Whiteboard Technical Series: Natural Language Processing

A screenshot from the video showing a diagram. The subhead says, “WORDZVEC.” Below that there’s a single circle, with arrows pointing from it downward to 10 other circles.

Everything you need to know about NLP is right here.

This short but informative episode of the Whiteboard Technical Series explores how Juniper Mist AI™ uses Natural Language Processing (NLP) and key data science tools to power the AI-driven enterprise. Using NLP and Marvis, problems that would normally take days to resolve are solved with a single question. Watch and learn more about how NLP works, and how it can move your business forward. 

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You’ll learn

  • How NLP enables Marvis to become a virtual member of your IT operations team 

  • The important benefits NLP brings to your enterprise

Who is this for?

Business Leaders Network Professionals


0:10 today in the tech whiteboard series

0:12 we talk about nlp or natural language

0:15 processing

0:16 and how it impacts networking and more

0:18 specifically

0:19 ai ops natural language processing gives

0:23 machines the ability to derive meaning

0:25 from human language

0:26 nlp is a combination of linguistics and

0:28 ai specifically

0:30 machine learning right here is where nlp

0:33 lies

0:35 let's take a look at a question you

0:36 might ask marvis our virtual network

0:38 assistant

0:40 nlp converts this question into more

0:42 general meaning that our models know how

0:44 to interpret

0:45 to provide you with actionable insights

0:47 about your network needs

0:49 let's take a look at what's really

0:50 happening here the first step in nlp is

0:53 to clean up the text

0:54 and convert the words into a form the

0:56 computer can understand

0:58 first stop words or unimportant

1:01 information like and

1:02 and the are removed the remaining text

1:05 is then split into smaller units like

1:07 words and phrases

1:09 a process called tokenization next

1:12 featurization occurs meaning each word

1:14 is transformed into a vector

1:17 vectors numerically capture the features

1:19 or information about a word in a way

1:21 that the computer can understand and

1:23 process

1:24 here's an example of vectorized words

1:27 more semantically similar words fall

1:29 closer together

1:31 this is a crucial concept that allows

1:33 nlp to be possible

1:36 vector representations of words can

1:37 extend past 3d

1:39 higher dimensional vectors can

1:41 numerically capture more meaning about a

1:43 word

1:44 while each word is represented by a

1:46 vector we need to come up with an

1:47 encoded vector representation for the

1:49 overall sentence

1:51 sentence encoded vectors are valuable

1:54 because they allow information about the

1:55 order of the words to be captured

1:57 because words can have varying meanings

1:59 depending on their context

2:00 or position in a sentence at this point

2:03 in the process

2:05 embedding models are used embedding

2:07 models map

2:08 categorical data such as words or

2:10 sentences into high dimensional vectors

2:12 which capture semantic meaning about the

2:14 text

2:15 embedding models are usually pre-trained

2:17 on a large amount of data outside of

2:19 your own

2:20 like wikipedia which harnesses the power

2:22 of transfer learning

2:24 or leveraging prior knowledge from one

2:26 domain and task

2:27 into a different domain and task an

2:30 example of a pre-trained embedding model

2:32 is word to vec

2:33 which is trained on all the word data in

2:34 wikipedia meaning it's able to embed

2:37 extra meaning about the semantics of

2:39 text

2:39 into vectors because it's learned from

2:41 so many examples

2:42 what words can mean in certain contexts

2:45 the embeddings can now be fed into a

2:47 machine learning model

2:49 the machine learning model learns how to

2:50 understand the meaning of unseen words

2:53 by comparing

2:54 the similarity between the input word

2:56 vectors and the word vectors whose

2:57 meanings are known from your training

2:59 data

3:00 you can make sure that your model is

3:02 able to recognize certain meanings by

3:04 including them in your training data set

3:07 words that are semantically similar will

3:09 be closer in multi-dimensional space

3:11 which is how the model learns how to

3:13 predict the meaning of unseen words

3:15 the closest vector with a known meaning

3:16 in the vector space is the predicted

3:18 meaning

3:20 as a result of decades of

3:21 troubleshooting top-tier networks at

3:23 juniper

3:23 we've created a high-level structured

3:25 set of training data born from decades

3:27 of in-depth networking knowledge

3:30 we take real customer questions and

3:32 annotate them to create our training

3:34 data set

3:35 annotation includes flagging the tokens

3:37 in the question as intense

3:39 intended actions like troubleshoot count

3:42 list

3:43 or entities information about the intent

3:45 like device name or time frame

3:48 the training data questions are also

3:50 made into vectors and sentence encoded

3:52 vectors

3:53 with information about the annotation

3:54 flags

3:56 so when unseen questions are asked our

3:58 model can predict the user's desired

4:00 intents and entities

4:01 based on how similar the unseen vectors

4:03 are to the known vectors which have been

4:05 trained

4:07 the benefits of nlp are clear resolving

4:10 network issues in minutes

4:11 not days just by asking a single

4:13 question as opposed to poking around the

4:16 network looking for clues

4:17 and the versatility of using that same

4:19 interface to perform tasks

4:21 such as firmware upgrades allows marvis

4:24 to become a virtual member of the it

4:26 operations team

4:28 in networking we use nlp to allow our

4:30 customers to interface with marvis

4:32 pushing ai ops to the next level

4:35 we hope this episode helped uncover some

4:37 of the magic and mystery behind our

4:39 ai-driven network solutions

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