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adchia merged 4 commits into from
Sep 5, 2023
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docs: Add Dragonfly as an online-store option to Feast Readme and docs #3647

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Add dragonfly to feast Readme and docs
yirenlu92 Jun 8, 2023
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Remove new lines
yirenlu92 Jul 20, 2023
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yirenlu92 Jul 20, 2023
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rebase and fix using template generation
adchia Sep 5, 2023
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Remove new lines 
Signed-off-by: Danny C <d.chiao@gmail.com>
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@yirenlu92 @adchia
yirenlu92 authored and adchia committed Sep 5, 2023
commit effda9a8951cc5cf21dc4dd6de92b17aefb5f56f
13 changes: 1 addition & 12 deletions README.md
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Expand Up @@ -29,42 +29,36 @@ Feast allows ML platform teams to:
Please see our [documentation](https://docs.feast.dev/) for more information about the project, or sign up for an [email newsletter](https://feast.dev/).

## 📐 Architecture

![](docs/assets/feast_marchitecture.png)

The above architecture is the minimal Feast deployment. Want to run the full Feast on Snowflake/GCP/AWS? Click [here](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws).

## 🐣 Getting Started

### 1. Install Feast

```commandline
pip install feast
```

### 2. Create a feature repository

```commandline
feast init my_feature_repo
cd my_feature_repo/feature_repo
```

### 3. Register your feature definitions and set up your feature store

```commandline
feast apply
```

### 4. Explore your data in the web UI (experimental)

![Web UI](ui/sample.png)

```commandline
feast ui
```

### 5. Build a training dataset

```python
from feast import FeatureStore
import pandas as pd
Expand Down Expand Up @@ -96,7 +90,6 @@ print(training_df.head())
# Train model
# model = ml.fit(training_df)
```

```commandline
event_timestamp driver_id conv_rate acc_rate avg_daily_trips
0 2021-04-12 08:12:10+00:00 1002 0.713465 0.597095 531
Expand All @@ -107,7 +100,6 @@ print(training_df.head())
```

### 6. Load feature values into your online store

```commandline
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
Expand All @@ -118,7 +110,6 @@ Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 don
```

### 7. Read online features at low latency

```python
from pprint import pprint
from feast import FeatureStore
Expand All @@ -139,7 +130,6 @@ pprint(feature_vector)
# Make prediction
# model.predict(feature_vector)
```

```json
{
"driver_id": [1001],
Expand Down Expand Up @@ -228,7 +218,6 @@ Please refer to the official documentation at [Documentation](https://docs.feast
## 👋 Contributing

Feast is a community project and is still under active development. Please have a look at our contributing and development guides if you want to contribute to the project:

- [Contribution Process for Feast](https://docs.feast.dev/project/contributing)
- [Development Guide for Feast](https://docs.feast.dev/project/development-guide)
- [Development Guide for the Main Feast Repository](./CONTRIBUTING.md)
Expand All @@ -239,4 +228,4 @@ Thanks goes to these incredible people:

<a href="https://github.com/feast-dev/feast/graphs/contributors">
<img src="https://contrib.rocks/image?repo=feast-dev/feast" />
</a>
</a>