Drive real value from your machine learning investments,
moving past PoC to productionised efficiency.
39% of businesses accelerated their ML projects due to COVID-19.
Businesses are expecting to see a 10X return on their investments.
Lack of in-house skills is the #1 challenge for starting ML projects.
Fraud detection leads the way in the use case race.
MLOps – DevOps for Machine Learning
One of the most difficult aspects of machine learning is integrating it with software engineering and building out a CI/CD pipeline for your ML solutions.
At Appsbroker, we have the skills to help you productionise your machine learning – whether that’s taking a Jupyter notebook live or building a scalable and resilient API for low latency inference.
Ethical, Fair and Regulatory Compliant AI
A key challenge when adopting machine learning is ensuring your algorithms are ethical and comply with regulations.
If you haven’t considered how GDPR might affect you, or you want to ensure your algorithms don’t discriminate against certain demographics, or you need to be able to explain your decisions to a regulatory, we can help.