Intelligence Professionals interested in exploring new tools for data analysis

Getting started with OpenSearch (search engine) and OpenSearch Dashboard

AI is at the forefront of innovation. It changes the shape of all industries, challenging organisations to rethink their processes and adjust their infrastructure. Enterprises are looking nowadays to leverage their  existing computing power to accelerate model training and use tools such as Kubernetes for container orchestration or Kubeflow for workload automation in order to move their ML projects to production. 

In this webinar, you will:
- Learn what are the challenges with running AI on top of K8s
- Learn how to schedule your ML workloads
- Discover why K8s is a good fit for AI projects
- Leverage Kubernetes operators and schedulers to accelerate project delivery
- Understand better the cloud-native applications ecosystem and how it enhances AI/ML projects
- Have a clear path of taking ML projects to production with a fully open source solution

Optimise your ML workloads on Kubernetes

Are you a .NET developer looking to optimise your development and deployment processes? Join our exclusive webinar, where we dive deep into the seamless integration of .NET on Ubuntu, offering you the tools and insights needed to enhance your development experience.

Join this webinar to learn more about:
- Expert Guidance: Learn directly from industry experts at Canonical and Microsoft who have spearheaded the integration of .NET on Ubuntu.
- .NET on Ubuntu 24.04
- What are chiselled Ubuntu containers?
- How do chiselled containers address container security challenges?
- Chiselled .NET and what it means for .NET developers
- Chiselled .NET 8 demo

Accelerate .NET development and deployment on Ubuntu

The Cyber Resilience Act (CRA) is a European Union legislation that will enter into force in 2027. Its overall goal is to make devices safer by implementing more rigorous cybersecurity, documentation, and vulnerability reporting requirements for the IT industry. The CRA is especially relevant for device manufacturers, who will need to ensure devices are secure throughout the product lifecycle. 

Join us for a 45 minute webinar, where Joao Pezzato Hellmeister and Jean-Charles Verdié will share insights on how this regulation will impact device manufacturers and how Canonical’s Ubuntu Pro for Devices can help you ensure compliance. 

We will zoom into:
- An overview of the vulnerability management obligations mandated by the CRA and what they mean for your software stack
- Implications for device management
- How a hardened attack surface can help you minimise threats and simplify compliance

Understand how the Cyber Resilience Act will impact device manufacturers

The Cyber Resilience Act is an upcoming European Union legislation that aims to implement more rigorous cybersecurity commitments, documentation, and vulnerability reporting requirements for the IT industry. The legislation would apply to developers, distributors, manufacturers and retailers of hardware, devices, software, applications, and other “products with digitally connected elements” sold within the EU. The requirements you need to meet depend on the CRA classification of products. 

Although the regulation does not enter into force until 2027, organisations need to start preparing now. Join us for a 30-minute webinar to understand how this new regulatory framework will impact the compliance landscape, and what it could mean for you. We will delve into the CRA’s 4 key obligations:

1. Risk Assessment
2. Documentation
3. Conformity assessment
4. Vulnerability reporting

We will also provide recommended actions so you can implement a roadmap for compliance in your organisation.

What is the Cyber Resilience Act?

In this webinar, Henry Coggill will showcase USG (the Ubuntu Security Guide) and demonstrate how it can automate compliance requirements. We will discuss the hardening profiles that are available, including DISA-STIG and CIS benchmarks, then cover setting up and configuring the tool and demonstrate the configuration options that you can make for maximum security and coverage of the STIG rules.

USG is available with an Ubuntu Pro subscription, and we will show you what extra features and capabilities that are included with Pro, such as extended security vulnerability patching and FIPS-140 certified crypto modules.

STIG hardening on Ubuntu 22.04 with Ubuntu Security Guide

Data lake is a very large scale data processing paradigm that disrupts the conventional data warehousing model. Data warehouses require all data to be structured and stored in a relational database, which can be inflexible and may require significant upfront data processing using extract-transform-load (ETL) technologies.

Data lakes can offer greater flexibility whilst retaining the benefits and efficiency of centralised data governance. With Canonical OpenStack private cloud platform, Kubernetes and Charmed Spark solutions, your data lake architecture can also benefit from extended flexibility and scalability whilst remaining cost effective to operate.

Join this webinar to learn more about the benefits of the data lake architecture, and how you can efficiently adopt this technology at scale using modern private cloud technology.

A data lake on your cloud with Spark, Kubernetes and OpenStack

Confidential computing is a revolutionary technology that disrupts the conventional threat model of public clouds. In the past, vulnerabilities within the extensive codebase of the cloud’s privileged system software, including the operating system and hypervisor, could pose a constant risk to the confidentiality and integrity of code and data in operation. Likewise, unauthorized access by a cloud administrator could compromise the security of your virtual machine (VM). 

To protect your data and code against such threats on Google Cloud, Ubuntu Confidential VMs (CVMs) offer enhanced security for your workloads by utilizing hardware-protected Trusted Execution Environments (TEEs). To achieve such strong security guarantees, Ubuntu Confidential VMs build on Intel’s innovations at the silicon level, and the foundation laid by Intel(R) Trust Domain Extensions (Intel(R) TDX), now available through Google Cloud.  Ubuntu also offers further primitives to further secure your CVMs at rest, and offer a strong remote attestation.

Come join this webinar to learn about the great engineering that goes into making Ubuntu confidential VMs with Intel TDX on Google Cloud, and how you use them to implement a zero trust strategy for your security-sensitive application.

Exploring Ubuntu Confidential VMs with Intel TDX on Google Cloud

Across the world, the application of Artificial Intelligence and Machine Learning (AI/ML) to business challenges is a hot topic. But, what does it mean for storage systems, and how can open source storage answer those needs? Without data, and speedy access to it, AI solutions might not deliver the results as quickly as expected, delaying insights and innovation. Canonical Ceph is a great example of a highly scalable open source storage solution that can be used to back modern AI/ML workflows and unlock the full potential of your AI ecosystem.
In this webinar we will explore typical use cases for external storage for AI systems, the workflows, the impact AI has on storage systems, and also the impact that storage systems can have on your AI jobs.

AI Storage with Ceph

Welcome to AI Summer Camp. 

Join 5 essential trainings on AI from Canonical experts and level up your skills while on holidays. 

 

Episode 5: AI on private cloud - why is it relevant in the hyperscalers era?

The webinar will cover:

Why consider a private cloud for AI? 

Key considerations when building a private cloud for AI projects 

Performance acceleration options for private cloud

Guidance for Kubernetes on OpenStack for AI initiatives

AI on private cloud - why is it relevant in the hyperscalers era?

Welcome to AI Summer Camp. 

Join 5 essential trainings on AI from Canonical experts and level up your skills while on holidays. 

 

Episode 4: AI on public cloud with open source

The webinar will cover: 

Scenarios in which open source tooling on public cloud solves a problem for AI initiatives 

Main benefits of using open source tooling on the public cloud for AI projects 

Use cases from our customers 

Hybrid cloud and multi cloud opportunities for AI projects

AI on public cloud with open source

Learn how to get started with AI/ML on Ubuntu with this webinar that will walk you through running a Kaggle experiment on Kubeflow on Microk8s on Ubuntu. These highly reproducible instructions can be used for any machine learning exercise, in addition to any Kaggle competitions. You’ll be able to run your experiments locally or in the cloud. 

In this webinar, join Canonical’s Kubernetes Product Manager Carmine Rimi for a demo with instructions for everything from Ubuntu, with multiple examples of how to get an AI/ML environment on Ubuntu environment up and running.

At the end of the exercise, you’ll end up with a summary of commands needed to quickly and efficiently launch your machine learning environment.

How to build and deploy your first AI/ML model on Ubuntu

Machine Learning

Kubernetes

Ubuntu

Kaggle

Deployment

Artificial Intelligence

Cloud

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Cloud Computing

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How to build and deploy your first AI/ML model on Ubuntu

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