Andreea Munteanu

A podcast on open source, machine learning and levelling the playing field for data-driven innovation.  In a world where generative AI and large language models (LLMs) are the new hot topics, having conversations about machine learning, MLOps or open source is a real need. This is what we had in mind when we first thought

Eight trends to keep an eye on this Artificial Intelligence Appreciation Day On 16 July the world celebrates International Artificial Appreciation Day. In the previous century, science fiction often covered topics and inventions that are now closer to science fact, such as humanoid robots. In the 50s, artificial intelligence met both grea

Large language models (LLMs) are machine-learning models specialised in understanding natural language. They became famous once ChatGPT was widely adopted around the world, but they have applications beyond chatbots. LLMs are suitable to generate translations or content summaries. This blog will explain large language models (LLMs), inclu

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising.  This

Canonical’s MLOps portfolio is growing with a new machine learning tool. Charmed MLFlow 2.1 is now available in Beta. MLFlow is a crucial component of the open-source MLOps ecosystem. The project announced it had passed 10 million monthly downloads at the end of 2022. With Charmed MLFlow users benefit from a platform where they can

The retail industry is going through a period of major upheaval. AI is transforming the landscape at a rapid pace. Grand View Research evaluated the market value at USD 5.79 billion in 2021 and this is expected to grow at a 23.9% compound annual growth rate (CAGR) from 2022 to 2030. For retailers, this translates

Timely patching with Ubuntu Pro for fully secured MLOps Generative AI projects like ChatGPT have motivated enterprises to rethink their AI strategy and make it a priority. In a report published by PwC, 72% of respondents said they were confident in the ROI of artificial intelligence. More than half of respondents also state that their

The public sector is investing heavily on artificial intelligence and machine learning initiatives. Deloitte AI Institute reported that 60% of government AI and data analytics investments aim to directly impact real-time operational decisions and outcomes by 2024. From automating redundant tasks to increasing the quality of services offer

Date: 17-21 April 2023 Location: Amsterdam  Booth: P15 In just a few weeks, Kubecon will be held at RAI Convention Center, in Amsterdam, the Netherlands. After a bunch of news from the industry around AI projects, such as GPT4 or MidJourney4, Canonical is also ready to bring open source into the landscape. Among the attendees,

Canonical is happy to announce that Charmed Kubeflow 1.7 is now available in Beta. Kubeflow is a foundational part of the MLOps ecosystem that has been evolving over the years. With Charmed Kubeflow 1.7, users benefit from the ability to run serverless workloads and perform model inference regardless of the machine learning framework they

After ChatGPT took off, the AI/ML market suddenly became attractive to everyone. But is it that easy to kickstart a project? More importantly, what do you need to scale an AI initiative? MLOps or machine learning operations is the answer when it comes to automating machine learning workflows. Adopting MLOps is like adopting DevOps, you

MLOps (short for machine learning operations) is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring. It will become a standard as artificial intelligence is moving towards becoming part of everyday business, rather than an innovative act

While AI seems to be the topic of the moment, especially in the tech industry, the need to make it happen in a reliable way is becoming more obvious. MLOps, as a practice, finds itself in a place where it needs to keep growing and remain relevant in view of the latest trends. Solutions like

MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into

From brick-and-mortar stores to online marketplaces, retail companies are all increasing their investments in artificial intelligence, in order to gain a competitive advantage!