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5 Pillars of Data-Centric AI
Currently, AI is the latest buzzword of the technology industry. Social media users and tech enthusiasts seem to discuss it daily, with a range of discussions, debates, and opinions, both positive and negative. If you work in tech or are...
MLOps as the Remedy to Tech Debt in Machine Learning
Tech Debt is on every technologist’s mind, even if they often choose to blissfully avoid discussing the topic. Few bother breaking the silence and warning their peers of the potentially dramatic consequences of letting Tech Debt go...
Tips to Label Data for Autonomous Driving
Labeling data for Autonomous Driving is not just very tedious and time-consuming: it is actually one of those times where annotating data the right way is fundamentally a matter of life and death. Luckily, a tremendous quantity of...
Workflows for Data-Centric AI
The concept of Data-Centric AI might still not be fully understood by the Machine Learning community yet, but it has unquestionably taken a more centric (no pun intended!) part of the ML landscape. Around the time when Data-Centric AI was...
6 Best Practices to Ace your Data Annotation Game
Data, data everywhere! We are dwelling into an ocean of diverse data at all times. And this data is useful for many use cases that have made our day-to-day life way easier than what it used to be. But all this comes at a price, as the...
7 Tips To Turn a Profit on your ML Project
To look cool, sound smart & futuristic, people in tech often start talking about solving a problem in real life using ML. It's all the hype right now, and people can’t get enough of it. But all these fancy ML projects meaning to solve...
9 Ways to Leverage Technology for Enhanced Data Preparation
Most Machine Learning experts agree that Data Preparation (the preparation of training data for Machine Learning) is the most time consuming part of their job; in fact, they dread it, in part because it is not a process that is associated...
7 Reasons Why You Should Give Active Learning a Try Right Now
At Alectio, Active Learning is one of our favorite topics: most of our Data Curation technology is built on top of it. In fact, we are on a mission to have all data scientists give it a try at least once, which is not trivial since...
How Good is your Data Labeling Tool?
AI research is accelerating, and with it, the development of new MLOps and DataOps tools. Labeling tools have been no exception: both open source tools and dedicated companies have been popping up at an unprecedented rate over the past...
Active Learning 101: Tricks for Tuning your Active Learning
As we have seen previously in this series of blog posts, Active Learning is a Machine Learning training paradigm which requires the repetitive succession of the same steps until the model is trained to the satisfaction of the data...
Active Learning 101 : A Deep Dive into the Least-Confidence Querying Strategy
In the first part of this mini-series, we have taken a high-level approach to explain the mechanics of Active Learning, and have also emphasized that there were many things to be tuned when trying out Active Learning: choice of a labeling...
Active Learning 101: the Only Intro You’ll Ever Need
With ever more conversations about Data-Centric AI, the number of posts and articles discussing Active Learning has grown exponentially over the past few months. Yet, quite paradoxically, the level of confusion about the topic has never...