SuperAnnotate
Labelbox
V7
CloudFactory
Playment
Hive
Dataloop AI
Clarifai
Google Cloud TPU
Scikit-learn
machine-learning in Python
python-recsys
Qubole
Amazon Forecast
Microsoft Recommendations API
BigML
SuperAnnotate is the leading platform for building, fine-tuning, iterating, and managing your AI models faster with the highest-quality training data. With advanced annotation and QA tools, data curation, automation features, native integrations, and data governance, we enable enterprises to build datasets and successful ML pipelines. Partner with SuperAnnotateโs expert and professionally managed annotation workforce that can help you quickly deliver high-quality data for building top-performing models.
SuperAnnotate Google Cloud TPUBased on our record, Google Cloud TPU seems to be a lot more popular than SuperAnnotate. While we know about 17 links to Google Cloud TPU, we've tracked only 1 mention of SuperAnnotate. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Ok, so I tried comparing 4 of the better data annotation tools like dLabel.org, CVAT.com, SuperAnnotate.com and Labelbox.com . I tried them all as thoroughly as I could and I probably missed some things so apologies in advance for that! Let me know what I missed in the comment. Btw, I'm Amir and I've worked most of my data-labeling career at dLabel.org. Source: about 5 years ago
I think the third company (likely Google) is going to make LLMs financially feasible with: - dedicated hardware (https://cloud.google.com/tpu) - optimized models (https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/). - Source: Hacker News / about 2 months ago
Previous TPU generations, including last year's Ironwood, were pitched as unified flagship chips. Google's internal experience running Gemini, its consumer AI products, and increasingly complex agent workloads apparently showed that a single architecture forces uncomfortable trade-offs. So they split the roadmap. - Source: dev.to / 3 months ago
Tensor Processing Units are a technology developed and owned by Google. While you can find GPUs in every cloud provider offer, the TPUs are currently only available through Google Cloud Platform. Situation when you invest in a technology or a service that is not available anywhere else is called vendor lock-in โ it's something the sales people love, while customers try to avoid it. What does this look like for... - Source: dev.to / 3 months ago
Google's model is cloud-based. You can't buy a TPU to put in your server. Instead, Google keeps them in their own data centers and rents access exclusively through this. This allows Google to control the entire stack and they don't have to pay the "NVIDIA Tax". - Source: dev.to / 6 months ago
While I don't use Gemini, I'm betting they'll end up being the cheapest in the future because Google is developing the entire stack, instead of relying on GPUs. I think that puts them in a much better position than other companies like OpenAI. https://cloud.google.com/tpu. - Source: Hacker News / 6 months ago
Labelbox - Build computer vision products for the real world
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
V7 - Pixel perfect image labeling for industrial, medical, and large scale dataset creation. Create ground truth 10 times faster.
machine-learning in Python - Do you want to do machine learning using Python, but youโre having trouble getting started? In this post, you will complete your first machine learning project using Python.
CloudFactory - Human-powered Data Processing for AI and Automation
python-recsys - python-recsys is a python library for implementing a recommender system.
