Data Phoenix Digest - ISSUE 33

Instant access to OpenAI’s API. The perks of AI and automation. What Is the difference between outlier detection and data drift detection? PyTorch C++ API for use on mobile platforms. Compositional transformers for scene generation. CleanRL, Causal-BALD, Deceive D, jobs, and more ...

Dmitry Spodarets
Dmitry Spodarets


What's new this week?

Instant access to OpenAI’s API. New TensorFlow GNNs. The perks of AI and automation. And the now and future of Edge AI.

  • OpenAI has announced that now developers in supported countries can sign up and start experimenting with their API right away, without having to wait in the waitlist.
  • TensorFlow has released TensorFlow Graph Neural Networks (GNNs), a library designed to make it easy to work with graph structured data using TensorFlow.
  • According to a report by SnapLogic and Cebr, US companies witnessed an average year-on-year increase in revenue of 7%, or an extra $195 billion per month, due to automation.
  • According to a report by Unsupervised, 50% of business owners who has implemented AI say it has helped their business during the labor shortage by “filling in” for certain jobs.
  • As AI/ML moves from the cloud to embedded systems in the field, Edge AI arises to come up with new ways of setting up neurals, designing memory paths, and compiling to hardware.
  • In the future, AI will be everywhere. Find out what Ludovic Larzul, Founder and CEO, Mipsology, thinks about AI, Edge AI, and the possible scenarios for the future.

Funding News

  • Comet, an MLOps startup, raises $50M in Series B funding led by OpenView and existing investors: Scale Venture Partners, Trilogy Equity Partners, and Two Sigma Ventures.
  • Verbit, an AI transcription & real-time captioning company, closes $250M in Series E funding, bringing its valuation to $2B just five years after it was founded.
  • LifeVoxel, a developer of Prescient SaaS platform, raises $5M in a seed round, to bolster data intelligence of its AI diagnostic visualization platform.


How to Handle ML Model Drift in Production
Data drift is an everyday challenge in Data Science and Machine Learning. In this introductory overview, you'll learn about major steps you can take to handle it more efficiently.

Get Started: DCGAN for Fashion-MNIST
In this tutorial for beginners, you'll implement a typical DCGAN with TensorFlow 2 and Keras, based on a basic GAN paper and a Colab notebook.

Using CNN for Financial Time Series Prediction
In this tutorial, you'll learn how a CNN model can be built for prediction in financial time series, from creating 2D convolutional layers to monitoring the performance of model training.

Machine Learning Inference at Scale Using AWS Serverless
This short article explores the ways of how to run and scale ML inference using AWS serverless solutions: AWS Lambda and AWS Fargate.

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