By Idan Noti, MLOps & DevOps Director, UVEye
In the fast-paced world of AI and machine learning, orchestrating model training isn’t just a technical checkbox – it’s the backbone of scaling innovation. Building a model is one thing, but managing the chaos of datasets, compute resources, and team workflows, that’s where the real challenge lies. Without a solid system, you’re left juggling scattered experiments, runaway costs, and missed deadlines.
Model training orchestration brings order to this mess, ensuring your team can focus on creating value rather than wrestling with infrastructure.
Orchestration means automating the gritty details – scheduling jobs, allocating GPUs, tracking experiments, and ensuring reproducibility. It’s about making sure your data scientists aren’t stuck waiting for compute resources or untangling failed runs. A good orchestration system optimizes resource usage, cuts down on idle time, and keeps costs in check.
For instance, dynamically scaling cloud instances or splitting GPUs for multiple tasks can boost efficiency by up to 32%, according to industry insights. That’s not just a number; it’s time and money saved, letting teams iterate faster and ship models sooner.
Exploring Solutions: Our Take on ClearML
Enter ClearML, a platform we’ve been using extensively, not just for its orchestration capabilities but for its proven capabilities in our day-to-day AI/ML operations. We currently rely on ClearML to manage our datasets. Given that UVEye now scans more than 1M vehicles each month, we need to process billions of frames and videos. ClearML handles this demanding task exceptionally well, providing the robust framework needed for such vast data volumes.
Beyond dataset management, ClearML is also our go-to for running complex training experiments. It allows us to execute very long training runs with comprehensive tracking capabilities, effortlessly visualizing our training metrics in an incredibly helpful way. The platform makes it easy to compare different experiments side-by-side, so we can quickly identify regressions or significant improvements in model performance. What’s more, ClearML offers complete flexibility with metrics; we’re never limited in what we can track and explore, adapting to our specific research needs.
While we’re already seeing immense value from these features, ClearML also promises to handle even more of the heavy lifting — queue management, resource allocation, and advanced experiment tracking — all in one place. It supports diverse setups, whether you’re on Kubernetes or bare metal, and integrates seamlessly with cloud providers like AWS or GCP. This flexibility could be a game-changer for us, letting our team focus on refining models instead of configuring clusters.
Editor’s Note: UVEye is using ClearML’s AI Development Center, a complete AI builders’ workbench, offering collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment for ML models and LLMs. ClearML enables customers to focus on developing their AI/ML code and automation, ensuring their work is reproducible and scalable. The AI Development Center is part of ClearML’s three-layer platform that delivers a smooth, scalable AI workflow. It seamlessly integrates with the company’s Infrastructure Control Plane, enabling customers to launch any AI workload securely on approved remote clusters. It’s the ultimate solution for AI builders who need flexibility and easy access to compute resources while driving AI adoption. Customers’ AI journeys with ClearML often start by provisioning compute resources through the Infrastructure Control Plane. From there, they can accelerate their GenAI projects with the ClearML GenAI App Engine, deploying custom or fine-tuned LLMs while iterating using integrated data tools and orchestration. Tying it all together, the AI Development Center enables customers to code, test, and deploy AI models with ease.
The Bottom Line: Execution Drives AI Success
Why does this matter? Because AI isn’t just about algorithms – it’s about execution. Poor orchestration can bottleneck progress, leaving your team stuck in manual loops or drowning in untracked experiments.
Tools like ClearML could help us automate pipelines, ensure reproducibility, and scale seamlessly across hybrid environments. It’s not just about keeping up; it’s about staying ahead. As we explore AI/ML solutions, the goal is clear: streamline, optimize, and deliver.
Orchestration isn’t glamorous, but it’s the engine that drives AI success.
Editor’s Note: If you’d like to learn more about ClearML or see it in action, be sure to request a demo.