[DOCS] Adds data frame analytics at scale page to the ML DFA docs (#1394) (#1395)

Author: szabosteve

docs/en/stack/ml/df-analytics/index.asciidoc

@@ -3,6 +3,7 @@ include::ml-dfanalytics.asciidoc[]
 include::ml-dfa-overview.asciidoc[leveloffset=+1]
 include::ml-supervised-workflow.asciidoc[leveloffset=+2]
 include::ml-dfa-phases.asciidoc[leveloffset=+2]
+include::ml-dfa-scale.asciidoc[leveloffset=+2]
 
 include::ml-dfa-concepts.asciidoc[leveloffset=+1]
 include::dfa-outlier-detection.asciidoc[leveloffset=+2]

docs/en/stack/ml/df-analytics/ml-dfa-scale.asciidoc

@@ -0,0 +1,154 @@
+[role="xpack"]
+[[ml-dfa-scale]]
+= Working with {dfanalytics} at scale
+
+A {dfanalytics-job} has numerous configuration options. Some of them may have a 
+significant effect on the time taken to train a model. The training time depends 
+on various factors, like the statistical characteristics of your data, the 
+number of provided hyperparameters, the number of features included in the 
+analysis, the hardware you use, and so on. This guide contains a list of 
+considerations to help you plan for training {dfanalytics} models at scale and 
+optimizing training time.
+
+In this guide, you’ll learn how to:
+
+* Understand the impact of configuration options on the time taken to train 
+  models for {dfanalytics-jobs}.
+
+
+**Prerequisites:**
+
+This guide assumes you’re already familiar with: 
+
+* How to create data frame analytics jobs. If not, refer to <<ml-dfa-overview>>.
+
+* How data frame analytics jobs work. If not, refer to <<ml-dfa-phases>>.
+
+It is important to note that there is a correlation between the training time, 
+the complexity of the model, the size of the data, and the quality of the 
+analysis results. Improvements in quality, however, are not linear with the 
+amount of training data; for very large source data, it might take hours to 
+train a model for very small gains in quality. When you work at scale with 
+{dfanalytics}, you need to decide what quality of results is acceptable for your 
+use case. When you have determined your acceptance criteria, you have a better 
+picture of the factors you can trade off while still achieving your goal.
+
+
+The following recommendations are not sequential – the numbers just help to 
+navigate between the list items; you can take action on one or more of them in 
+any order.
+
+
+[discrete]
+[[rapid-iteration]]
+== 0. Start small and iterate rapidly
+
+Training is an iterative process. Experiment with different settings and 
+configuration options (including but not limited to hyperparameters and feature 
+importance), then evaluate the results and decide whether they are good enough 
+or need further experimentation.
+
+Every iteration takes time, so it is useful to start with a small set of data so 
+you can iterate rapidly and then build up from here.
+
+
+[discrete]
+[[small-training-percent]]
+== 1. Set a small training percent
+
+(This step only applies to {regression} and {classification} jobs.)
+
+The number of documents used for training a model has an effect on the training 
+time. A higher training percent means a longer training time.
+
+Consider starting with a small percentage of training data so you can complete 
+iterations more quickly. Once you are happy with your configuration, increase 
+the training percent.  As a rule of thumb, if you have a data set with more than 
+100,000 data points, start with a training percent of 5 or 10.
+
+
+[discrete]
+[[disable-feature-importance]]
+== 2. Disable {feat-imp} calculation
+
+(This step only applies to {regression} and {classification} jobs.)
+
+<<ml-feature-importance>> indicates which fields had the biggest impact on each 
+prediction that is generated by the analysis. Depending on the size of the data 
+set, {feat-imp} can take a long time to compute.
+
+For a shorter runtime, consider disabling {feat-imp} for some or all iterations 
+if you do not require it.
+
+
+[discrete]
+[[optimize-included-fields]]
+== 3. Optimize the number of included fields 
+
+You can speed up runtime by only analyzing relevant fields.
+
+By default, all the fields that are supported by the analysis type are included 
+in the analysis. In general, more fields analyzed requires more resources and 
+longer training times, including the time taken for automatic feature selection. 
+To reduce training time, consider limiting the scope of the analysis to the 
+relevant fields that contribute to the prediction. You may do this by either 
+excluding non-relevant fields or by including relevant ones.
+
+NOTE: {feat-imp-cap} can help you determine the fields that contribute most to 
+the prediction. However, as calculating {feat-imp} increases training time, this 
+is a trade-off that can be evaluated during an iterative training process. 
+
+
+[discrete]
+[[increase-threads]]
+== 4. Increase the maximum number of threads
+
+You can set the maximum number of threads that are used during the analysis. The 
+default value of `max_num_threads` is 1. Depending on the characteristics of the 
+data, using more threads may decrease the training time at the cost of increased 
+CPU usage. Note that trying to use more threads than the number of CPU cores has 
+no advantage.
+
+Hyperparameter optimization and calculating {feat-imp} gain the most benefit 
+from the increased number of threads. This can be seen in phases 
+`coarse_parameter_search`, `fine_tuning_parameters`, and `writing_results`. The 
+rest of the phases are not affected by the increased number of threads.
+
+To learn more about the individual phases, refer to <<ml-dfa-phases>>.
+
+NOTE: If your {ml} nodes are running concurrent {anomaly-detect} or 
+{dfanalytics-jobs}, then you may want to keep the maximum number of threads set 
+to a low number – for example the default 1 – to prevent jobs competing for 
+resources.
+
+
+[discrete]
+[[optimize-source-index]]
+== 5. Optimize the size of the source index
+
+Even if the training percent is low, reindexing the source index – which is a 
+mandatory step in the job creation process – may take a long time. During 
+reindexing, the documents from the source index or indices are copied to the 
+destination index, so you have a static  copy of the analyzed data. 
+
+If your data is large and you do not need to test and train on the whole source 
+index or indices, then reduce the cost of reindexing by using a subset of your 
+source data. This can be done by either defining a filter for the source index 
+in the {dfanalytics-job} configuration, or by manually reindexing a subset of 
+this data to use as an alternate source index.
+
+
+[discrete]
+[[configure-hyperparameters]]
+== 6. Configure hyperparameters
+
+(This step only applies to {regression} and {classification} jobs.)
+
+<<hyperparameters>> is the most complicated mathematical process during model 
+training and may take a long time.
+
+By default, optimized hyperparameter values are chosen automatically. It is 
+possible to reduce the time taken at this step by manually configuring 
+hyperparameters – if you fully understand the purpose of the hyperparameters and 
+have a sensible value for any or all of them. This reduces the computing load 
+and therefore decreases training time.