Update serverless-tutorial.md (#5360)

site/content/en/docs/manual/advanced/serverless-tutorial.md

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 ## Introduction
 
-Computers have now become our partners. They help us to solve routine problems,
-fix mistakes, find information, etc. It is a natural idea to use their
-compute power to annotate datasets. There are multiple DL models for
-classification, object detection, semantic segmentation which can do
-data annotation for us. And it is relatively simple to integrate your
-own ML/DL solution into CVAT.
-
-But the world is not perfect and we don't have a silver bullet which can
-solve all our problems. Usually, available DL models are trained on public
-datasets which cannot cover all specific cases. Very often you want to
-detect objects which cannot be recognized by these models. Our annotation
-requirements can be so strict that automatically
-annotated objects cannot be accepted as is, and it is easier to annotate them
-from scratch. You always need to keep in mind all these mentioned limitations.
-Even if you have a DL solution which can
-_perfectly_ annotate 50% of your data, it means that manual work will only be
-reduced in half.
-
-When we know that DL models can help us to annotate data faster, the next
-question is how to use them? In CVAT all such DL models are implemented
-as serverless functions for the [Nuclio][nuclio-homepage] serverless platform.
-And there are multiple implemented functions which can be
+Leveraging the power of computers to solve daily routine problems,
+fix mistakes, and find information has become second nature. It is therefore
+natural to use computing power in annotating datasets. There are multiple
+publicly available DL models for classification, object detection, and semantic
+segmentation which can be used for data annotation. Whilst some of these publicly
+available DL models can be found on CVAT, it is relatively simple to integrate your
+privately trained ML/DL model into CVAT.
+
+With the imperfection of the world, alongside the unavailability of a silver bullet
+that can solve all our problems; publicly available DL models cannot be used when we
+want to detect niche or specific objects on which these publicly available models were not trained.
+As annotation requirements can be sometimes strict, automatically annotated objects cannot be accepted
+as it is, and it is easier to annotate them from scratch. With these limitations in mind, a DL solution
+that can _perfectly_ annotate 50% of your data equates to reducing manual annotation by half.
+
+Since we know DL models can help us to annotate faster, how then do we use them?
+In CVAT all such DL models are implemented as serverless functions using the [Nuclio][nuclio-homepage]
+serverless platform. There are multiple implemented functions that can be
 found in the [serverless][cvat-builtin-serverless] directory such as _Mask RCNN,
 Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc.
 Follow [the installation guide][cvat-auto-annotation-guide] to build and deploy