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The following commit(s) were added to refs/heads/main by this push:
new b3de5f2 Fix the img of AInode doc by English (#152)
b3de5f2 is described below
commit b3de5f21ee4a9b5a9813ff6cb0ccea2c5568e161
Author: wanghui42 <[email protected]>
AuthorDate: Wed Jan 17 17:41:08 2024 +0800
Fix the img of AInode doc by English (#152)
---
src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md | 4 ++--
src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md | 4 ++--
2 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md
b/src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md
index a4847c9..cae9f95 100644
--- a/src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md
+++ b/src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it
has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in
IoTDB can be directly applied to the inference of machine learning models
without having to move the data to a separate machine learning service
platform, which accelerates data processing, improves security, and reduces
costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning
analytics algorithms covering typical timing analysis tasks, empowering the
timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time
series; thus outputs the most likely prediction of future series based on
observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it
has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or
home-grown models for common timing analysis scenarios (e.g., prediction and
anomaly detection).
::: center
-<img src="https://alioss.timecho.com/docs/img/h3.PNG"; style="zoom:50%" />
+<img src="https://alioss.timecho.com/upload/AInode2.png"; style="zoom:50%" />
::::
## 3. Installation and Deployment
diff --git a/src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md
b/src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md
index a4847c9..cae9f95 100644
--- a/src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md
+++ b/src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it
has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in
IoTDB can be directly applied to the inference of machine learning models
without having to move the data to a separate machine learning service
platform, which accelerates data processing, improves security, and reduces
costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning
analytics algorithms covering typical timing analysis tasks, empowering the
timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time
series; thus outputs the most likely prediction of future series based on
observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it
has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or
home-grown models for common timing analysis scenarios (e.g., prediction and
anomaly detection).
::: center
-<img src="https://alioss.timecho.com/docs/img/h3.PNG"; style="zoom:50%" />
+<img src="https://alioss.timecho.com/upload/AInode2.png"; style="zoom:50%" />
::::
## 3. Installation and Deployment
![https://alioss.timecho.com/docs/img/h3.PNG"](https://alioss.timecho.com/docs/img/h3.PNG"){kind=link}
![https://alioss.timecho.com/upload/AInode2.png"](https://alioss.timecho.com/upload/AInode2.png"){kind=link}