Your brilliant ML models are crunching numbers at an unprecedented scale in a safe and controlled environment. But how many of them have successfully made the transition to the real world?
We can all agree that this gap between the lab and live production is a monumental challenge that many technologists face.
And there are numerous reasons why your ML models stay stuck that it becomes a mission to pinpoint exactly what the reason is. Whether the problem is data, model selection and tuning or straight-up operational – then you’re not alone:
- IBM says that poor quality data costs businesses in the US $3.1 trillion annually.
- Google reveals that improper tuning of hyperparameters is a key reason for the failure of ML models.
- Gartner found that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them.
The Dark Side of the Transition
You’ve invested time and resources into developing the perfect model.
But moving it to production?
That’s often a maze of broken pipelines, scaling issues, and server crashes. Don’t forget about data drift and model decay, which can silently degrade your model’s performance over time.
Your Blueprint for Success
Don’t get stuck in the lab. At Teraflow.ai, we specialise in AI enablement, turning your cutting-edge models into business-as-usual.
Here’s how:
The Essentials of Data Engineering
Data: The Invisible Foundation
Without high-quality, curated data, even the most sophisticated ML models falter. Data engineering ensures that data ingestion, storage, and processing are seamless.
Data Pipelines
Robust data pipelines are essential for operational efficiency. Our data engineering services include automated ETL processes and real-time analytics.
Machine Learning Engineering: From Prototype to Production
Version Control
Deploying ML models isn’t as simple as a git push. Version control is crucial. We ensure that your model versions are tracked, helping you roll back if needed.
Scalability
Deploying containerised models is essential for quick scaling. Docker and Kubernetes are part of our toolbox.
Cloud Architecture: The Sky’s the Limit
Reliability
Your models need a home.
Our cloud architecture services ensure that your ML models are always accessible and reliable, with real-time monitoring and auto-scaling capabilities.
Vendor Agnostic
Whether AWS, Azure, or Google Cloud, we’ve got you covered.
The User Experience: The Final Frontier
Dashboards & Monitoring
We don’t just deploy; we monitor.
Our UX services include intuitive dashboards, ensuring you’re never in the dark about your model’s performance.
Skyrocket Your ML Models (with the Best)
The transition from lab to live doesn’t have to be painful.
We’re your one-stop shop for data engineering, machine learning engineering, cloud architecture, and UX services.
We’ve helped enterprises and organisations across the globe make AI work for them. Let’s make AI work for you, too.
