Innovating the future of IT Operations
Whether you’re a consulting company or building your own great software, if you’re looking to streamline your IT operations then look no further than MLOps with Seldon. By incorporating machine learning into your operational processes, you can achieve faster deployment, improved performance, and better collaboration between your data science and IT teams.
Seldon is the IT Industry leaders' choice
Why use Seldon in the
IT industry?
- Improve the time-to-market for new products or services
- Reduce operational costs through automating mundane tasks
- Collaborate more effectively by augmenting data science team skills and workflows
The role of AI is growing rapidly
A business’s only path to technical progress is through risk and confrontation. According to Semrush. AI is predicted to increase business value and worker skills significantly. By 2030, AI will lead to an estimated 26% increase in global GDP.
Streamlined Model Deployment
Serving and model management at scale is simplified with Seldon. It’s easy to integrate these models into their existing infrastructure, which allows IT organizations to focus on developing and training models, rather than spending time on deployment and management. This can have a positive impact by reducing the time to market for new products and services.
Efficiency Gains through
ML Monitoring
Access greater control and visibility over your ML models so you can detect outliers and data drift quicker, and retrain models when necessary. Adopt an efficient, systematic problem-solving approach to your ML pipelines by introducing faster root cause analysis. Improve the quality of your workflows, reduce risk, and drive cost savings by decreasing errors.
Build Trust with Model Transparency
Gain deeper understanding of the behavior of ML model outputs and associated systems so you can trust the results of your models and identify any biases or issues. If the model doesn’t correspond with decision-making policies, simply arrange to retrain the model. This is particularly important in areas like cybersecurity and regulatory compliance that require transparency and accountability in the use of data and algorithms.
"The review process has gone from multiple days to just hours giving us a massive competitive advantage both for our existing customers and soon our potential customers."
Thor Larsen
Data Scientist
Read our case study on how Noitso accelerated their model deployment from days to hours.