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7 Jun, 5 tweets, 2 min read
Here is our latest free Scope

And a recap thread👇
Editorial

2/⬇️
The latest AI/ML research

3/⬇️
Tech

4/⬇️
Money in AI

5/5

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More from @TheSequenceAI

TheSequence Profile picture
7 Jun
The centralized nature of AI makes it difficult for startups to compete with the large tech incumbents that have access to:
+massive datasets
+virtually unlimited computing resources
+world-class research talent

Decentralized AI is the key

Thread⬇️
The research in decentralized ML is nothing new and can be traced back to the late 1970s

But the space has caught new momentum w/ blockchains and distributed ledger technologies
2/⬇️
However, blockchains are not the only technology trend influencing decentralized ML

Decentralized ML has benefited from:
+Blockchains
+Federated Learning
+Private ML
3/⬇️
Read 7 tweets
TheSequence Profile picture
5 Jun
🤖@hopsworks = feature store for your deep learning solution

It’s a feature store platform with its own loyal community that has been adopted by several major companies
Thread🧵👇 Image
❓HopsWorks = open-source feature store platform that enables the management and maintenance of features in a deep learning infrastructure

It’s a centralized catalog of features that can be discovered, used, and maintained across different ML models
2/⬇️
HopsWorks capabilities:
+Feature Reusability
+Feature Discovery
+Feature Analysis
3/⬇️
Read 6 tweets
TheSequence Profile picture
29 May
🤖@MLflow = one of the most popular platforms for end-to-end ML lifecycle management

It is integrated with every major framework and platform on the market

Thread🧵👇
❓MLflow = open-source framework that implements many of the principles of architectures like:
 
+FBLearner Flow by @FacebookAI
+TFX by @GoogleAI
+Michelangelo by @UberEng
2/⬇️
MLflow main components:
+MLflow Tracking
+MLflow Projects
+MLflow Models
+MLflow Model Registry
3/⬇️
thesequence.substack.com/p/edge12-the-c…
Read 6 tweets
TheSequence Profile picture
28 May
TensorFlow Serving = the first mainstream model serving architecture in ML frameworks

+It serves ML models inside Google
+It is available in the cloud and via open-source

How it was created and how it works?
Thread⬇️
Deep dive into "TensorFlow-Serving: Flexible, High-Performance ML Serving" by @JeremiahHarmsen, @FangweiLi, @sukritiramesh, Christopher Olston, Noah Fiedel, Kiril Gorovoy, Li Lao, Vinu Rajashekhar, Jordan Soyke

2/⬇️
Paper outlined the architecture of a serving pipeline for @TensorFlow models

Capabilities of TensorFlow serving:

+model lifecycle management;
+experiments with multiple algorithms;
+efficient use of GPU resources
3/⬇️
Read 6 tweets
TheSequence Profile picture
24 May
Model serving = processes of operationalizing a machine learning model for production.

OR what most normal software developers call ‘deployment’. Read more about it.

Thread⬇️
thesequence.substack.com/p/edge12-the-c…
Model serving goes a bit beyond deployment, given the unique nature of the lifecycle of ML programs.

ML models operate in a circular lifecycle, where phases such as training and optimization are continuously repeated.
2/⬇️
Some of the most important aspects of any model serving pipeline:
+API interface
+real-time vs. batch execution
+versioning
+A/B testing
+scalability
3/⬇️
Read 7 tweets
TheSequence Profile picture
22 May
PySyft = open-source framework for private deep learning that enables secure, private computations

Thread🧵👇
thesequence.substack.com/p/-edge30-priv…
❓PySyft combines several privacy techniques:
+federated learning
+ secured multiple-party computations
+differential privacy

into a single programming model integrated into different deep learning frameworks such as @PyTorch, Keras & @TensorFlow
2/⬇️
The core component of PySyft = abstraction called the SyftTensor

SyftTensors represent a state or transformation of the data and can be chained together
3/⬇️
Read 5 tweets

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