Tarek Oraby

Let's take a look back at the past year. In this first part of our annual review, we'll recap all the key additions and improvements to our end-to-end MLOps platform, ecosystem integrations, and more. Stick around and you'll find out what to expect in the next year and far beyond!

Create structured and efficient workflows that help your data science team work faster and smarter, i.e., maximize the impact on the business and increase the speed of experimentation and delivery without compromising quality.

Our latest feature is built to help you make the most out of your on-prem hardware: utilize idle GPUs, adjust GPU usage for every ML job, and forget about managing priority queues. It’s live and ready for you to give it a spin (no pun intended).

Introducing an out-of-the-box solution that gives all Valohai users automatic, immutable, and secure audit logs that ensure traceability for navigating compliance requirements, debugging issues, and improving accountability within teams.

We’ve built the Model Hub to help you streamline and automate model lifecycle management. Leverage Valohai for lineage tracking, performance comparison, workflow automation, access control, regulatory compliance, and more.

Valohai’s new experimental feature selects compute instances based on where the data has been cached already, helping you reduce data transfer overhead and increase model iteration speed.

Valohai’s latest feature helps you avoid unnecessary costs by reusing the results of matching pipeline steps from previous executions. This feature is already available to all Valohai users!

We’ve built significant enhancements into our platform to further empower data science teams in accelerating time-to-market and optimizing operational costs. These enhancements tackle model iteration speed, efficient resource utilization, and dataset management.

Valohai’s Model Registry is a centralized hub for managing model lifecycle from development to production. Think of it as a single source of truth for model versions and lineage.

We designed our new Kubernetes support so that Data Science teams can effortlessly manage and scale their workflows on top of Kubernetes and enhance their overall machine-learning operations.

We're excited to announce that Valohai now supports Slurm, an open-source workload manager used in HPC environments. Valohai users can now scale their ML workflows with Slurm-based clusters with unprecedented ease and efficiency.