Join the Canonical Data and AI team at Data Innovation Summit 2024

Canonical is delighted to be a technology partner at the Data Innovation Summit (DIS) in 2024. We are proud to showcase our Data and AI solutions through our conference talk and technology in practice sessions. The event will take place in Kistamässan, Stockholm on April 24-25, 2024. Visit us at booth C71 to learn how open source data and AI solutions can help you take your models to production, from edge to cloud.

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Data and AI: get first-hand insights from Canonical experts

The modern enterprise can use AI algorithms and models to learn from their treasure troves of big data, and make predictions or decisions based on the data without being explicitly programmed to do so. What’s more, the AI models grow more accurate over time. 

The magic is in the melding of AI and big data. Data of incredible volume, velocity, and variety is fed into the AI engine, making the AI smarter. Over time, less human intervention is needed for the AI to run properly; in time, the AI can deliver deeper insights—and strategic value—from the ever-increasing pools of data, often in real time. 

In today’s competitive business environment, your AI and data strategies need to be more interconnected than ever. According to an MIT Technology Review survey, 78% of CIOs say that scaling AI to create business value is the top priority of their enterprise data strategy, and 96% of AI leaders agree. Nearly three out of four CIOs also say that data challenges are the biggest factor jeopardising AI success.

The Data Innovation Summit is a significant event in the field of Data and AI, especially in the Nordics. It brings together professionals, enterprise practitioners, technology providers, start-up innovators, and academics working with data and AI. We at Canonical are delighted to announce that we will be participating in this event and sharing our expertise in Data and AI.

Canonical is a well-known publisher of Ubuntu, which is the preferred operating system (OS) for data scientists. In addition to the OS, Canonical offers an integrated data and AI stack. We provide the most cost-effective options to help you gain control over your Total Cost of Ownership (TCO), and ensure reliable security maintenance, allowing you to innovate at a faster pace.

Canonical DIS talk: open source DataOps and MLOps

Canonical data and AI Product Managers, and Andreea Munteanu and Michelle Anne Tabirao will be speaking about open source for your DataOps and MLOps.

Talk description

Open source data and AI tools enable organisations to create a comprehensive solution that covers all stages of the data and machine learning lifecycle. This includes correlating data from various sources, regardless of their collection engine, and serving the model in production. Together, DataOps and MLOps drive the collaboration, communication, and integration that great data and AI teams need, making them essential to the model lifecycle. DataOps is an approach to data management that focuses on collaboration, communication, and integration among data engineers, data scientists, and other data-related roles to improve the efficiency and effectiveness of data processes. MLOps is a set of practices that combines machine learning, software development, and operations to enable the deployment, monitoring, and maintenance of machine learning models in production environments.

In this talk, we will explore how to build an end-to-end solution for DataOps and MLOps using open-source solutions like databases, ML and analytics tools such as OpenSearch, Kubeflow, and MLFlow. Professionals can focus on building ML models without spending time on the tooling operational work. We will highlight some use cases, e.g. in the telco sector, where they use MLOps and DataOPs to optimise the telco network infrastructure and reduce power consumption.

Attendees will learn about the critical factors to consider when selecting tools and best practices needed for building a robust, production-grade ML project.

Canonical demo and technology in practice sessions

The Technology in Practice sessions will be led by Maciej Mazur, Canonical’s Principal AI Engineer. We will conduct a live demo to showcase our data and AI solutions at booth C71. You can also watch this demo online via the event platform. 

Demo # 1: End-to-end data integration and MLOps in the Telco sector

Discover how to tackle the challenge of integrating diverse data sources in the telecom sector into a cohesive data and MLOps pipeline. This session focuses on the unique problem faced by the telecom industry in managing data from various vendors with different data collection engines and OSS tools. The technical implementation involves utilising SQL database, MongoDB, OpenSearch, Kafka, and Spark. You will learn how Kubeflow and MLFlow facilitate model training, KServe handles inference endpoints, and Superset is employed for visualising results such as cell capacity and mapping.

Demo # 2: Enterprise Spark 

We will show how to operate Apache Spark in an enterprise environment. Apache Spark is a free, open source software framework for developing distributed, parallel processing jobs. It’s popular with data engineers and data scientists alike when building data pipelines for both batch and continuous data processing at scale. Engineers can write Python or Scala code to develop Spark jobs for ETL (extract-transform-load), analytics and machine learning.

This demonstration will show how to use Spark in combination with other open source tools like Airflow / Jupyter / Volcano.

Come and meet us at DIS 2024

If you are interested in building or scaling your data and AI projects with open source solutions, we are here to help you. Visit our Data and AI offerings to explore our solutions.

Book a meeting with us

Learn more about our Data and AI solutions

• Ubuntu AI podcast: dive into data and AI on the go
• Big data and AI WP: build a smarter enterprise with a secure, integrated open source stack
• MongoDB for enterprise data management WP: MongoDB benefits for modern enterprise data management, use cases for financial, telecommunications and automotive industries
• MLOps toolkit: from hardware to applications, discover the key factors to consider when building your machine learning toolkit
• Using PostgreSQL to power your AI applications: learn what PostgreSQL has to offer for your AI projects
• Canonical Data: enterprise data solutions for rapid innovation at any scale
• Canonical AI: take your models to production with open source AI