Identifying the causes of inaccurate measurements
CFM-ID’s latest version performs better in understudied metabolite classes
CliqueMS turns an old foe, in-source fragmentation, into a friend to build similarity networks for annotation
How the GNPS Dashboard improves collaboration from a web browser
How to use Cosine distance and Spec2Vec to compare spectra
AutoTuner is a new Bioconductor package that quickly and accurately computes preprocessing parameters for your MS2 metabolomics data
Developing metabolomics tools with doctors in mind
How to download, understand, clean, and analyze the GNPS JSON dataset
A unique machine learning strategy allows you to annotate each spectrum in an MS2 experiment with high accuracy on the chemical class level
How Feature-Based and Ion Identity Molecular Networking improve the accuracy of GNPS molecular networks
What software does our team use to keep knocking down the efficiency barriers?
A high level introduction to Dask
How we built Omigami: A scalable machine learning tool for metabolomic researchers
Leverage machine learning to detect poor quality integrations and save hours assessing peaks manually
Grokking the internals of Dask
Predict structure similarity from MS/MS spectra directly with a deep learning model.
What it’s like to be part of our team and why it might be different from what you expect
We are releasing the beta of Open MLOps: an open source platform for building machine learning solutions.
AI can predict the risk of Apnea from a single ultrasound, so patients no longer need 24 hours in a sleep lab
A handbook on MLOps and how to think about it
Properly tracking your machine learning experiments is easier than you think.
A platform like Seldon converts your model file into an API that you can serve at scale
A model registry tracks versions and metadata for all your machine learning models
Both manage large codebases but machine learning engineers handle models and big data too
Why you should monitor your machine learning models after shipping them
It’s important to own your ML pipeline for flexibility and control
A new tool to smartly enrich molecular networks with metabolite annotations from different sources
Spec2Vec uses an unsupervised machine learning model to predict structural similarity from MS/MS mass spectra.
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
FEAST is the only standalone open-source feature store, but you have some other options too.
ZebraMedicalVision has 7 FDA approved solutions – here’s how they did it.
It’s Open Source, plays nicely with Kubernetes, and there’s a great community
A novel approach to feature annotation
Why we abandoned Kubeflow in our machine learning architecture
Insights from Nathan Wan
How to not overfit your models and get less false positives
What’s the right way for your team to approach Machine Learning?
Analyzing sewage for cheap, fast, accurate, non-invasive COVID-19 monitoring
Comparing machine learning platforms
Comparing data dashboarding tools and frameworks
Insights from Alexander Titus
Can deep learning extract new insights from basic H&E stains?
Choosing a task orchestration tool
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)
How can you process more data quicker?
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.
9 ways machine learning is helping us fight the viral pandemic.
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?
How to deal with unseen data, optimise response times, and update models frequently.
How to parallelize and distribute your Python machine learning pipelines with Luigi, Docker, and Kubernetes
They are not the same – but are often used interchangeably
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