Name: Introduction to MLFlow, the open-source tool for machine learning
Start: 2023-09-26T16:00:00Z
End: 2023-09-26T16:00:46.000Z
Location: BrightTALK
Rating: 4.3

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.

IoT and edge devices are now deployed across manufacturing, agriculture, logistics, healthcare, and more. 
As these environments become increasingly interconnected, they also become prime targets for opportunistic attacks. Device makers must strengthen security at every layer, beginning with the operating system.

Join us for a walkthrough of OS-level hardening practices, based on our practical security hardening whitepaper,  that device makers can apply to their Ubuntu-based devices.

In this session you’ll discover:
- Key steps to secure connections to your devices, limit network and physical exposure, customize user access, stay up to date with security patches, and harden your kernel configuration. 
- Insights into how Ubuntu Core, Canonical’s immutable, strictly confined operating system for production devices, implements many of these protections out of the box.
- How Ubuntu Pro extends long-term security maintenance, delivers compliance and hardening profiles, and supports standards and regulations such as the EU Cyber Resilience Act.

This session is ideal for organisations preparing devices for production deployment and for teams navigating increasing regulatory and security expectations across the industry.

We will also have a live Q&A where you’ll be able to ask our experts questions on the OS-level hardening steps.

Securing Ubuntu Deployments for IoT and Edge Devices

Are you struggling with the high costs of managing your data center's physical servers? 

The manual processes of provisioning, deploying, and maintaining bare metal infrastructure are time-consuming, and can be a significant drain on your budget and IT resources. This concise 30-minute webinar will explore how to transform your data center operations from a drain on funding into a strategic asset. Join us to discover Canonical MAAS (Metal-as-a-Service): the tool that brings cloud-like agility and automation to your bare metal environment.

We will dive into:
- The hidden operational costs of traditional server management.
- How MAAS automates the entire lifecycle of your physical servers, from discovery and commissioning, to deployment and retirement.
- Real-world use cases, including efficient Kubernetes provisioning, CI/CD infrastructure, and private cloud scenarios.
- Tangible ways MAAS reduces OpEx by slashing provisioning times from days to minutes, improving hardware utilization, and freeing your team to focus on higher-value work.

Stop letting manual processes set your pace and budget. Register now to learn how you can automate your infrastructure and reclaim your time and resources.

Reduce data center costs with bare metal automation

In this walkthrough, our Product and Engineering teams will describe how Ubuntu Pro transforms the Windows Subsystem for Linux (WSL) experience, providing a secure, compliant, and fully supported developer environment for enterprises.

This talk will cover:
- What is Ubuntu Pro on WSL and why it's the optimal choice for organizations and developers operating in Windows-first environments.
- Ubuntu Pro's enterprise features on WSL, including auto-enrollment, integration with the Windows registry, and how it secures and maintains all open source packages Main and Universe repositories.
- How management and compliance are simplified through integrations like Landscape (in beta), and how to leverage existing Windows management tools like Microsoft Intune for automated fleet enrollment.
- The benefits of using Ubuntu Pro for developers, detailing how it enables secure, production-ready deployments via Pro-enabled repositories.
- A look at licensing models, including free options for personal use and paid models for enterprises, along with the simple steps to activate Pro via the Microsoft Store app.

Ubuntu on WSL in the enterprise

Join us for this session exploring how Canonical and VMware work together to enable a consistent, secure platform for modern applications.

Learn how the combination of VMware Cloud Foundation and Ubuntu gives Platform Engineers a stable base to automate, standardize, and secure their infrastructure, while providing Developers with the freedom and confidence to build and run software their way. 

See the integration in action through a live demo and discover how this approach simplifies operations across your entire stack.

VCF Platform with Ubuntu OS

Türkçe web seminerine katılın 27 Kasım 2025.
- Canonical’ın sunduğu yenilikçi açık kaynak altyapı çözümlerine genel bir bakış
- MAAS (Metal as a Service) gibi çözümlerle fiziksel sunucu kurulumundan sanallaştırmaya ve uygulama katmanına kadar tüm açık kaynak altyapı katmanları için kurumsal destek:
- Lider açık kaynak bulut bilişim platformu OpenStack’in gücü ve esnekliği
- Ubuntu işletim sistemi ve üzerinde çalışan altyapı başta olmak üzere çeşitli açık kaynak çözümleri için gelişmiş güvenlik, uyumluluk ve destek sağlayan Ubuntu Pro
- Güvenilir açık kaynak kullanılarak oluşturulmuş kurumsal yapay zeka çözümleri

Canonical ile Güvenilir Açık Kaynak

Federal organizations operate in some of the most demanding IT environments in the world, where compliance, performance, and trust are non-negotiable. Under these constraints, achieving end-to-end FIPS and STIG compliance across a Kubernetes stack is often highly complex, requiring integration of multiple components with different baselines and certifications.

Canonical’s fully compliant, full-stack Kubernetes solution is designed to solve this challenge. Built on Ubuntu Pro hosts, Canonical Kubernetes now incorporates FIPS 140-3 validated cryptographic modules and STIG hardening directly within the platform, providing a faster, more direct route to meeting FedRAMP and other federal compliance requirements. Combined with STIG-hardened hosts, this represents Canonical’s most comprehensive compliance-ready Kubernetes offering to date.

Join us to explore how Canonical’s integrated approach simplifies compliance, accelerates deployment, and delivers a secure and supported Kubernetes stack built for federal and high-security use cases.

How to secure your federal cloud native stack with Canonical Kubernetes & Ubuntu Pro

Canonical creates base images of Ubuntu that are optimized for AMD’s adaptive computing platforms such as Kria, Versal, and Zynq, so that AMD’s customers can use those as a starting point for product production deployments.  

This webinar will cover the value proposition of using these base Ubuntu images for your development processes, and then walk you through the steps of getting from there to a fully customized and optimized production solution for your SOM-based or custom chip-down designs using AMD AECG chip sets.

From prototype to production with Ubuntu on AMD adaptive computing solutions

The Open Infrastructure Foundation has joined the Linux Foundation, marking a new chapter for cloud-native infrastructure. This merger brings OpenStack and Kubernetes under one collaborative umbrella, paving the way for deeper integration and innovation.

Join our experts as they unpack what this means for the future of open source infrastructure. We'll explore how Canonical is leading the charge with innovative projects like Sunbeam, which simplifies OpenStack deployment and operations, making production-grade private clouds more accessible than ever.

In this webinar, you will learn:
1) The future of OpenStack and Kubernetes: Gain insights into the strategic alignment of these two powerful technologies under the Linux Foundation governance and what it means for the entire community.
2) Canonical's OpenStack vision: Discover how Canonical is democratizing OpenStack with the Sunbeam project, distilling OpenStack’s excellence and lowering the barrier to entry for everyone.
3) Canonical OpenStack (based on Sunbeam) architecture: Learn about Canonical's reference architecture for building robust, scalable, and enterprise cloud environments.
4) Live demo: See a live demonstration of Canonical OpenStack (based on Sunbeam), showcasing its internals and powerful features.

Join us for this webinar to learn more.

OpenStack Reimagined: Building a Cloud-Native Infrastructure

Air-gapped or offline networks are an increasingly common security requirement for deployments in regulated or federal environments and sovereign clouds. Today’s hyper-connected infrastructure often makes assumptions about remote systems being online and available, which break in these offline disconnected scenarios. Some of the most common issues revolve around installation packages and security updates not being accessible behind the air gap or firewall boundaries.

Landscape is Canonical’s systems management tool that enables administrators to control, monitor and update Ubuntu systems at scale. Managing assets is a key component of all security & compliance frameworks, and Landscape enables a range of security controls within an Ubuntu deployment. One feature of Landscape that is particularly relevant for air-gapped environments is package and repository mirroring, or the ability to copy packages and updates from an internet server and make them available to clients behind a firewall or air gap.

In this webinar we will cover what air-gapped environments are, why they might be a requirement in various scenarios, and how Landscape can be deployed and used to address the most common problems with distributing software packages and updates behind the air gap.

Using Landscape for Air-Gapped Ubuntu Deployments

Security is the foundation of modern IT, and in this webinar, Canonical and IBM will show how Ubuntu & IBM LinuxONE and IBM Z delivers enterprise-grade protection without compromising agility or cost efficiency. We’ll explore technologies that secure critical workloads: hardware-assisted cryptography, pervasive encryption for data at rest and in transit, and confidential computing that isolates sensitive applications from threats.

You’ll also learn how Canonical Livepatch minimizes downtime for essential systems, and how Ubuntu’s FIPS-certified modules and Common Criteria certifications simplify compliance in highly regulated industries. Meanwhile, IBM LinuxONE adds an extra layer of resilience, ensuring your workloads remain protected against future threats - when combined with centralized compliance and auditing capabilities (including automated controls and real-time monitoring), this solution empowers IT teams to confidently meet standards like GDPR, HIPAA, and PCI-DSS.

Join us to explore how Canonical and IBM are redefining open source enterprise security at scale, and how you can secure your infrastructure for today and tomorrow.

Security at scale: Cost-efficient Ubuntu solutions on IBM LinuxONE and IBM Z

Linux is well known for the flexibility, interoperability, and control it offers to developers. 
Building upon years of contributions from the open source community, Ubuntu is the most widely used Linux OS among professional IoT developers, according to the 2024 State of IoT Report by Eseye. 


However, using Linux on embedded and IoT devices presents a unique set of challenges compared to servers, including maintenance complexity, security risks, and deployment at scale. To address these challenges, Canonical created Ubuntu Core: a minimal, containerized, and immutable flavor of Ubuntu for embedded systems.

Like Ubuntu, Ubuntu Core is fully open source and binary-compatible, but with added features tailored specifically to the needs of devices: secure boot, full-disk encryption, confined application packages, and reliable OTA updates. Its immutable architecture simplifies compliance, enhances security, and reduces operational risk across fleets of devices.

In this webinar, we’ll cover:
Key challenges in managing IoT and edge devices at scale
Security and maintenance considerations for embedded Linux
Why Ubuntu Core is the new standard for production-grade IoT deployments

Embedded Ubuntu for devices

Explore the performance differences and future direction of two leading in-memory databases

Redis. Then Valkey. Now Redis and Valkey? 

Redis has long been a go-to choice for developers seeking an open source caching solution. However, in March 2024, Redis shifted to a source-available license, sparking the creation of several forks – one of the most popular being Valkey. A year later, Redis returned to open source under the AGPLv3 license, leaving many developers and organizations weighing their options between Redis and Valkey.

In this webinar, we’ll:
Dive into the differences between these two solutions and what those differences mean from a performance, operability and community engagement perspective 
Explore how the two projects might diverge further in the future
Explain how Canonical can support you in adopting, securing and scaling both open-source databases
Live Q&A session – bring your questions :)

Redis or Valkey?

We compare the two embedded Linux distributions against the requirements of the Cyber Resilience Act

Yocto vs Ubuntu Core for the Cyber Resilience Act

What it means for device manufacturers, how to mitigate risk and plan your path forward

Ubuntu 20.04 LTS:  End of Standard Support for Devices

Do you want to build your own cloud in your homelab? Or just have a way to simulate a cloud environment for development and testing? Join us for this webinar that will guide you through the steps needed to build a homelab, or other personal, small-scale private cloud projects. 
We’ll also introduce you to Canonical MicroCloud, a lightweight, open source cloud platform that is perfect for homelabs and small-scale deployments. MicroCloud is deployed with only one command, without the need for specialist knowledge. It also requires minimal resources and can be easily run on both commodity and high-end hardware. 

MicroCloud is a cloud for everyone. Whether you’re a technology enthusiast with a homelab or an enterprise architect with a distributed cloud to maintain, you’ll find it seamless to deploy and run.

In this session, you’ll discover:
- What a homelab is and how it empowers you to take control of your infrastructure
- The benefits of having your own private cloud, including complete data ownership, enhanced privacy and the freedom to experiment and customize
- Popular use cases, from self-hosted apps and media servers to development environments
- The tools, hardware, and steps to get started on a budget
- How MicroCloud is a great fit for homelab

Join us for this webinar to learn more.

Building a homelab with MicroCloud

You’ve probably heard of Linux, but maybe you’re not quite sure what it is, what you can do with it, and, more importantly, whether you can trust it to run your next project! Perhaps you’re keen to explore the use of open source software within your organization but have questions about its security and suitability for the enterprise.
In this webinar, Canonical’s Ijlal Loutfi and Henry Coggill discuss the security of Linux and Ubuntu. We’ll start with the basics of why the operating system is important and how it underpins the integrity of any applications running on top. We will provide background information as to what Linux is, for those who might be unfamiliar with its history, and discuss how open-source software is developed and bundled into distributions.
We will discuss how the open-source ecosystem deals with security vulnerabilities, how they are discovered and fixed, and how end users react to this perpetual dance. Then we will go deeper into the range of security technology built into Ubuntu and what this means for protection against emerging threats. Finally, we will explore how Canonical’s enterprise-grade support delivers a comprehensive platform for secure open-source adoption.

Is Linux secure?

À medida que cresce a demanda por soluções open source, os revendedores de canal estão em uma posição única para orientar os clientes em suas transições tecnológicas com alternativas flexíveis e econômicas.
Neste webinar, a Canonical mostrará como a parceria conosco capacita os revendedores a oferecer soluções robustas de virtualização e infraestrutura, adaptadas às necessidades dos clientes. Você descobrirá como o portfólio da Canonical — incluindo tecnologias de virtualização seguras e escaláveis — ajuda as organizações a modernizarem sua infraestrutura, mantendo desempenho e controle.
Participe para explorar oportunidades de parceria, capacitação técnica e suporte de go-to-market pensados para ajudá-lo a prosperar em um cenário de TI em constante evolução.

Guia para parceiros sobre alternativas de virtualização: apoiando seus clientes na migração com a Canonical

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.

Learn more or contact our team: https://canonical.com/data

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.

Learn more or contact our team: https://canonical.com/solutions/ai

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

Learn more or contact our team: https://canonical.com/data/mysql

Navigating multi-cloud database deployments with MySQL for maximum uptime

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.

Learn more or contact our team: https://ubuntu.com/ai/data-science

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

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

Learn more: https://canonical.com/data/opensearch

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

Learn more or contact our team: https://canonical.com/solutions/ai/infrastructure

Optimise your ML workloads on Kubernetes

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.

Learn more or contact our team: https://canonical.com/data/spark

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

There are benefits to using a specialised database for every new use case you need to support. For example, we can make a case for using a graph database to manipulate graph data, or a specialised vector database to manage vectors. Yet, multiplying your data stores comes with consistency, cost and operability issues that you need to consider carefully.

PostgreSQL is built from the ground up to be extensible, so you can choose from over 1000 extensions to cover more use cases with the same solution – saving you time and resources.

In this webinar, we will examine the use cases you can meet with just PostgreSQL.We will cover the following:
Analytics 
Online transaction processing 
Geospatial data
Graph data 
Search and similarity
AI and ML 
Time Series and more

Learn more or contact our team: https://canonical.com/data/postgresql

How many use cases can you cover with PostgreSQL extensions?

Each type of NoSQL database offers unique strengths and is suited to specific types of applications and data requirements, making them versatile tools in modern data management. Many of them are compatible with an open source approach, giving you advantages such as the acceleration of innovation, flexibility, customisation, and community support. There are also innovations in the space where databases can run in a multi-cloud environment. 
However, these features and benefits come with their own set of challenges, and operating database clusters can be difficult. In addition, databases need to be integrated within a stack of technologies, such as the infrastructure it is deployed on and other data and AI tools. Faced with these and other challenges (like unpredictable costs and industry skill gaps), companies are finding it increasingly difficult to achieve operational excellence with an in-house team to manage databases. This is why they turn to managed service providers (MSPs).

In this webinar, Canonical product managers Michelle Tabirao (NoSQL Databases) and Adrian Matei (Managed Services) will dive into what operational excellence means for NoSQL Databases, and how this can be achieved with a managed service provider. We will explore: 

1) Introduction and main advantages of adopting NoSQL databases
2) Big data and AI use cases that leverage non-relational databases
3) Operational excellence in NoSQL Databases, from key operations to holistic approaches 
4) Benefits and challenges when choosing a managed service provider for your NoSQL database operations 
5) Future of NoSQL databases

Join us on the 18th of June 2024 for 30 minutes of expertise in NoSQL operations!

Managed NoSQL databases for big data and AI-powered applications

As AI dominates the tech stage, the intersection of Internet of Things (IoT) and artificial intelligence is taking up more of the spotlight. From AI-powered healthcare instruments to autonomous vehicles, there are many cases where artificial intelligence can be used and benefited from on edge devices. However, building an end-to-end architecture to enable such a solution comes with different challenges to other AI stacks, both in training models and in deploying them to the edge. 

## Open source at the edge
Open source solutions enable edge devices in different ways. From operating systems to ML platforms, enterprises can choose from a wide variety of solutions. This abundance of choice can be overwhelming, leading to organisations delaying decisions and not scaling their AI at the edge initiatives.
Join this webinar about AI at the edge

Join Andreea Munteanu, Product Manager for AI, Steve Barriault, VP of IoT Field Engineering and Alex Lewontin, IoT Field Engineering Manager, to discuss AI at the edge. 

During the webinar, you will learn:
- The main challenges rolling out AI at the edge and how to address them
- How to secure your ML infrastructure, from the data centre to the edge
- Key considerations to start and scale your ML embedded projects
- Benefits of running AI at the edge
- Common use cases and how to get them started quickly
- Role of open source in the Edge AI space

AI on the edge: solving enterprise challenges with embedded machine learning

Join us for a deep dive into the role of vector databases in generative AI applications. Vector databases facilitate efficient data representation, retrieval and manipulation, enabling AI systems to generate high-fidelity outputs across various domains, from natural language processing to image synthesis.

This webinar will discuss various concepts, such as generative AI, retrieval augmented generation (RAG), the importance of search engines, and efficient open source tooling that enables developers and enthusiasts to build their generative AI applications.

Learn more or contact our team: https://canonical.com/data/opensearch

Vector databases for generative AI applications

What can PostgreSQL do for AI application?

PostgreSQL is capable of efficiently storing and retrieving large amounts of data, such as training data for your models. Its wide support of the SQL standard, including SQL/MED, makes inspecting and querying that data a breeze. Moreover, PostgreSQL comes with similarity search capabilities, providing convenient index structures (GIST, GIN...) and built-in extensions (fuzzystrmatch, pg_trgm...).

Yet, there is more to PostgreSQL's ecosystem than the database server. There are more than 1000 extensions for PostgreSQL, many of which can be useful for AI developers and users, for example pgvector and PostgresML. 


The Role of PostgreSQL in MLOps

MLOps projects such as Kubeflow rely on relational databases for storing different artefacts, and choosing which relational database to use for MLOps is a common dilemma. We approached a similar topic in our recent webinar “PostgreSQL vs MySQL”, where we explored how, despite looking similar on the surface, the two databases are actually very different. The significant structural differences between databases can have a tremendous influence on the performance of any ML project, so understanding the possible impact is critical for both the enterprises who use these platforms and the engineers who develop them.


So how do we use PostgreSQL for machine learning?

Join the live discussion to learn more about using PostgreSQL for AI projects. The webinar will cover:
- An overview of the PostgreSQL ecosystem for AI projects
- Benefits of PostgreSQL for AI projects
- Use cases where PostgreSQL should be used for AI projects

Learn more or contact our team: https://canonical.com/data/postgresql

Using PostgreSQL to power your AI applications

AI is at the heart of a revolution in the technology space. From oil & gas to public sector, from telco to retail, all departments from all industries are looking for ways to put AI to work. Once organisations have finalised use case assessment, their next question is typically related to the environment they will use to develop and deploy their AI initiatives. 

They often prefer the public clouds as an initial environment, because of the computing power and ability to scale as projects mature. In addition to the infrastructure, enterprises need software where they can develop and deploy the machine learning models. Open source tools such as Jupyter Notebooks or MLflow are a great starting point and enable you to have a consistent experience on any cloud for your ML workloads. When scaling projects, the need for MLOps platforms such as Charmed Kubeflow is absolutely obvious.

Join our webinar to learn more about open source tooling for AI on public cloud. Led by Aaron Whitehouse, Senior Public Cloud Enablement Director, and Andreea Munteanu, AI Product Manager, 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

Many entities mistakenly consider ML models as the only significant challenge in an AI project. In reality, one of the most essential considerations for a successful AI/ML initiative is the infrastructure on which models are deployed, tested, and rolled out. This in itself creates the need for AI infrastructure specialists, which can be both difficult and costly to find. There is, however, another option: outsourcing your AI infrastructure to a multi-cloud managed service provider. 

Choosing an open-sourced managed infrastructure solution can help you accelerate your time to production, easily and cost-effectively cover some essential skill gaps in your ML journey, and even increase your budget’s predictability. In this webinar, product managers Adrian Matei and Andreea Munteanu explore the implications and benefits of adopting a Managed AI solution like Canonical’s. Join us and find out more!

Topics covered include: 
- Managed AI infrastructure overview & processes 
- Products & platforms
- Pre-deployment considerations
- Benefits of adopting the solution 
and more!

Managed AI infrastructure: demystifying AI’s most underrated challenge

As organisations increasingly look to take advantage of AI technologies, there are several critical considerations that they must contend with, including intellectual property, data security and costs related to computing infrastructure. Private cloud solutions are ideally suited to solving these challenges. Canonical Openstack, for instance, is a great example of a cloud platform that can be used to build and deploy machine learning applications securely and cost-effectively.

Why consider a private cloud for AI?
Private clouds are a handy solution for enterprises when it comes to AI/ML since they deliver many of the key capabilities that organisations report as important, including:
1) Cost optimisation: Private clouds enable businesses to optimise their costs by always running their workloads where it makes more sense from an economic standpoint.
2) Digital sovereignty: Private clouds offer a safe environment for data and applications by ensuring that the organisation owns access and controls the level of sharing amongst the different teams using the cloud.
3) Performance acceleration: Private clouds offer GPU virtualisation and other capabilities to improve performance and therefore project delivery, confidentiality, efficiency and time to setup as required by sophisticated AI/ML workloads. 

Learn more about AI on the private cloud in this webinar:
Join the webinar on 21 February 2023, where Tytus Kurek, OpenStack Product Manager, and Andreea Munteanu, AI Product Manager, will talk more about private clouds for AI projects. The presentation will cover:
1) Key considerations when building a private cloud for AI projects
2) Performance acceleration options for private cloud
3) Guidance for Kubernetes on OpenStack for AI initiatives
And more…

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

Comparing the most popular open source databases:

PostgreSQL and MySQL are undoubtedly the most popular open source databases. You can check the latest developer surveys from StackOverflow and JetBrains, or DB-Engines’ popularity index if you are not convinced. Their popularity is fueled by their vibrant communities and their track record of successful deployments across  IT sectors.

In this webinar, we will dive into the differences between these two popular databases and what those differences mean from a performance, operability and community engagement perspective and how Canonical can support you in adopting, securing and scaling open-source databases.

PostgreSQL vs MySQL

Data and AI Library

A lightweight, secure machine learning platform you can use on any infrastructure

Introduction to MLFlow, the open-source tool for machine learning

mlflow

Machine Learning

Artificial Intelligence

Open Source

Open Data

Cloud computing has exploded over the past few years, delivering a previously unimagined level of workplace mobility and flexibility. The cloud computing community on BrightTALK is made up of thousands of engaged professionals learning from the latest cloud computing research and resources. Join the community to expand your cloud computing knowledge and have your questions answered in live sessions with industry experts and vendor representatives.

Cloud Computing

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Application Development

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Introduction to MLFlow, the open-source tool for machine learning

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Ubuntu and Canonical