##Note that this workshop is not yet live. It will be live at reinvent 2017 Wednesday 11/29

Workshop Overview

In this workshop you will build a voice powered analytic engine that you can take back to your stakeholders to deliver valuable company insights. Common questions that may be asked, “Alexa, how many Unique Users did our site have last month?” and “Alexa, how many orders have breached their delivery SLA this week?”.

The primary products used in this workshop are:

All workshop attendees will need an AWS account with access to the following products.
Note For re:invent attendees: AWS will not be providing temporary accounts for this workshop. Expected costs for this workshop is < $1 (Assuming free tier eligibility), AWS will be providing $10 AWS credits to cover the expense of the workshop.

Pre-workshop Checklist

Please make sure you have the following availabile prior to the workshop.

Amazon Developer account
AWS Account with root access or full access

Note There are two steps that differ from the typical AWS workflow.

Development of an Alexa skill requires creation of an account at Amazon Developer If you have not created an account yet, please do so before the workshop.
Using QuickSight requires signing up for the service on a per user basis. Please conplete this step before the workshop to save on time.

Workshop Steps

This workshop is designed for first time users of Athena and Alexa. We have broken the workshop into three Steps or focus topics. These are:

Athena and Data Discovery Steps
Alexa Skill Building Steps

We expect most attendees to be able to complete both the Alexa Skill Building and Athena and Data Discovery Steps.

We have provided cloud formation templates and solutions for all steps where the attende is expected to write code. Generally these come in two flavors:

Full solution where the attendee does not have to write any code
Partial solution where the attendee can author key selections of the code and double check thier work. This path is the recomended path as it provides for the most learning. If time becomes an issue, attendes will always have access to the full solitions so be bold!
Many sections also have Bonus Sections where you can build additional capability on top of the workshop. While there aren't hard-and-fast answers for the bonus sections, feel free to engage your workshop facilitator(s)/lab assistant(s) if you'd like additional assistance with these areas.

In addition to deciding between using the full or partial solutions. The attende can also choose to focus in on the Big Data portion or the Voice powered portion. Please spend time in the workshop you find most interesting and use our full solutions for anything you have already mastered or are not interested in. The partial solution is designed to give you a head start, but still require key additions from the attende.

Region	Launch Template
Ireland (eu-west-1)

TODO This needs to create additional DDB tables as well

BI and Data Discovery Steps

Step 0 - Understand Raw Data Set To Query

We will be using a dataset created from Twitter data related to AWS re:Invent 2017. In short, tweets with the #reinvent hashtag or to/from @awsreinvent Let's first take a look at the data set we're going to analyze and query.

How we get the data into S3

The data is acquired starting with a Cloudwatch Event triggering an AWS Lambda function every 1 minute. Lambda is making calls to Twitter's APIs for data, and then ingesting the data into AWS Kinesis Firehose. Firsthose then microbatches the results into S3 as shown in the following diagram:

Step 1 - Explore the Twitter data

This dataset is available as:

US-EAST-1 
s3://aws-vpa-tweets/
EU-WEST-1
s3://aws-vpa-tweets-euw1/

Partial solution - Explore the Twitter Data

AWS Firehose delivers the data into S3 as a GZIP file format. There are 3 ways to get to this public dataset:

Download a sample extract of the data at the following File Location
Using the AWS CLI
Using a 3rd party S3 File Explorer such as Cloudberry Explorer

Full solution - Explore the Twitter Data Using AWS CLI

When using the AWS CLI, you can run the following commands to see the folder/prefix structure of the data. Firehose delivers the data in micro-batches by time. When navigating to the file itself, it is in the GZIP file format:

$ aws s3 ls s3://aws-vpa-tweets-euw1/
         PRE tweets/
$ aws s3 ls s3://aws-vpa-tweets-euw1/tweets/
         PRE 2017/
$ aws s3 ls s3://aws-vpa-tweets-euw1/tweets/2017/
         PRE 10/
         PRE 11/
$ aws s3 ls s3://aws-vpa-tweets-euw1/tweets/2017/11/
         PRE 01/
         PRE 02/
         PRE 03/
         PRE 04/
         PRE 05/
         PRE 06/
$ aws s3 ls s3://aws-vpa-tweets-euw1/tweets/2017/11/06/
         PRE 00/
         PRE 01/
         PRE 02/
         PRE 03/
         PRE 04/
$ aws s3 ls s3://aws-vpa-tweets-euw1/tweets/2017/11/06/04/
2017-11-05 20:09:30        270 aws-vpa-tweets-1-2017-11-06-04-08-28-f5542a86-818d-4b7a-8d84-aaff9ea4bec9.gz

Let's download the file by copying it locally and then using our favorite editor to open it:

$ aws s3 cp s3://aws-vpa-tweets-euw1/tweets/2017/11/06/04/aws-vpa-tweets-1-2017-11-06-04-08-28-f5542a86-818d-4b7a-8d84-aaff9ea4bec9.gz .
download: s3://aws-vpa-tweets-euw1/tweets/2017/11/06/04/aws-vpa-tweets-1-2017-11-06-04-08-28-f5542a86-818d-4b7a-8d84-aaff9ea4bec9.gz to ./aws-vpa-tweets-1-2017-11-06-04-08-28-f5542a86-818d-4b7a-8d84-aaff9ea4bec9.gz
$ vi aws-vpa-tweets-1-2017-11-06-04-08-28-f5542a86-818d-4b7a-8d84-aaff9ea4bec9.gz

The data format looks something like this:

{  
	"id": 914642318044655600,  
	"text": "Two months until #reInvent! Check out the session calendar & prepare for reserved seating on Oct. 19! http://amzn.to/2fxlVg7  ",  
	"created": "2017-10-02 00:04:56",  
	"screen_name": " AWSReinvent ",  
	"screen_name_followers_count": 49864,  
	"place": "none",  
	"country": "none",  
	"retweet_count": 7,  
	"favorite_count": 21
}

In the next section, we're going to use these fields to create HIVE external tables in AWS Athena and query the data directly in S3.

Step 2 - Create an Athena table for Initial Data Discovery

Now we're comfortable with the dataset, let's create a table in Athena on top of this data. There is no need to copy the dataset to a new bucket for the workshop. The data is publicly available. You will however need to create an Athena database and table to query the data. The twitter data is compressed in s3, but in a JSON syntax which Athena has native support for.

Full solution - Create Athena table (expand for details)

In your AWS account navigate to the Athena service
In the top left menu, choose Query Editor.
To create a new table, use this code to create the HIVE external table Data Definition Language (DDL):

CREATE EXTERNAL TABLE tweets(
  id bigint COMMENT 'Tweet ID', 
  text string COMMENT 'Tweet text', 
  created timestamp COMMENT 'Tweet create timestamp', 
  screen_name string COMMENT 'Tweet screen_name',
  screen_name_followers_count int COMMENT 'Tweet screen_name follower count',
  place string COMMENT 'Location full name',
  country string COMMENT 'Location country',
  retweet_count int COMMENT 'Retweet count', 
  favorite_count int COMMENT 'Favorite count')
ROW FORMAT SERDE 
  'org.openx.data.jsonserde.JsonSerDe' 
WITH SERDEPROPERTIES ( 
  'paths'='id,text,created,screen_name,screen_name_followers_count,place_fullname,country,retweet_count,favorite_count') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  's3://aws-vpa-tweets/tweets/'

Then hit the Run Query button
In a few seconds, you'll see an Athena table called tweets in the default database (You may have to hit refresh).
If you click on the tweets table, you can see the fields that are in our raw S3 data.
Let's test that the tweets table works. In the same Query Editor run the following SELECT statement (clear the previous statement):

SELECT COUNT(*) AS TOTAL_TWEETS FROM tweets;

The statement above shows the total amount of tweets in our data set (result value should be well over 8k).

Partial solution - Create Athena table using Glue(expand for details)

TODO: Use AWS Glue to discover and build a DDL.

Step 3 - Explore the data using Quicksight

We've created an Athena table directly on top of our S3 Twitter data, let's explore some insights on the data. While this can be achieved through Athena itself or compatible query engines, Amazon Quicksight enables you to connect directly to Athena and quickly visualize it into charts and graphs without writing any SQL code. Let's explore:

Full solution - Explore Athena data in Quicksight

Launch the QuickSight portal. This may ask you to register your email address for Quicksight access.
If haven't already configured, Quicksight may need special permissions to access Athena:
a. (These settings can only be changed in the N.Virginia region) In the upper right corner, ensure US East N. Virginia is selected, then to the right of the region in the upper right corner, choose your profile name, and from the dropdown menu, choose Manage Quicksight.
b. On the left menu, click Account Settings
c. Click the Edit AWS permissions button
d. Ensure the box Amazon Athena is checked, then click Apply
In the main Quicksight portal page (ensure you're in the EU Ireland Region)
In the upper right choose your Manage data
Now in the upper left choose New data set
You will see tiles for each of the QuickSight supported data sources. From this page select the Athena tile.
When asked for the dataset name you can choose anything you like, for our example we use tweets-dataset You can choose to validate that SSL will be used for data between Athena and QuickSight. Finish be selecting Create data source
Now we need to choose the Athena table we created in Step 1. For our example we used the Default database, with a table name of tweets. Finish by clicking on Select.
You will now be asked if you want to use spice or direct query access. If in the Ireland region, choose direct query access (SPICE is not yet available in this region). Click Visualize when done.
QuickSight will now import the data. Wait until you see Import Complete. Then close the summary window.
Add the created field from the Athena table by dragging it from the Field list to the empty AutoGraph window.
From the Visual types in the botom left corner, select Vertical bar chart
Add another Visual by selecting in the top left corner, the + Add button and then Add visual
On this new graph, lets add the country field.
As you can see, lots of tweets do not include which country the tweet was created in. Lets filter these results out. Click on the large bar labeled none, then select exclude "none" from the pop up window. As you can see the tweets without a location were excluded.
Lets change the visual from a bar chart to a pie chart. Select the entire visual, then from the bottom right select the pie chart visual. Add Group By: "none"

Bonus: What other interesting insights can you find from this data in Quicksight

Step 4 - Create a query to find the number of reinvent tweets

Full solution - Athena Query (expand for details)

We need to produce an integer for our Alexa skill. To do that we need to create a query that will return our desired count.
To find the last set of queries from Quicksight, go to the Athena AWS Console page, then select History on the top menu.
You can see the latest queries under the column Query (starting with the word 'SELECT'). You can copy these queries to a text editor to save later.
We'll be running these queries in the Query Editor. Navigate there in the top Athena menu.
Ensure that the default database is selected and you'll see our tweets table.
The Athena syntax is widely compatable with Presto. You can learn more about it from our Amazon Athena Getting Started and the Presto Docs web sites
Once you are happy with the value returned by your query you can move to Step 4, otherwise you can experiment with other query types.
Let's write a new query, there are several ways that you can do this:
a. Use one of the queries that we had selected from the Query Editor
b. Write a new query using the Presto SELECT format Hint: The Query text to find the number of #reinvent tweets is: SELECT COUNT(*) FROM tweets
TODO Show advanced query building techniques for our dataset
c. Use or build off one of th examples below:

A few examples of other queries are listed below.

--Total number of tweets
SELECT COUNT(*) FROM tweets

--Total number of tweets in last 3 hours
SELECT COUNT(*) FROM tweets WHERE created > now() - interval '3' hour

--Total number of tweets by user chadneal
SELECT COUNT(*) FROM tweets WHERE screen_name LIKE '%chadneal%'

--Total number of tweets that mention AWSreInvent
SELECT COUNT(*) FROM tweets WHERE text LIKE '%AWSreInvent%'

Step 5 - Create a lambda to query Athena

In this step we will create a Lambda function that runs every 5 minutes. The lambda code is provided but please take the time to review the function.

Before we create the Lambda function, we need to retrieve the bucket where Athena will be delivering the results in our local account. We can retrieve this by going to the Athena service in the AWS console, then clicking Settings in the top right Athena menu. From the dialog, let's copy the value in the Query result location (beginning with 's3://') to a local text editor to save for later.

Full Solution - Create the lambda to query Athena

Go to the AWS Lambda console page
Click Create Function
We will skip using a blueprint to get started and author one from scratch. Click Author one from scratch
Leave the trigger blank for now. Click Next without adding a trigger from the Configure triggers page.
Give your Lambda function a unique name. For example you can use vpa_lambda_athena for the query name. For runtime select Python 3.6
Add a role. Under role, Choose an existing role, and in the box below, choose the role starting with title vparoles-VPALambdaAthenaPoller
Click Create function
Select inline code and then use the:

import boto3
import csv
import time
import os
import logging
from urllib.parse import urlparse

# Setup logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)


# These ENV are expected to be defined on the lambda itself:
# vpa_athena_database, vpa_ddb_table, vpa_metric_name, vpa_athena_query, region, vpa_s3_output_location

# Responds to lambda event trigger
def lambda_handler(event, context):
    vpa_athena_query = os.environ['vpa_athena_query']
    athena_result = run_athena_query(vpa_athena_query, os.environ['vpa_athena_database'],
                                     os.environ['vpa_s3_output_location'])
    upsert_into_DDB(os.environ['vpa_metric_name'], athena_result, context)
    logger.info("{0} reinvent tweets so far!".format(athena_result))
    return {'message': "{0} reinvent tweets so far!".format(athena_result)}


# Runs athena query, open results file at specific s3 location and returns result
def run_athena_query(query, database, s3_output_location):
    athena_client = boto3.client('athena', region_name=os.environ['region'])
    s3_client = boto3.client('s3', region_name=os.environ['region'])
    queryrunning = 0

    # Kickoff the Athena query
    response = athena_client.start_query_execution(
        QueryString=query,
        QueryExecutionContext={
            'Database': database
        },
        ResultConfiguration={
            'OutputLocation': s3_output_location
        }
    )

    # Log the query execution id
    logger.info('Execution ID: ' + response['QueryExecutionId'])

    # wait for query to finish.
    while (queryrunning == 0):
        time.sleep(2)
        status = athena_client.get_query_execution(QueryExecutionId=response['QueryExecutionId'])
        results_file = status["QueryExecution"]["ResultConfiguration"]["OutputLocation"]
        if (status["QueryExecution"]["Status"]["State"] != "RUNNING"):
            queryrunning = 1

    # parse the s3 URL and find the bucket name and key name
    s3url = urlparse(results_file)
    s3_bucket = s3url.netloc
    s3_key = s3url.path

    # download the result from s3
    s3_client.download_file(s3_bucket, s3_key[1:], "/tmp/results.csv")

    # Parse file and update the data to DynamoDB
    # This example will only have one record per petric so always grabbing 0
    metric_value = 0
    with open("/tmp/results.csv", newline='') as f:
        reader = csv.DictReader(f)
        for row in reader:
            metric_value = row['_col0']

    os.remove("/tmp/results.csv")
    return metric_value


# Save result to DDB for fast access from Alexa/Lambda
def upsert_into_DDB(nm, value, context):
    region = os.environ['region']
    dynamodb = boto3.resource('dynamodb', region_name=region)
    table = dynamodb.Table(os.environ['vpa_ddb_table'])
    try:
        response = table.put_item(
            Item={
                'metric': nm,
                'value': value
            }
        )
        return 0
    except Exception:
        logger.error("ERROR: Failed to write metric to DDB")
        return 1

Add the role to the Lambda function: lambda_athena_poller
Set the following Environment variables:

vpa_athena_database = tweets
vpa_ddb_table = VPA_Metrics_Table
vpa_metric_name = Reinvent Twitter Sentiment
vpa_athena_query = SELECT count(*) FROM default."tweets"
region = eu-west-1
vpa_s3_output_location = s3://<your_s3_bucket_name>/poller/

Note: for vpa_s3_output_location, use the Athena s3 location copied in the top of this Step's instructions.

From the Lambda function handler and role ensure the Handler is set to vpa_lambda_athena.lambda_handler and the Existing role to lambda_athena_poller
Select Advanced Settings in order to configure the Timeout value to 1 minute
Click Next
From the review page, select Create Function

Full Solution - Create a CloudWatch Event Rule to trigger Lambda

Go to the CloudWatch Events Rules console page.
Click create rule
From the create rule page in the Event Source section. Select Schedule followed by fixed rate with a value of 5 minutes.
From the Target section select Add target, then lambda function, followed by the new query we just created, Athena_poller.
Next click on the Configure Details
Give your rule a name, in this case every-5-min
Unselect the Enabled button to disable the trigger and then select Create rule

Optional CloudFormation

If you couldn't complete the steps above, optionally, you can deploy the following CloudFormation into your account:

Region	Launch Template
Ireland (eu-west-1)

You select the new policy you created for this roles permissions. You can use the filter to search for poller. Now select Next: Review to review our role.
Set the Role name to poller_full_access and click create role
Open the Lambda function and retrieve the S3 Athena output location to put in the environment variable

Alexa Skill Building Steps

Setting up Your Voice User Interface

There are two parts to an Alexa skill. The first part is the Voice User Interface (VUI). This is where we define how we will handle a user's voice input, and which code should be executed when specific commands are uttered. The second part is the actual programming logic for our skill. Both will be configured in this step-by-step guide.

Alexa fits into your Voice Powered Analytics architecture as the interaction interface for retrieving metrics. Alexa determines what metrics to retrieve through intents (which we'll describe and configure in the next steps). The intents correspond to metrics in your DynamoDB data store, which Lambda functions retrieve and send back to the Alexa-enabled device to communicate back to the user:

Full solution - Setting up VUI (expand for details)

1. Go to the Amazon Developer Portal. In the top-right corner of the screen, click the "Sign In" button.
(If you don't already have an account, you will be able to create a new one for free.)

2. Once you have signed in, click the Alexa button at the top of the screen.

3. On the Alexa page, choose the "Get Started" button for the Alexa Skills Kit.

4. Select "Add A New Skill." This will get you to the first page of your new Alexa skill. 5. Fill out the Skill Information screen. Make sure to review the tips we provide below the screenshot.

Skill Information Tips (expand for details)

Skill Information Tips

i. Skill Type For this skill, we are creating a skill using the Custom Interaction Model. This is the default choice. ii. Language Choose the first language you want to support. You can add additional languages in the future, but we need to start with one. (This guide is using U.S. English to start.) iii. Name This is the name that will be shown in the Alexa Skills Store, and the name your users will refer to. iv. Invocation Name This is the name that your users will need to say to start your skill. We have provided some common issues developers encounter in the list below, but you should also review the entire Invocation Name Requirements.

Invocation Name Requirements	Examples of incorrect invocation names
The skill invocation name must not infringe upon the intellectual property rights of an entity or person.	korean air; septa check
Invocation names should be more than one word (unless it is a brand or intellectual property), and must not be a name or place	horoscope; trivia; guide; new york
Two word invocation names are not allowed when one of the words is a definite article, indefinite article, or a preposition	any poet; the bookie; the fool
The invocation name must not contain any of the Alexa skill launch phrases and connecting words. Launch phrase examples include "launch," "ask," "tell," "load," and "begin." Connecting word examples include "to," "from," "by," "if," "and," "whether."	trivia game for star wars; better with bacon
The invocation name must not contain the wake words "Alexa," "Amazon," "Echo," or the words "skill" or "app."	hackster initial skill; word skills
The invocation name must be written in each language you choose to support. For example, the German version of your skill must have an invocation name written in German, while the English (US) version must have an invocation name written in English.	kitchen stories (German skill)

6. Click the Next button to move to the Interaction Model. 7. Click on the Launch Skill Builder (Beta) button . This will launch the new Skill Builder Dashboard.

8. Click on the "Code Editor" item under Dashboard on the top left side of the skill builder. 9. In the textfield provided, replace any existing code with the code provided in the Interaction Model, then click "Apply Changes" or "Save Model". 10. Click on the "Dashboard" button. 11. Add some more sample utterances for your newly generated intents. These are the things a user would say to make a specific intent happen. Here are a few examples: - Show me my metrics / List my metrics - What is my {metric}

12. Click "Build Model" and "Save"

13. If your interaction model builds successfully, click on Configuration button to move on to Configuration. In our next step of this guide, we will be creating our Lambda function in the AWS developer console, but keep this browser tab open, because we will be returning here on Page #3: Connect VUI to Code.

If you get an error from your interaction model, check through this list: - Did you copy & paste the provided code into the appropriate boxes? - Did you accidentally add any characters to the Interaction Model or Sample Utterances?

Configure Alexa Backend

Now that we've configured the voice interaction, let's set up our Lambda function to leverage your DynamoDB metrics and be triggered by the Alexa Skills Kit.
Please deploy the following template into your AWS environment which contains the Lambda code for the Alexa skill.

Region	Launch Template
Ireland (eu-west-1)

Full solution - Setting up Alexa Backend (expand for details)

1. Check your AWS region. For the reinvent workshop, we'll be using the EU (Ireland) region.

2. Open the Lambda function, starting with “VoiceAlexaSkillFull-AlexaMetricSkill-1” deployed with the Cloudformation. Configure your trigger. Click the Triggers tab. Within the Triggers pane, click the link to Add a Trigger. A pop-up should appear, click in the dashed box and select Alexa Skills Kit from the list. If you don't see Alexa Skills Kit in the list, jump back to step #3 on this page.

3. Once you have selected Alexa Skills Kit, click the Configuration Tab to go back to your code.
4. The ARN value should be in the top right corner. Copy this value for use in the next section of the guide.

5. Within the Lambda configuration, navigate to Environment Variables. Add a greeting and exit message for your Alexa skill by adding two environment variables(case sensitive): greeting_msg and exit_msg

Example

greeting_msg Welcome to the Voice Powered Analytics. Please tell me what metrics you'd like to hear. To hear available metrics, ask Alexa tell me my metrics
and exit_msg Thank you for trying the Voice Powered Analytics. Have a nice day!

6. We'll also add an environment variable called: metrics_table called VPA_Metrics_Table. This references the DynamoDB table that the Alexa skill will be querying for your metric

Hint

7. Bonus (If time): can you add a skill to the Lambda function which enables users to "List My Metrics"

Optionally, you can deploy the following CloudFormation:

Region	Launch Template
Ireland (eu-west-1)

Connecting Your Voice User Interface to Your Lambda Function

On Step 1 "Setting up Your Voice User Interface" of this Step, we created a voice user interface for the intents and utterances we expect from our users. On "Step 2 Configure Alexa Backend", we created a Lambda function that contains all of our logic for the skill. On this page, we need to connect those two pieces together.

Full solution - Connecting VUI to Lambda (expand for details)

Go back to the Amazon Developer Portal and select your skill from the list. You may still have a browser tab open if you started at the beginning of this tutorial.
Open the "Configuration" tab on the left side.
Select the "AWS Lambda ARN" option for your endpoint. You have the ability to host your code anywhere that you would like, but for the purposes of simplicity and frugality, we are using AWS Lambda. (Read more about Hosting Your Own Custom Skill Web Service.) With the AWS Free Tier, you get 1,000,000 free requests per month, up to 3.2 million seconds of compute time per month. Learn more at https://aws.amazon.com/free/. In addition, Amazon now offers AWS Promotional Credits for developers who have live Alexa skills that incur costs on AWS related to those skills IMPORTANT: Make sure you select the same region that you created your Lambda in.
Paste your Lambda's ARN (Amazon Resource Name) into the textbox provided. It should look similar to the screenshot above.
Leave "Account Linking" set to "No". For this skill, we won't be using Account Linking, but you can learn more about Linking an Alexa User with a User in Your System.
Click the "Next" button to continue to page #4 of this guide.

Testing Your Alexa Skill

You've now created a Voice User Interface and a Lambda function, and connected the two together. Your skill is now ready to test.

Full Solution - Testing Your Alexa Skill

1. Go back to the Amazon Developer Portal and select your skill from the list. You may still have a browser tab open if you started at the beginning of this tutorial. 2. Open the "Test" tab on the left side.

3. Test your skill with the Service Simulator. To validate that your skill is working as expected, use the Service Simulator. In the Enter Utterance text box, type "What’s my reinvent tweets over the last hour."

4. Other testing methods to consider: - Echosim.io - a browser-based Alexa skill testing tool that makes it easy to test your skills without carrying a physical device everywhere you go. - Unit Testing with Alexa - a modern approach to unit testing your Alexa skills with Postman and Amazon API Gateway. 5. If your sample skill is working properly, you can now customize your skill.

Service Simulator Tips

- After you click the "Ask [Your Skill Name]" button, you should see the Lambda Request and Lambda Response boxes get populated with JSON data like in the screenshot above. - Click the Listen button in the bottom right corner to hear Alexa read the response. - You can have an entire conversation with your skill with the Service Simulator. Try the following commands: - "tell me about this place" - [Press the listen button, and type "recommend an attraction" in the box] - [Press the listen button, and type "give me an activity" in the box] (Continue this process for all of the utterances. To start over, click the "Reset" button.) - If you receive a response that reads: "The remote endpoint could not be called, or the response it returned was invalid," this is an indication that something is broken. AWS Lambda offers an additional testing tool to help you troubleshoot your skill.

Bonus: What Utterances and Intents Are Needed For a "List My Metrics" skill

Hints

Intent: ListMetrics
Utterance(s):
- ListMetrics List My Metrics
- ListMetrics What are my metrics

gridl / voice-powered-analytics Goto Github PK

voice-powered-analytics's Introduction