Build Modern ML Solutions, Faster
Partner with experts to ensure your ML training and deployment pipelines are optimized with state-of-the-art research and industry best practices.
World-Class ML Experts by Your Side
Per Gartner, only 50% of AI projects make it from pilot into production. And those that do take an average of nine months to do so. Labs by Neural Magic is here to change that.
Engage with us to build the right AI solution and get it into production quickly. From research to production, our team of award-winning, world-class ML Experts will work side-by-side with your team to build out a modern ML training and deployment process.
Together, we ensure you are production-ready with transfer-learned, state-of-the-art models for your data while achieving front line performance and scalability in your target deployment scenarios.
With You at Every Step of Your Journey
Build & Train
WHAT’S LIMITING YOUR POTENTIAL?
- Keeping pace with ML research
- Picking the correct model
- Applying user data to performant models
MOVING FORWARD, FASTER
Build the right thing the first time. Whether it’s just an idea or an early POC, Labs is here to help your team execute the right approach for your desired business outcomes and get your ML solution into production as soon as possible.
WHAT’S SLOWING YOU DOWN?
- Enabling sparsity while retaining accuracy
- Delivering speed on commodity hardware
- Improving accuracy & performance
DELIVERING BEST-IN-CLASS RESULTS
Create models that push the boundaries of performance, regardless of your ML task. Labs walks you through all of the steps to optimize cutting edge models with your data, so that they deliver best-in-class performance in your target production environments.
WHAT’S KEEPING YOU UP AT NIGHT?
- Measuring deployment effectively
- Optimizing deployment spend
- Meeting latency and scalability requirements
Run at scale with maximum efficiency and confidence. Whether you are deploying on-premise or at the edge, Labs works with you to develop a deployment strategy with your business goals and constraints at the forefront.