It’s important to own your ML pipeline for flexibility and control
Ensuring your team’s work is reproducible, accountable, collaborative, and continuous
Translational Metabolomics bridges the gap between research and industry, “translating” scientific breakthroughs into real-world products.
Why and how to make your machine learning models interpretable
ZebraMedicalVision has 7 FDA approved solutions – here’s how they did it.
What’s the right way for your team to approach Machine Learning?
Analyzing sewage for cheap, fast, accurate, non-invasive COVID-19 monitoring
Insights from Alexander Titus
Can deep learning extract new insights from basic H&E stains?
The 11 fundamental building blocks that make up any machine learning solution
5 common hurdles for Machine Learning projects and how to solve them.
Lessons from someone who’s done it all (or at least most of it)
Train a model to predict who survives the titanic.
9 use cases for energy distribution companies
Machine Learning Engineers finally deliver on the promise of AI.
6 applications for transmission system operators.
9 curated images, interactive tools and flowcharts that explain machine learning
Everything you need to know to succeed in your machine learning project.
3 use cases for electric power plants
9 things to check when you pick a consultancy.
Find the best use cases for your team with this simple checklist.
Avoid confusion and plan your AI project with this simple checklist.
How to avoid Coupon Hunters by predicting the long-term impact of offers on each shopper
Double Your Conversion Rates
How AI is used for trading, fraud detection, insurance & personalised banking
How AI helps traders make better decisions & improve high-frequency trading
What are the best applications of AI in marketing in 2018?
Our process is based on helping > 50 businesses decide what to do with AI
Email only users who are ready to buy
Part 1: From Zero to First Recommendations
AI for Diagnostics, Drug Development, Treatment Personalisation and Gene Editing
How to make sure your project stays on track
Which AI-supported processes will be taken for granted in ten years?
What's different about machine learning projects? How do you limit risks and build a good solution?
They are not the same – but are often used interchangeably
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