You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@iotdb.apache.org by qi...@apache.org on 2024/01/17 09:41:12 UTC
(iotdb-docs) branch main updated: Fix the img of AInode doc by English (#152)
This is an automated email from the ASF dual-hosted git repository.
qiaojialin pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/iotdb-docs.git
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 <10...@users.noreply.github.com>
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