-Remote Data Science - Code for `computing on data`, you `do not own` and `cannot see`
+Perform `numpy`-like analysis on `data` that remains in `someone else's` server
@@ -24,7 +24,7 @@ Remote Data Science - Code for `computing on data`, you `do not own` and `cannot
💻 `hagrid quickstart`
- In the tutorial you will learn how to install and deploy:
- `PySyft` = our `torch`-like 🐍 Python Library
+ `PySyft` = our `numpy`-like 🐍 Python Library
`PyGrid` = our 🐳 `docker` / `k8s` Data Platform
- During quickstart we will deploy `PyGrid` to localhost with 🐳 `docker`, however 🛵 HAGrid can deploy to `k8s` or a 🐧 `ubuntu` VM on `azure` / `gcp` / `ANY_IP_ADDRESS` by using 🔨 `ansible`†
@@ -36,9 +36,9 @@ Remote Data Science - Code for `computing on data`, you `do not own` and `cannot
✅ `Linux` ✅ `macOS`\* ✅ `Windows`†‡
-- `HAGrid` = our our handy 🛵 cli tool
-- `PySyft` = our `torch`-like 🐍 Python Library
-- `PyGrid` = our 🐳 `docker` / `k8s` Data Platform
+- `PySyft` = our `numpy`-like 🐍 Python library for computing on `private data` in someone else's `Domain` server
+- `PyGrid` = our 🐳 `docker` / `k8s` / 🐧 `vm` `Domain` & `Network` Servers where `private data` lives
+- `HAGrid` = our our handy 🛵 cli tool which makes `deploying` a `Domain` or `Network` server a one-liner
3. Read our 📚 Docs
4. Ask Questions ❔ in `#support` on Slack
@@ -64,7 +64,8 @@ Remote Data Science - Code for `computing on data`, you `do not own` and `cannot
PySyft and PyGrid use the same `version` and its best to match them up where possible. We release weekly betas which can be used in each context:
PySyft: `pip install syft --pre`
PyGrid: `hagrid launch ... tag=latest`
-Quickstart: `hagrid quickstart --pre`
+
+
HAGrid is a cli / deployment tool so the latest version of `hagrid` is usually the best.
@@ -74,11 +75,11 @@ HAGrid is a cli / deployment tool so the latest version of `hagrid` is usually t
-`Syft` is OpenMined's `open source` stack that provides `secure` and `private` Data Science in Python. Syft decouples `private data` from model training, using techniques like [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). This is done with a `torch`-like interface and integration with `Deep Learning` frameworks so that you as a `Data Scientist` can maintain your current workflow while using these new `privacy-enhancing techniques`.
+`Syft` is OpenMined's `open source` stack that provides `secure` and `private` Data Science in Python. Syft decouples `private data` from model training, using techniques like [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). This is done with a `numpy`-like interface and integration with `Deep Learning` frameworks, so that you as a `Data Scientist` can maintain your current workflow while using these new `privacy-enhancing techniques`.
### Why should I use Syft?
-`Syft` allows a `Data Scientist` to ask `questions` about a `dataset` and, within `privacy limits` set by the `data owner`, get `answers` to those `questions`, all without obtaining a `copy` of the data itself. We call this process `Remote Data Science`. It means in a wide variety of `domains` across society, the current `risks` of sharing information (`copying` data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the ability to utilize the vast `benefits` such as innovation, insights and scientific discovery.
+`Syft` allows a `Data Scientist` to ask `questions` about a `dataset` and, within `privacy limits` set by the `data owner`, get `answers` to those `questions`, all without obtaining a `copy` of the data itself. We call this process `Remote Data Science`. It means in a wide variety of `domains` across society, the current `risks` of sharing information (`copying` data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the vast `benefits` such as innovation, insights and scientific discovery which secure access will provide.
No more cold calls to get `access` to a dataset. No more weeks of `wait times` to get a `result` on your `query`. It also means `1000x more data` in every domain. PySyft opens the doors to a streamlined Data Scientist `workflow`, all with the individual's `privacy` at its heart.
@@ -222,7 +223,7 @@ Provides services to a group of `Data Owners` and `Data Scientists`, such as dat
🎥 Privacy-Preserving Data Science - TWiML Talk #241🎥 Privacy Preserving AI - PyTorch Devcon 2019
📖 Towards general-purpose infrastructure for protecting ...
-📖 Syft 0.5: A platform for universally deployable structured ...
+📖 Syft 0.5: A platform for universally deployable ...
📖 A generic framework for privacy preserving deep learning
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