How to scale the use of AI within your organisation
How you safely run hundreds of AI and ML models driving better customer outcomes and growth.
- AI in business
- AI implementation
- digital strategy
- Machine Learning
All Posts
- All
- AI Ethics (1)
- AI implementation (2)
- AI in business (3)
- AI roadmap (1)
- Artificial Intelligence (3)
- bandits (2)
- Contextual Bandits (1)
- Continuous Intelligence (4)
- digital strategy (2)
- ethics (1)
- Experimentation (8)
- fairness (1)
- IBM (1)
- Machine Learning (6)
- mlops (2)
- Next Best Actions (2)
- Product (1)
- Statistics (1)
- transparency (1)
- Uncategorized (1)
Breaking through the FUD (Fear, Uncertainty, Doubt) in AI
AI has grown in popularity in recent years, but Fear, Uncertainty and Doubt over the technology remains.
- mlops
Explainable AI (XAI) and Interpretable Machine Learning (IML) models
As the use of AI in the modern world continues to grow, the topic of XAI becomes increasingly important.
- transparency
Next-best-action for social good
Next-best-action systems are often used to optimise business metrics and individual customer outcomes, but could they also become a vehicle for promoting social good?
- ethics
How to detect algorithmic bias in production
Sometimes, algorithms look biased but might not be, and other times algorithms look unbiased but may be - how can you tell?
- AI Ethics
- Experimentation
- Machine Learning
- Artificial Intelligence
Design patterns for continuous intelligence
We show how the deployment design pattern for continuous intelligence systems differs from those used in traditional ML.
- continuous-intelligence
Data access patterns in next-best-action systems
A system for next-best-actions needs to resolve several challenges related to data access.
- Machine Learning
An architecture for enabling next-best-actions for your customers
At Ambiata, we have built a next-best-action system for automated decisioning which is currently used in production on a variety of use cases.
- Machine Learning
MLOps: How to choose the best ML model tools
MLOps is a rapidly growing field aimed at standardising and streamlining the lifecycle of ML models, from development and deployment through to ongoing maintenance.
- mlops
How to test decision-making algorithms safely
The ability to test decision-making algorithms without actually deploying them to live systems is an important feature for automated decision-making systems to have.
- bandits
POWERful Contextual Bandits
Real-time personalized recommendations in enterprises with contextual bandits. Keep your automated recommendations relevant in a post COVID-19 world.
- IBM
- Contextual Bandits
Experimentation platforms at scale
We examine how experimentation platforms used by search engine and social media companies have been designed to handle a range of experimentation issues that occur at scale.
- Experimentation
Next-Best-Action Recommendation
Reinforcement learning, supervised learning, contextual bandits, active learning – how do they all fit together to make better customer outcomes?
- Continuous Intelligence
- Experimentation
- Machine Learning
- Artificial Intelligence
Comparing approaches to experimental analysis
Clear and unbiased analysis methods are critical to understanding the experiments that businesses run in-market on real users.
- Experimentation
An overview of Continuous Intelligence
Continuous intelligence has been identified as an emerging trend, but what exactly is it and why is it important?
- continuous intelligence
How uplift modeling works
Uplift models are used to identify customers who are most likely to respond positively as a result of receiving some intervention.
- experimentation
Turning an AI strategy into an AI plan
You have set strategic goals for AI in your organisation, but how do you turn a strategy into a successful implementation plan?
- experimentation
- AI roadmap
- AI in business
- AI implementation
Continuous Intelligence with Contextual Bandits
What is a contextual bandit? How does it enable continuous intelligence and real-time decision-making?
- bandits
Experimentation - the hidden layer in the data science pyramid
Why experimentation is required for a good return on your data investment.
- experimentation
- digital strategy
- AI in business
Introducing Atmosphere
Here we introduce Atmosphere – Ambiata’s new continuous intelligence service – and explain how it fits into our renewed values and purpose.
- Product
- Continuous Intelligence
- Experimentation
- Machine Learning
- Artificial Intelligence
Bias detection and mitigation
Fairness and understanding hidden biases in algorithms used for decision making are increasingly important.
- fairness
From A/B testing to contextual bandits
A/B testing is a simple way for companies to make design decisions under uncertainty.
- Uncategorized
Researcher degrees of freedom
If you torture the data long enough, Nature will confess.
- Statistics