Welcome to the first Alectio newsletter! We’ve been hard at work building out our platform, building on our research, and building up a library of content about both. The folks at Forbes took notice and we’re eager to share it with you as well!
First off, if you’ve been waiting for our on-prem solution, we have good news. We’re hard at work on it and will be looking for beta testers soon. Please reply to this email or let us know at firstname.lastname@example.org.
We’ve also been writing a lot about our research and wanted to share what we’ve been learning:
Are you spending too much money labeling data?
Our founder Jennifer Prendki wrote a byline for Towards Data Science that answers just that question. But, spoiler alert: the answer’s probably “yes.”
How to tell if active learning will work for your problem
If you think active learning is just about models learning from the most uncertain data, think again. Here’s a quick primer on how Alectio checks if active learning is feasible for a given problem.
Why the end of Moore’s Law means the end of Big Data as we know it
The days of limitless data collection may well be coming to an end. So what does that mean for your machine learning projects?
All data is not created equal
There’s a misnomer that more data is always better. It isn’t. In fact, adding more data to your models can be hazardous.
Everything you wanted to know about Alectio
Some FAQs about our process, what we believe, and how we can https://medium.com/alectio/heres-why-you-need-a-data-collection-strategy-ac98bc4323bhelp your machine learning models using a fraction of the data you’d expect.
If you want to stay up to date on everything we’re doing, our Medium blog is the best spot to do so. Thanks for reading!