This paper presents a Large Language Model (LLM) based conversational agent system designed to enhance human-machine collaboration in Machine Learning Operations (MLOps). We introduce the Swarm Agent, an extensible architecture that integrates specialized agents to create and manage ML workflows through natural language interactions. The system leverages a hierarchical, modular design incorporating a KubeFlow Pipelines (KFP) Agent for ML pipeline orchestration, a MinIO Agent for data management, and a Retrieval-Augmented Generation (RAG) Agent for domain-specific knowledge integration. Through iterative reasoning loops and context-aware processing, the system enables users with varying technical backgrounds to discover, execute, and monitor ML pipelines; manage datasets and artifacts; and access relevant documentation, all via intuitive conversational interfaces. Our approach addresses the accessibility gap in complex MLOps platforms like Kubeflow, making advanced ML tools broadly accessible while maintaining the flexibility to extend to other platforms. The paper describes the architecture, implementation details, and demonstrates how this conversational MLOps assistant reduces complexity and lowers barriers to entry for users across diverse technical skill levels.

Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizations difficult and unportable. We present Edge Impulse, a practical MLOps platform for developing TinyML systems at scale. Edge Impulse addresses these challenges and streamlines the TinyML design cycle by supporting various software and hardware optimizations to create an extensible and portable software stack for a multitude of embedded systems. As of Oct. 2022, Edge Impulse hosts 118,185 projects from 50,953 developers.

The largest ICT employer in Hungary, Deutsche Telekom IT Solutions (formerly IT-Services Hungary, ITSH) is a subsidiary of the Deutsche Telekom Group. Established in 2006, the company provides a wide portfolio of IT and telecommunications services with more than 5000 employees. ITSH was awarded with the Best in Educational Cooperation prize by HIPA in 2019, acknowledged as one of the most attractive workplaces by PwC Hungary's independent survey in 2021 and rewarded with the title of the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team. We seek our new passionate Tech Lead for our existing MLOps platform.

Tractable is an Artificial Intelligence company bringing the speed and insight of Applied AI to visual assessment. Trained on millions of data points, our AI-powered solutions connect everyone involved in insurance, repairs, and sales of homes and cars – helping people work faster and smarter, while reducing friction and waste. Founded in 2014, Tractable is now the AI tool of choice for world-leading insurance and automotive companies. Our solutions unlock the potential of Applied AI to transform the whole recovery ecosystem, from assessing damage and accelerating claims and repairs to recycling parts. They help make response to recovery up to ten times faster – even after full-scale disasters like floods and hurricanes.

Tractable is an Artificial Intelligence company bringing the speed and insight of Applied AI to visual assessment. Trained on millions of data points, our AI-powered solutions connect everyone involved in insurance, repairs, and sales of homes and cars – helping people work faster and smarter, while reducing friction and waste. Founded in 2014, Tractable is now the AI tool of choice for world-leading insurance and automotive companies. Our solutions unlock the potential of Applied AI to transform the whole recovery ecosystem, from assessing damage and accelerating claims and repairs to recycling parts. They help make response to recovery up to ten times faster – even after full-scale disasters like floods and hurricanes.

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn't easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria. Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI's product intelligence team, told Protocol earlier this year. "The duct tape starts to show," he said. Nokleby, who has since left the company, said that for a long time Lily AI got by using a homegrown system, but that wasn't cutting it anymore.

Machine Learning Model Monitoring is the operational stage that follows model deployment in the machine learning lifecycle. It comprises keeping an eye out for changes in the ML models, such as model deterioration, data drift, and idea drift, and ensuring that the model is still performing well. Many model monitoring software tools are available to monitor these models' changes. Let us look at some of the most helpful ML model monitoring tools. Neptune AI is an MLOps company designed for research and production teams who run a large number of experiments.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! There is no shortage of options for organizations seeking places in the cloud, or on-premises to deploy and run machine learning and artificial intelligence (AI) workloads. A key challenge for many though is figuring out how to orchestrate those workloads across multi-cloud and hybrid-cloud environments. Today, AI compute orchestration vendor Run AI is announcing an update to its Atlas Platform that is designed to make it easier for data scientists to deploy, run and manage machine learning workloads across different deployment targets including cloud providers and on-premises environments.

Imagine that you are a digital map application. You collect live data from cell towers, GPS signals, and anonymous users. This includes information such as travel times, traffic speeds, and roadworks. Every data source is unique and each one has different ownership. Access, formats, accuracy,y, and access can all change depending on signal strength.

The machine learning operations (MLOps) product category has been moving quickly, especially in the last year, and several platforms have emerged to take it on. Cloud providers including AWS and Microsoft, analytics players including Databricks and Cloudera, MLOps pure plays like Algorithmia, and even open source projects like MLflow, offer integrated platforms to manage machine learning model experimentation, deployment, monitoring and explainability. Now Spell, a New York City-based MLOps startup, is providing an MLOps platform specifically geared to deep learning. As such, Spell refers to its platform, announced last week, as facilitating "DLOps." ZDNet spoke with Spell's head of marketing, Tim Negris as well as its CEO and co-founder, Serkan Piantino (who previously served as Director of Engineering at Facebook AI Research, and who opened Facebook's New York City office).