🤖@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…
It bridges the gap between 
+data science research teams focused on experimentation
AND
+ML engineers tasked with deploying and scaling models in production
4/⬇️
MLflow is open source at github.com/mlflow/mlflow
5/⬇️
TheSequence Edge covers:
+ML concept you should learn
+Review of an impactful research paper
+New ML framework or platform and how you can use it
thesequence.substack.com/subscribe
6/6

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More from @TheSequenceAI

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
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
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
21 May
Deep dive into "Scalable Private Learning with PATE" by @NicolasPapernot @_kunal_talwar_ @UlfarEr Shuang Song, Ilya Mironov, Ananth Raghunathan

It presents a Private Aggregation of Teacher Ensembles (PATE) method to ensure privacy in training datasets
Thread👇🏼 🔎
Imagine that two different models, trained on two different datasets produce similar outputs

Then, their decision does not reveal information about any single training example

And this is another way to say it ensures the privacy of the training data
2/⬇️
PATE uses a perturbation technique that structures the learning process using an ensemble of teacher models communicating their knowledge to a student model
3/⬇️
Read 6 tweets
19 May
Parallel training Recap⬇️

1. The concept of parallel training
2. Impactful research paper
3. Open-source framework for parallel training

Thread👇
Paper proposing one of the 1st statistical metrics to effectively quantify the correct size of the training batch

3/⬇️
Read 4 tweets
8 May
AllenNLP @allen_ai = an Important Framework for NLU Researchers
Thread🧵👇
thesequence.substack.com/p/-edge22-mach…
❓AllenNLP:
+includes key building blocks for NLU
+offers state of the art NLU methods
+facilitates the work of researchers
thesequence.substack.com/p/-edge22-mach…
2/
AllenNLP is built on top of @PyTorch and designed with experimentation in mind

Key contribution = maintains implementations of new models:
+text generation,
+question answering,
+sentiment analysis
+& many others
3/
Read 5 tweets

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