The average machine learning engineer spends 98% of their time stitching services together. At @abacusai
, we take data from you, train a model, deploy it and give you an endpoint you can use immediately. Less complexity → More time to focus on what truly matters.
AI
-
Abacus AI Simplifies ML Deployment with End-to-End Solutions
By
–
-
Top 2023 Tech Trends Predicted By AI Model
By
–
Here Are The Top 2023 Trends As Predicted By An #AI Model : A Perfect Storm of Tech https://
medium.datadriveninvestor.com/here-are-the-t
op-2023-trends-as-predicted-by-artificial-intelligence-828a093a0ca6
… @pierrepinna @Xbond49 @PawlowskiMario @psb_dc @gvalan @TheRudinGroup @HaroldSinnott @mikeflache @Shi4Tech @Nicochan33 #MachineLearning #DeepLearning #Fintech #Datascience -
2022 Conference Review Activity Across Seven ML Programs
By
–
2022's year in reviews: I did 28 conference reviews, plus 42 meta-reviews, on 7 program committees (COLT, UAI, NeurIPS, SaTML, ICLR, USENIX Security, ALT). I also chaired two #ICML2022 workshops (TPDP and UpML). Numbers are down from last year, so why do I feel more tired…
-
5G standalone infrastructure essential for Apple mixed reality scaling
By
–
Much of the 5G network in the US remains non standalone 5G. It will require stand-alone 5G to scale mixed reality. Also 2023 was reported target for VR headset from Apple not AR & Mixed reality glasses which face more complexity challenges with 24 to 25 reported as timelines.
-
The Future of AI: What Business Leaders Need to Know
By
–
I just published The Future of AI: What Business Leaders Need to Know https://
link.medium.com/UfXhXfalgwb -
The Future of AI: What Business Leaders Need to Know
By
–
Check out my latest article: The Future of AI: What Business Leaders Need to Know https://
linkedin.com/pulse/future-a
i-what-business-leaders-need-know-ganesh-padmanabhan
… via @LinkedIn -
Differential Privacy and Secure Aggregation in Federated Learning
By
–
It's a bit complicated. Methods like differential privacy and secure aggregation on top of FL generally help, but there are some caveats. See "Is it possible to Prevent Our Passive and Active Attacks?" of http://
cleverhans.io/2022/04/17/fl-
privacy.html
…. -
Roadmap for Aspiring Machine Learning Engineers
By
–
I understand your point. But this could act as a roadmap for someone just starting in the field who aspires to be an ML engineer.
-
Federated Learning: Heterogeneity and Privacy Perspectives
By
–
I like FL as a lens through which to study heterogeneity in clients, which may have different distributions, resources, or capabilities. But not for privacy. Here is another perspective on privacy of FL, which is a bit more conspiratorial than my own https://
x.com/le_science4all
/status/1602432680657928193
… -
Federated Learning Privacy Misconceptions Debunked
By
–
~30% of true/false voters made the biggest misconception in privacy-preserving ML. Fact: federated learning is not private. @jasondeanlee and @tomgoldsteincs posted some nice papers showing this. Also, here's a blog explainer I like by @fraboeni et al.: http://
cleverhans.io/2022/04/17/fl-
privacy.html
…