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Diffstat (limited to 'docs/streaming-programming-guide.md')
| -rw-r--r-- | docs/streaming-programming-guide.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 2ee3b80185..de82a064d1 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -15,7 +15,7 @@ like Kafka, Flume, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like `map`, `reduce`, `join` and `window`. Finally, processed data can be pushed out to filesystems, databases, and live dashboards. In fact, you can apply Spark's -[machine learning](mllib-guide.html) and +[machine learning](ml-guide.html) and [graph processing](graphx-programming-guide.html) algorithms on data streams. <p style="text-align: center;"> @@ -1673,7 +1673,7 @@ See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more abo *** ## MLlib Operations -You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. +You can also easily use machine learning algorithms provided by [MLlib](ml-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](ml-guide.html) guide for more details. *** |