Posts in category: Amazon AWS

Recommender systems help you tailor customer experiences on online platforms. Amazon Personalize is an artificial intelligence and machine learning service that specializes in developing recommender system solutions. It automatically examines the data, performs feature and algorithm selection, optimizes the model based on your data, and deploys and hosts the model for real-time recommendation inference. However, if you don’t have explicit user rating data or need to access trained models’ weights, you may need to build your recommender system from scratch. In this post, I show you how to train and deploy a customized recommender system in TensorFlow 2.0, using a Neural Collaborative Filtering (NCF) (He et al., 2017) model on Amazon SageMaker.

Understanding Neural Collaborative Filtering

A recommender system is a set of tools that helps provide users with a personalized experience by predicting user preference amongst a large number of options. Matrix factorization (MF) is a well-known approach to solving such a problem. Conventional MF solutions exploit explicit feedback in a linear fashion; explicit feedback consists of direct user preferences, such as ratings for movies on a five-star scale or binary preference on a product (like or not like). However, explicit feedback isn’t always present in datasets. NCF solves the absence of explicit feedback by only using implicit feedback, which is derived from user activity, such as clicks and views. In addition, NCF utilizes multi-layer perceptron to introduce non-linearity into the solution.

Architecture overview

An NCF model contains two intrinsic sets of network layers: embedding and NCF layers. You use these layers to build a neural matrix factorization solution with two separate network architectures, generalized matrix factorization (GMF) and multi-layer perceptron (MLP), whose outputs are then concatenated as input for the final output layer. The following diagram from the original paper illustrates this architecture.

In this post, I walk you through building and deploying the NCF solution using the following steps:

1. Prepare data for model training and testing.
2. Code the NCF network in TensorFlow 2.0.
3. Perform model training using Script Mode and deploy the trained model using Amazon SageMaker hosting services as an endpoint.
4. Make a recommendation inference via the model endpoint.

You can find the complete code sample in the GitHub repo.

Preparing the data

For this post, I use the MovieLens dataset. MovieLens is a movie rating dataset provided by GroupLens, a research lab at the University of Minnesota.

First, I run the following code to download the dataset into the ml-latest-small directory:

%%bash  
# delete the data directory if it already exists  
rm -r ml-latest-small  
  
# download movielens small dataset  
curl -O http://files.grouplens.org/datasets/movielens/ml-latest-small.zip  
  
# unzip into data directory
unzip ml-latest-small.zip  
rm ml-latest-small.zip  

I only use rating.csv, which contains explicit feedback data, as a proxy dataset to demonstrate the NCF solution. To fit this solution to your data, you need to determine the meaning of a user liking an item.

To perform a training and testing split, I take the latest 10 items each user rated as the testing set and keep the rest as the training set:

def train_test_split(df, holdout_num):  
    """ perform training testing split 
     
    @param df: dataframe 
    @param holdhout_num: number of items to be held out per user as testing items 
     
    @return df_train: training data
    @return df_test testing data
     
    """  
    # first sort the data by time  
    df = df.sort_values(['userId', 'timestamp'], ascending=[True, False])  
      
    # perform deep copy to avoid modification on the original dataframe  
    df_train = df.copy(deep=True)  
    df_test = df.copy(deep=True)  
      
    # get test set  
    df_test = df_test.groupby(['userId']).head(holdout_num).reset_index()  
      
    # get train set  
    df_train = df_train.merge(  
        df_test[['userId', 'movieId']].assign(remove=1),  
        how='left'  
    ).query('remove != 1').drop('remove', 1).reset_index(drop=True)  
      
    # sanity check to make sure we're not duplicating/losing data  
    assert len(df) == len(df_train) + len(df_test)  
      
    return df_train, df_test  

Because we’re performing a binary classification task and treating the positive label as if the user liked an item, we need to randomly sample the movies each user hasn’t rated and treat them as negative labels. This is called negative sampling. The following function implements the process:

def negative_sampling(user_ids, movie_ids, items, n_neg):  
    """This function creates n_neg negative labels for every positive label 
     
    @param user_ids: list of user ids 
    @param movie_ids: list of movie ids 
    @param items: unique list of movie ids 
    @param n_neg: number of negative labels to sample 
     
    @return df_neg: negative sample dataframe 
     
    """  
      
    neg = []  
    ui_pairs = zip(user_ids, movie_ids)  
    records = set(ui_pairs)  
      
    # for every positive label case  
    for (u, i) in records:  
        # generate n_neg negative labels  
        for _ in range(n_neg):  
            j = np.random.choice(items)  
            # resample if the movie already exists for that user  
            While (u, j) in records:  
                j = np.random.choice(items)  
            neg.append([u, j, 0])  
              
    # convert to pandas dataframe for concatenation later  
    df_neg = pd.DataFrame(neg, columns=['userId', 'movieId', 'rating'])  
      
    return df_neg  

You can use the following code to perform the training and testing splits, negative sampling, and store the processed data in Amazon Simple Storage Service (Amazon S3):

import os  
import boto3  
import sagemaker  
import numpy as np  
import pandas as pd  
  
# read rating data    
fpath = './ml-latest-small/ratings.csv'    
df = pd.read_csv(fpath)    
    
# perform train test split    
df_train, df_test = train_test_split(df, 10)    
   
# create 5 negative samples per positive label for training set    
neg_train = negative_sampling(    
    user_ids=df_train.userId.values,     
    movie_ids=df_train.movieId.values,    
    items=df.movieId.unique(),    
    n_neg=5    
)    
    
# create final training and testing sets    
df_train = df_train[['userId', 'movieId']].assign(rating=1)    
df_train = pd.concat([df_train, neg_train], ignore_index=True)    
    
df_test = df_test[['userId', 'movieId']].assign(rating=1)    
  
# save data locally first    
dest = 'ml-latest-small/s3'    
!mkdir {dest}  
train_path = os.path.join(dest, 'train.npy')    
test_path = os.path.join(dest, 'test.npy')    
np.save(train_path, df_train.values)    
np.save(test_path, df_test.values)    
    
# store data in the default S3 bucket  
sagemaker_session = sagemaker.Session()  
bucket_name = sm_session.default_bucket()  
print("the default bucket name is", bucket_name)  
  
# upload to the default s3 bucket
sagemaker_session.upload_data(train_path, key_prefix='data')    
sagemaker_session.upload_data(test_path, key_prefix='data')    

I use the Amazon SageMaker session’s default bucket to store processed data. The format of the default bucket name is sagemaker-{region}–{aws-account-id}.

Coding the NCF network

In this section, I implement GMF and MLP separately. These two components’ input are both user and item embeddings. To define the embedding layers, we enter the following code:

def _get_user_embedding_layers(inputs, emb_dim):  
    """ create user embeddings """  
    user_gmf_emb = tf.keras.layers.Dense(emb_dim, activation='relu')(inputs)  
    user_mlp_emb = tf.keras.layers.Dense(emb_dim, activation='relu')(inputs)  
    return user_gmf_emb, user_mlp_emb  
  
def _get_item_embedding_layers(inputs, emb_dim):  
    """ create item embeddings """  
    item_gmf_emb = tf.keras.layers.Dense(emb_dim, activation='relu')(inputs)  
    item_mlp_emb = tf.keras.layers.Dense(emb_dim, activation='relu')(inputs)  
    return item_gmf_emb, item_mlp_emb  

To implement GMF, we multiply the user and item embeddings:

def _gmf(user_emb, item_emb):  
    """ general matrix factorization branch """  
    gmf_mat = tf.keras.layers.Multiply()([user_emb, item_emb])  
    return gmf_mat  

The authors of Neural Collaborative Filtering show that a four-layer MLP with 64-dimensional user and item latent factor performed the best across different experiments, so we implement this structure as our MLP:

def _mlp(user_emb, item_emb, dropout_rate):  
    """ multi-layer perceptron branch """  
    def add_layer(dim, input_layer, dropout_rate):  
        hidden_layer = tf.keras.layers.Dense(dim, activation='relu')(input_layer)  
        if dropout_rate:  
            dropout_layer = tf.keras.layers.Dropout(dropout_rate)(hidden_layer)  
            return dropout_layer  
        return hidden_layer  
  
    concat_layer = tf.keras.layers.Concatenate()([user_emb, item_emb])  
    dropout_l1 = tf.keras.layers.Dropout(dropout_rate)(concat_layer)  
    dense_layer_1 = add_layer(64, dropout_l1, dropout_rate)  
    dense_layer_2 = add_layer(32, dense_layer_1, dropout_rate)  
    dense_layer_3 = add_layer(16, dense_layer_2, None)  
    dense_layer_4 = add_layer(8, dense_layer_3, None)  
    return dense_layer_4  

Lastly, to produce the final prediction, we concatenate the output of GMF and MLP as the following:

def _neuCF(gmf, mlp, dropout_rate):  
    """ final output layer """  
    concat_layer = tf.keras.layers.Concatenate()([gmf, mlp])  
    output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(concat_layer)  
    return output_layer  

To build the entire solution in one step, we create a graph building function:

def build_graph(user_dim, item_dim, dropout_rate=0.25):  
    """ neural collaborative filtering model  
     
    @param user_dim: one hot encoded user dimension 
    @param item_dim: one hot encoded item dimension 
    @param dropout_rate: drop out rate for dropout layers 
     
    @return model: neural collaborative filtering model graph 
     
    """  
  
    user_input = tf.keras.Input(shape=(user_dim))  
    item_input = tf.keras.Input(shape=(item_dim))  
  
    # create embedding layers  
    user_gmf_emb, user_mlp_emb = _get_user_embedding_layers(user_input, 32)  
    item_gmf_emb, item_mlp_emb = _get_item_embedding_layers(item_input, 32)  
  
    # general matrix factorization  
    gmf = _gmf(user_gmf_emb, item_gmf_emb)  
  
    # multi layer perceptron  
    mlp = _mlp(user_mlp_emb, item_mlp_emb, dropout_rate)  
  
    # output  
    output = _neuCF(gmf, mlp, dropout_rate)  
  
    # create the model
    model = tf.keras.Model(inputs=[user_input, item_input], outputs=output)  
  
    return model  

I use the Keras plot_model utility to verify the network architecture I just built is correct:

# build graph  
ncf_model = build_graph(n_user, n_item)  
  
# visualize and save to a local png file  
tf.keras.utils.plot_model(ncf_model, to_file="neural_collaborative_filtering_model.png")  

The output architecture should look like the following diagram.

Training and deploying the model

For instructions on deploying a model you trained using an instance on Amazon SageMaker, see Deploy trained Keras or TensorFlow models using Amazon SageMaker. For this post, I deploy this model using Script Mode.

We first need to create a Python script that contains the model training code. I compiled the model architecture code presented previously and added additional code required in the ncf.py, which you can use directly. I also implemented a function for you to load training data; to load testing data, the function is the same except the file name is changed to reflect the testing data destination. See the following code:

def _load_training_data(base_dir):  
    """ load training data """  
    df_train = np.load(os.path.join(base_dir, 'train.npy'))  
    user_train, item_train, y_train = np.split(np.transpose(df_train).flatten(), 3)  
    return user_train, item_train, y_train  

After downloading the training script and storing it in the same directory as the model training notebook, we can initialize a TensorFlow estimator with the following code:

ncf_estimator = TensorFlow(  
    entry_point='ncf.py',  
    role=role,  
    train_instance_count=1,  
    train_instance_type='ml.c5.2xlarge',  
    framework_version='2.1.0',  
    py_version='py3',  
    distributions={'parameter_server': {'enabled': True}},  
    hyperparameters={'epochs': 3, 'batch_size': 256, 'n_user': n_user, 'n_item': n_item}  
)  

We fit the estimator to our training data to start the training job:

# specify the location of the training data  
training_data_uri = os.path.join(f's3://{bucket_name}', 'data')  
  
# kick off the training job  
ncf_estimator.fit(training_data_uri)  

When you see the output in the following screenshot, your model training job has started.

When the model training process is complete, we can deploy the model as an endpoint. See the following code:

predictor = ncf_estimator.deploy(initial_instance_count=1,   
                                 instance_type='ml.c5.xlarge',   
                                 endpoint_type='tensorflow-serving')  

Performing model inference

To make inference using the endpoint on the testing set, we can invoke the model in the same way by using TensorFlow Serving:

# Define a function to read testing data  
def _load_testing_data(base_dir):  
    """ load testing data """  
    df_test = np.load(os.path.join(base_dir, 'test.npy'))  
    user_test, item_test, y_test = np.split(np.transpose(df_test).flatten(), 3)  
    return user_test, item_test, y_test  
  
# read testing data from local  
user_test, item_test, test_labels = _load_testing_data('./ml-latest-small/s3/')  
  
# one-hot encode the testing data for model input  
with tf.Session() as tf_sess:  
    test_user_data = tf_sess.run(tf.one_hot(user_test, depth=n_user)).tolist()  
    test_item_data = tf_sess.run(tf.one_hot(item_test, depth=n_item)).tolist()  
      
# make batch prediction  
batch_size = 100  
y_pred = []  
for idx in range(0, len(test_user_data), batch_size):  
    # reformat test samples into tensorflow serving acceptable format  
    input_vals = {  
     "instances": [  
         {'input_1': u, 'input_2': i}   
         for (u, i) in zip(test_user_data[idx:idx+batch_size], test_item_data[idx:idx+batch_size])  
    ]}  
   
    # invoke model endpoint to make inference  
    pred = predictor.predict(input_vals)  
      
    # store predictions  
    y_pred.extend([i[0] for i in pred['predictions']])  

The model output is a set of probabilities, ranging from 0 to 1, for each user-item pair that we specify for inference. To make final binary predictions, such as like or not like, we need to apply a threshold. For demonstration purposes, I use 0.5 as a threshold; if the predicted probability is equal or greater than 0.5, we say the model predicts the user will like the item, and vice versa.

# combine user id, movie id, and prediction in one dataframe  
pred_df = pd.DataFrame([  
    user_test,  
    item_test,  
    (np.array(y_pred) >= 0.5).astype(int)],  
).T  
  
# assign column names to the dataframe  
pred_df.columns = ['userId', 'movieId', 'prediction']  

Finally, we can get a list of model predictions on whether a user will like a movie, as shown in the following screenshot.

Conclusion

Designing a recommender system can be a challenging task that sometimes requires model customization. In this post, I showed you how to implement, deploy, and invoke an NCF model from scratch in Amazon SageMaker. This work can serve as a foundation for you to start building more customized solutions with your own datasets.

For more information about using built-in Amazon SageMaker algorithms and Amazon Personalize to build recommender system solutions, see the following:

• Omnichannel personalization with Amazon Personalize
• Creating a recommendation engine using Amazon Personalize
• Extending Amazon SageMaker factorization machines algorithms to predict top x recommendations
• Build a movie recommender with factorization machines on Amazon SageMaker

To further customize the Neural Collaborative Filtering network, Deep Matrix Factorization (Xue et al., 2017) model can be an option. For more information, see Build a Recommendation Engine on AWS Today.

About the Author

Taihua (Ray) Li is a data scientist with AWS Professional Services. He holds a M.S. in Predictive Analytics degree from DePaul University and has several years of experience building artificial intelligence powered applications for non-profit and enterprise organizations. At AWS, Ray helps customers to unlock business potentials and to drive actionable outcomes with machine learning. Outside of work, he enjoys fitness classes, biking, and traveling.

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Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first.

Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results. In this post, we walk through how you can manually tune your index to achieve the best results.

It’s important to understand the three main response types of Amazon Kendra: matching to FAQs, reading comprehension to extract suggested answers, and document ranking. Relevance tuning impacts document ranking. Additionally, relevance tuning is just one of many factors that impact search results for your users. You can’t change specific results, but you can influence how much weight Amazon Kendra applies to certain fields or attributes.

Faceting

Because you’re tuning based on fields, you need to have those fields faceted in your index. For example, if you want to boost the signal of the author field, you need to make the author field a searchable facet in your index. For more information about adding facetable fields to your index, see Creating custom document attributes.

Performing relevance tuning

You can perform relevance tuning in several different ways, such as on the AWS Management Console through the Amazon Kendra search console or with the Amazon Kendra API. You can also use several different types of fields when tuning:

• Date fields – Boost more recent results
• Number fields – Amplify content based on number fields, such as total view counts
• String fields – Elevate results based on string fields, for example those that are tagged as coming from a more authoritative data source

Prerequisites

This post requires you to complete the following prerequisites: set up your environment, upload the example dataset, and create an index.

Setting up your environment

Ensure you have the AWS CLI installed. Open a terminal window and create a new working directory. From that directory, download the following files:

• The sample dataset, available from: s3://aws-ml-blog/artifacts/kendra-relevance-tuning/ml-blogs.tar.gz
• The Python script to create your index, available from: s3://aws-ml-blog/artifacts/kendra-relevance-tuning/create-index.py

The following screenshot shows how to download the dataset and the Python script.

Uploading the dataset

For this use case, we use a dataset that is a selection of posts from the AWS Machine Learning Blog. If you want to use your own dataset, make sure you have a variety of metadata. You should ideally have varying string fields and date fields. In the example dataset, the different fields include:

• Author name – Author of the post
• Content type – Blog posts and whitepapers
• Topic and subtopic – The main topic is Machine Learning and subtopics include Computer Vision and ML at the Edge
• Content language – English, Japanese, and French
• Number of citations in scientific journals – These are randomly fabricated numbers for this post

To get started, create two Amazon Simple Storage Service (Amazon S3) buckets. Make sure to create them in the same Region as your index. Our index has two data sources.

Within the ml-blogs.tar.gz tarball there are two directories. Extract the tarball and sync the contents of the first directory, ‘bucket1’ to your first S3 bucket. Then sync the contents of the second directory, ‘bucket2’, to your second S3 bucket.

The following screenshot shows how to download the dataset and upload it to your S3 buckets.

Creating the index

Using your preferred code editor, open the Python script ‘create-index.py’ that you downloaded previously. You will need to set your bucket name variables to the names of the Amazon S3 buckets you created earlier. Make sure you uncomment those lines.

Once this is done, run the script by typing python create-index.py. This does the following:

• Creates an AWS Identity and Access Management (IAM) role to allow your Amazon Kendra index to read data from Amazon S3 and write logs to Amazon CloudWatch Logs
• Creates an Amazon Kendra index
• Adds two Amazon S3 data sources to your index
• Adds new facets to your index, which allows you to search based on the different fields in the dataset
• Initiates a data source sync job

Working with relevance tuning

Now that our data is properly indexed and our metadata is facetable, we can test different settings to understand how relevance tuning affects search results. In the following examples, we will boost based on several different attributes. These include the data source, document type, freshness, and popularity.

Boosting your authoritative data sources

The first kind of tuning we look at is based on data sources. Perhaps you have one data source that is well maintained and curated, and another with information that is less accurate and dated. You want to prioritize the results from the first data source so your users get the most relevant results when they perform searches.

When we created our index, we created two data sources. One contains all our blog posts—this is our primary data source. The other contains only a single file, which we’re treating as our legacy data source.

Our index creation script set the field _data_source_id to be facetable, searchable, and displayable. This is an essential step in boosting particular data sources.

The following screenshot shows the index fields of our Amazon Kendra index.

1. On the Amazon Kendra search console, search for Textract.

Your results should reference posts about Amazon Textract, a service that can automatically extract text and data from scanned documents.

The following screenshot shows the results of a search for ‘Textract’.

Also in the results should be a file called Test_File.txt. This is a file from our secondary, less well-curated data source. Make a note of where this result appears in your search results. We want to de-prioritize this result and boost the results from our primary source.

2. Choose Tuning to open the Relevance tuning
3. Under Text fields, expand data source.
4. Drag the slider for your first data source to the right to boost the results from this source. For this post, we start by setting it to 8.
5. Perform another search for Textract.

You should find that the file from the second data source has moved down the search rankings.

6. Drag the slider all the way to the right, so that the boost is set to 10, and perform the search again.

You should find that the result from the secondary data source has disappeared from the first page of search results.

The following screenshot shows the relevance tuning panel with data source field boost applied to one data source, and the search results excluding the results from our secondary data source.

Although we used this approach with S3 buckets as our data sources, you can use it to prioritize any data source available in Amazon Kendra. You can boost the results from your Amazon S3 data lake and de-prioritize the results from your Microsoft SharePoint system, or vice-versa.

Boosting certain document types

In this use case, we boost the results of our whitepapers over the results from the AWS Machine Learning Blog. We first establish a baseline search result.

1. Open the Amazon Kendra search console and search for What is machine learning?

Although the top result is a suggested answer from a whitepaper, the next results are likely from blog posts.

The following screenshot shows the results of a search for ‘What is machine learning?’

How do we influence Amazon Kendra to push whitepapers towards the top of its search results?

First, we want to tune the search results based on the content Type field.

1. Open the Relevance tuning panel on the Amazon Kendra console.
2. Under Custom fields, expand Type.
3. Drag the Type field boost slider all the way to the right to set the relevancy of this field to 10.

We also want to boost the importance of a particular Type value, namely Whitepapers.

4. Expand Advanced boosting and choose Add value.
5. Whitepapers are indicated in our metadata by the field “Type”: “Whitepaper”, so enter a value of Whitepaper and set the value to 10.
6. Choose Save.

The following screenshot shows the relevance tuning panel with type field boost applied to the ‘Whitepaper’ document type.

Wait for up to 10 seconds before you rerun your search. The top results should all be whitepapers, and blog post results should appear further down the list.

The following screenshot shows the results of a search for ‘What is machine learning?’ with type field boost applied.

7. Return your Type field boost settings back to their normal values.

Boosting based on document freshness

You might have a large archive of documents spanning multiple decades, but the more recent answers are more useful. For example, if your users ask, “Where is the IT helpdesk?” you want to make sure they’re given the most up-to-date answer. To achieve this, you can give a freshness boost based on date attributes.

In this use case, we boost the search results to include more recent posts.

1. On the Amazon Kendra search console, search for medical.

The first result is De-identify medical images with the help of Amazon Comprehend Medical and Amazon Rekognition, published March 19, 2019.

The following screenshot shows the results of a search for ‘medical’.

2. Open the Relevance tuning panel again.
3. On the Date tab, open Custom fields.
4. Adjust the Freshness boost of PublishDate to 10.
5. Search again for medical.

This time the first result is Enhancing speech-to-text accuracy of COVID-19-related terms with Amazon Transcribe Medical, published May 15, 2020.

The following screenshot shows the results of a search for ‘medical’ with freshness boost applied.

You can also expand Advanced boosting to boost results from a particular period of time. For example, if you release quarterly business results, you might want to set the sensitivity range to the last 3 months. This boosts documents released in the last quarter so users are more likely to find them.

The following screenshot shows the section of the relevance tuning panel related to freshness boost, showing the Sensitivity slider to capture range of sensitivity.

Boosting based on document popularity

The final scenario is tuning based on numerical values. In this use case, we assigned a random number to each post to represent the number of citations they received in scientific journals. (It’s important to reiterate that these are just random numbers, not actual citation numbers!) We want the most frequently cited posts to surface.

1. Run a search for keras, which is the name of a popular library for ML.

You might see a suggested answer from Amazon Kendra, but the top results (and their synthetic citation numbers) are likely to include:

• Amazon SageMaker Keras – 81 citations
• Deploy trained Keras or TensorFlow models using Amazon SageMaker – 57 citations
• Train Deep Learning Models on GPUs using Amazon EC2 Spot Instances – 68 citations

2. On the Relevance tuning panel, on the Numeric tab, pull the slider for Citations all the way to 10.
3. Select Ascending to boost the results that have more citations.

The following screenshot shows the relevance tuning panel with numeric boost applied to the Citations custom field.

4. Search for keras again and see which results appear.

At the top of the search results are:

• Train and deploy Keras models with TensorFlow and Apache MXNet on Amazon SageMaker – 1,197 citations
• Using TensorFlow eager execution with Amazon SageMaker script mode – 2,434 citations

Amazon Kendra prioritized the results with more citations.

Conclusion

This post demonstrated how to use relevance tuning to adjust your users’ Amazon Kendra search results. We used a small and somewhat synthetic dataset to give you an idea of how relevance tuning works. Real datasets have a lot more complexity, so it’s important to work with your users to understand which types of search results they want prioritized. With relevance tuning, you can get the most value out of enterprise search with Amazon Kendra! For more information about Amazon Kendra, see AWS re:Invent 2019 – Keynote with Andy Jassy on YouTube, Amazon Kendra FAQs, and What is Amazon Kendra?

Thanks to Tapodipta Ghosh for providing the sample dataset and technical review. This post couldn’t have been written without his assistance.

About the Author

James Kingsmill is a Solution Architect in the Australian Public Sector team. He has a longstanding interest in helping public sector customers achieve their transformation, automation, and security goals. In his spare time, you can find him canyoning in the Blue Mountains near Sydney.

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Machine learning (ML)-based recommender systems aren’t a new concept, but developing such a system can be a resource-intensive task—from data management during training and inference, to managing scalable real-time ML-based API endpoints. Amazon Personalize allows you to easily add sophisticated personalization capabilities to your applications by using the same ML technology used on Amazon.com for over 20 years. No ML experience required. Customers in industries such as retail, media and entertainment, gaming, travel and hospitality, and others use Amazon Personalize to provide personalized content recommendations to their users. With Amazon Personalize, you can solve the most common use cases: providing users with personalized item recommendations, surfacing similar items, and personalized re-ranking of items.

Amazon Personalize automatically trains ML models from your user-item interactions and provides an API to retrieve personalized recommendations for any user. A frequently asked question is, “How do I compare the performance of recommendations generated by Amazon Personalize to my existing recommendation system?” In this post, we discuss how to perform A/B tests with Amazon Personalize, a common technique for comparing the efficacy of different recommendation strategies.

You can quickly create a real-time recommender system on the AWS Management Console or the Amazon Personalize API by following these simple steps:

1. Import your historical user-item interaction data.
2. Based on your use case, start a training job using an Amazon Personalize ML algorithm (also known as recipes).
3. Deploy an Amazon Personalize-managed, real-time recommendations endpoint (also known as a campaign).
4. Record new user-item interactions in real time by streaming events to an event tracker attached to your Amazon Personalize deployment.

The following diagram represents which tasks Amazon Personalize manages.

Metrics overview

You can measure the performance of ML recommender systems through offline and online metrics. Offline metrics allow you to view the effects of modifying hyperparameters and algorithms used to train your models, calculated against historical data. Online metrics are the empirical results observed in your user’s interactions with real-time recommendations provided in a live environment.

Amazon Personalize generates offline metrics using test datasets derived from the historical data you provide. These metrics showcase how the model recommendations performed against historical data. The following diagram illustrates a simple example of how Amazon Personalize splits your data at training time.

Consider a training dataset containing 10 users with 10 interactions per user; interactions are represented by circles and ordered from oldest to newest based on their timestamp. In this example, Amazon Personalize uses 90% of the users’ interactions data (blue circles) to train your model, and the remaining 10% for evaluation. For each of the users in the evaluation data subset, 90% of their interaction data (green circles) is used as input for the call to the trained model, and the remaining 10% of their data (orange circle) is compared to the output produced by the model to validate its recommendations. The results are displayed to you as evaluation metrics.

Amazon Personalize produces the following metrics:

• Coverage – This metric is appropriate if you’re looking for what percentage of your inventory is recommended
• Mean reciprocal rank (at 25) – This metric is appropriate if you’re interested in the single highest ranked recommendation
• Normalized discounted cumulative gain (at K) – The discounted cumulative gain is a measure of ranking quality; it refers to how well the recommendations are ordered
• Precision (at K) – This metric is appropriate if you’re interested in how a carousel of size K may perform in front of your users

For more information about how Amazon Personalize calculates these metrics, see Evaluating a Solution Version.

Offline metrics are a great representation of how your hyperparameters and data features influence your model’s performance against historical data. To find empirical evidence of the impact of Amazon Personalize recommendations on your business metrics, such as click-through rate, conversion rate, or revenue, you should test these recommendations in a live environment, getting them in front of your customers. This exercise is important because a seemingly small improvement in these business metrics can translate into a significant increase in your customer engagement, satisfaction, and business outputs, such as revenue.

The following sections include an experimentation methodology and reference architecture in which you can identify the steps required to expose multiple recommendation strategies (for example, Amazon Personalize vs. an existing recommender system) to your users in a randomized fashion and measure the difference in performance in a scientifically sound manner (A/B testing).

Experimentation methodology

The data collected across experiments enables you to measure the efficacy of Amazon Personalize recommendations in terms of business metrics. The following diagram illustrates the experimentation methodology we suggest adhering to.

The process consists of five steps:

• Research – The formulation of your questions and definition of the metrics to improve are solely based on the data you gather before starting your experiment. For example, after exploring your historical data, you might be interested in why you experience shopping cart abandonment or high bounce rates from leads generated by advertising.
• Develop a hypothesis – You use the data gathered during the research phase to make observations and develop a change and effect statement. The hypothesis must be quantifiable. For example, providing user personalization through an Amazon Personalize campaign on the shopping cart page will translate into an increase of the average cart value by 10%.
• Create variations based on the hypothesis – The variations of your experiment are based on the hypothesized behavior you’re evaluating. A newly created Amazon Personalize campaign can be considered the variation of your experiment when compared against an existing rule-based recommendation system.
• Run an experiment – You can use several techniques to test your recommendation system; this post focuses on A/B testing. The metrics data gathered during the experiment help validate (or invalidate) the hypothesis. For example, a 10% increase on the average cart value after adding Amazon Personalize recommendations to the shopping cart page over 1 month compared to the average cart value keeping the current system’s recommendations.
• Measure the results – In this step, you determine if there is statistical significance to draw a conclusion and select the best performing variation. Was the increase in your cart average value a result of the randomness of your user testing set, or did the newly created Amazon Personalize campaign influence this increase?

A/B testing your Amazon Personalize deployment

The following architecture showcases a microservices-based implementation of an A/B test between two Amazon Personalize campaigns. One is trained with one of the recommendation recipes provided by Amazon Personalize, and the other is trained with a variation of this recipe. Amazon Personalize provides various predefined ML algorithms (recipes); HRNN-based recipes enable you to provide personalized user recommendations.

This architecture compares two Amazon Personalize campaigns. You can apply the same logic when comparing an Amazon Personalize campaign against a custom rule-based or ML-based recommender system. For more information about campaigns, see Creating a Campaign.

The basic workflow of this architecture is as follows:

1. The web application requests customer recommendations from the recommendations microservice.
2. The microservice determines if there is an active A/B test. For this post, we assume your testing strategy settings are stored in Amazon DynamoDB.
3. When the microservice identifies the group your customer belongs to, it resolves the Amazon Personalize campaign endpoint to query for recommendations.
4. Amazon Personalize campaigns provide the recommendations for your users.
5. The users interact with their respective group recommendations.
6. The web application streams user interaction events to Amazon Kinesis Data Streams.
7. The microservice consumes the Kinesis stream, which sends the user interaction event to both Amazon Personalize event trackers. Recording events is an Amazon Personalize feature that collects real-time user interaction data and provides relevant recommendations in real time.
8. Amazon Kinesis Data Firehose ingests your user-item interactions stream and stores the interactions data in Amazon Simple Storage Service (Amazon S3) to use in future trainings.
9. The microservice keeps track of your pre-defined business metrics throughout the experiment.

For instructions on running an A/B test, see the Retail Demo Store Experimentation Workshop section in the Github repo.

Tracking well-defined business metrics is a critical task during A/B testing; seeing improvements on these metrics is the true indicator of the efficacy of your Amazon Personalize recommendations. The metrics measured throughout your A/B tests need to be consistent across your variations (groups A and B). For example, an ecommerce site can evaluate the change (increase or decrease) on the click-through rate of a particular widget after adding Amazon Personalize recommendations (group A) compared to the click-through rate obtained using the current rule-based recommendations (group B).

An A/B experiment runs for a defined period, typically dictated by the number of users necessary to reach a statistically significant result. Tools such as Optimizely, AB Tasty, and Evan Miller’s Awesome A/B Tools can help you determine how large your sample size needs to be. A/B tests are usually active across multiple days or even weeks, in order to collect a large enough sample from your userbase. The following graph showcases the feedback loop between testing, adjusting your model, and rolling out new features on success.

For an A/B test to be considered successful, you need to perform a statistical analysis of the data gathered from your population to determine if there is a statistically significant result. This analysis is based on the significance level you set for the experiment; a 5% significance level is considered the industry standard. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. A lower significance level means that we need stronger evidence for a statistically significant result. For more information about statistical significance, see A Refresher on Statistical Significance.

The next step is to calculate the p-value. The p-value is the probability of seeing a particular result (or greater) from zero, assuming that the null hypothesis is TRUE. In other words, the p-value is the expected fluctuation in a given sample, similar to the variance. For example, imagine we ran an A/A test where we displayed the same variation to two groups of users. After such an experiment, we would expect the metrics results across groups to be very similar but not dramatically different: a p-value greater than your significance level. Therefore, in an A/B test, we hope to see a p-value that is less than our significance level so we can conclude the influence on the business metric was the result of your variance group. AWS Partners such as Amplitude or Optimizely provide A/B testing tools to facilitate the setup and analysis of your experiments.

A/B tests are statistical measures of the efficacy of your Amazon Personalize recommendations, allowing you to quantify the impact these recommendations have on your business metrics. Additionally, A/B tests allows you to gather organic user-item interactions that you can use to train subsequent Amazon Personalize implementations. We recommend spending less time on offline tests and getting your Amazon Personalize recommendations in front of your users as quickly as possible. This helps eliminate biases from existing recommender systems in your training dataset, which allows your Amazon Personalize deployments to learn from organic user-item interactions data.

Conclusion

Amazon Personalize is an easy-to-use, highly scalable solution that can help you solve some of the most popular recommendation use cases:

• Personalized recommendations
• Similar items recommendations
• Personalized re-ranking of items

A/B testing provides invaluable information on how your customers interact with your Amazon Personalize recommendations. These results, measured according to well-defined business metrics, will give you a sense of the efficacy of these recommendations along with clues on how to further adjust your training datasets. After you iterate through this process multiple times, you will see an improvement on the metrics that matter most to improve customer engagement.

If this post helps you or inspires you to use A/B testing to improve your business metrics, please share your thoughts in the comments.

Additional resources

For more information about Amazon Personalize, see the following:

• Amazon Personalize can now create up to 50% better recommendations for fast changing catalogs of new products and fresh content
• Increasing the relevance of your Amazon Personalize recommendations by leveraging contextual information
• Enhancing recommendation filters by filtering on item metadata with Amazon Personalize

About the Author

Luis Lopez Soria is an AI/ML specialist solutions architect working with the AWS machine learning team. He works with AWS customers to help them adopt machine learning on a large scale. He enjoys playing sports, traveling around the world, and exploring new foods and cultures.

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This blog post was co-authored, and includes an introduction, by Rob Smedley, Director of Data Systems at Formula 1

Formula 1 (F1) racing is the most complex sport in the world. It is the blended perfection of human and machine that create the winning formula. It is this blend that makes F1 racing, or more pertinently, the driver talent, so difficult to understand. How many races or Championships would Michael Schumacher really have won without the power of Benetton and later, Ferrari, and the collective technical genius that were behind those teams? Could we really have seen Lewis Hamilton win six World Championships if his career had taken a different turn and he was confined to back-of-the-grid machinery? Maybe these aren’t the best examples because they are two of the best drivers the world has ever seen. There are many examples, however, of drivers whose real talent has remained fairly well hidden throughout their career. Those that never got that “right place, right time” break into a winning car and, therefore, those that will be forever remembered as a midfield driver.

The latest F1 Insight powered by AWS is designed to build mathematical models and algorithms that can help us answer the perennial question: who is the fastest driver of all time? F1 and AWS scientists have spent almost a year building these models and algorithms to bring us that very insight. The output focuses solely on one element of a driver’s vast armory—the pure speed that is most evident on a Saturday afternoon during the qualifying hour. It doesn’t focus on racecraft or the ability to win races or drive at 200 mph while still having the bandwidth to understand everything going on around you (displayed so well by the likes of Michael Schumacher or Fernando Alonso). This ability, which transgresses speed alone, allowed them both, on many an occasion, to operate as master tacticians. For someone like myself, who has had the honor of watching those very skills in action from the pitwall, I cannot emphasize enough how important those skills are—they are the difference between the good and the great. It is important to point out that these skills are not included in this insight. This is about raw speed only and the ability to push the car to its very limits over one lap.

The output and the list of the fastest drivers of all time (based on the F1 Historic Data Repository information spanning from 1983 to present day) offers some great names indeed. Of course, there are the obvious ones that rank highly—Ayrton Senna, Michael Schumacher, Lewis Hamilton, all of whom emerge as the top five fastest drivers. However, there are some names that many may not think of as top 20 drivers on first glance. A great example I would cite is Heikki Kovalainen. Is that the Kovalainen that finished his career circling round at the back of the Grand Prix field in Caterham, I hear you ask? Yes in fact, it’s the very same. For those of us who watched Kovalainen throughout his F1 career, it comes as little surprise that he is so high up the list when we consider pure speed. Look at his years on the McLaren team against Lewis Hamilton. The qualifying speaks volumes, with the median difference of just 0.1 seconds per lap. Ask Kovalainen himself and he’ll tell you that he didn’t perform at the same level as Hamilton in the races for many reasons (this is a tough business, believe me). But in qualifying, his statistics speak for themselves—the model has ranked him so highly because of his consistent qualifying performances throughout his career. I, for one, am extremely happy to see Kovalainen get the data-driven recognition that he deserves for that raw talent that was always on display during qualifying. There are others in the list, too, and hopefully some of these are your favorites—drivers that you have been banging the drum about for the last 10, 20, 40 years; the ones that might never have gotten every break, but you were able to see just how talented they were.

— Rob Smedley

Fastest Driver

As part of F1’s 70th anniversary celebrations and to help fans better understand who are the fastest drivers in the sport’s history, F1 and the Amazon Machine Learning Solutions Lab teamed up to develop Fastest Driver, the latest F1 Insight powered by AWS.

Fastest Driver uses AWS machine learning (ML) to rank drivers using their qualifying sessions lap times from F1’s Historic Data Repository going back to 1983. In this post, we demonstrate how by using Amazon SageMaker, a fully managed service to build, train, and deploy ML models, the Fastest Driver insight can objectively determine the fastest drivers in F1.

Quantifying driver pace using qualifying data

We define pace as a driver’s lap time during qualifying sessions. Driver race performance depends on a large number of factors, such as weather conditions, car setup (such as tires), and race track. F1 qualifying sessions are split into three sessions: the first session eliminates cars that set a lap time in 16th position or lower, the second eliminates positions 11–15, and the final part determines the grid position of 1st (pole position) to 10th. We use all qualification data from the driver qualifying sessions to construct Fastest Driver.

Lap times from qualifying sessions are normalized to adjust for differences in race tracks, which enables us to pool lap times across different tracks. This normalization process equalizes driver lap time differences, helping us compare drivers across race tracks and eliminating the need to construct track-specific models to account for track alterations over time. Another important technique is that we compare qualifying data for drivers on the same race team (such as Aston Martin Red Bull Racing), where teammates have competed against each other in a minimum of five qualifying sessions. By holding the team constant, we get a direct performance comparison under the same race conditions while controlling for car effects.

Differences in race conditions (such as wet weather) and rule changes (such as rule impacts) leads to significant variations in driver performances. We identify and remove anomalous lap time outliers by using deviations from median lap times between teammates with a 2-second threshold. For example, let’s compare Daniel Ricciardo with Sebastian Vettel when they raced together for Red Bull in 2014. During that season, Ricciardo was, on average, 0.2 seconds faster than Vettel. However, the average lap time difference between Ricciardo and VetteI falls to 0.1 seconds if we exclude the 2014 US Grand Prix (GP), where Ricciardo was more than 2 seconds faster than Vettel on account of Vettel being penalized to comply with the 107% rule (which forced him to start from the pit lane).

Constructing Fastest Driver

Building a performant ML model starts with good data. Following the driver qualification data aggregation process, we construct a network of teammate comparisons over the years, with the goal of comparing drivers across all teams, seasons, and circuits. For example, Sebastian Vettel and Max Verstappen have never been on the same team, so we compare them through their respective connections with Daniel Ricciardo at Red Bull. Ricciardo was, on average, 0.18 seconds slower than Verstappen during the 2016–2018 seasons while they were at Red Bull. We remove outlier sessions, such as the 2018 GPs in Bahrain, where Ricciardo was quicker than Verstappen by large margins because Verstappen didn’t get past Q1 due to a crash. If each qualifying session is assumed to be equally important, a subset of our driver network including only Ricciardo, Vettel, and Verstappen yields Verstappen as the fastest driver: Verstappen was 0.18 seconds faster than Ricciardo, and Ricciardo 0.1 seconds faster than Vettel.

Using the full driver network, we can compare all driver pairings to determine the faster racers. Going back to Heikki Kovalainen, let’s look at his years in the McLaren team against Lewis Hamilton. The qualifying speaks volumes with the median difference of just 0.1 seconds per lap. Kovalainen doesn’t have the same number of World Championships as Hamilton, but his qualifying statistics speak for themselves—the model has ranked him high because of his consistent qualifying performance throughout his career.

An algorithm called the Massey’s method (a form of linear regression) is one of the core models behind the Insight. Fastest Driver uses Massey’s method to rank drivers by solving for a set of linear equations, where each driver’s rating is calculated as their average lap time difference against teammates. Additionally, when comparing ratings of teammates, the model uses features like driver strength of schedule normalized by the number of interactions with the driver. Overall, the model places high rankings to drivers who perform extraordinarily well against their teammates or perform well against strong opponents.

Our goal is to assign each driver a numeric rating to infer that a driver’s competitive advantage relative to other drivers assuming the expected margin of lap time difference in any race is proportional to the difference in a driver’s true intrinsic rating. For the more mathematically inclined reader: let x_j represent each of all drivers and r_j represent the true intrinsic driver ratings. For every race, we can predict the margin of the lap time advantage or disadvantage (y_i) between any pair of two drivers as:

In this equation, x_j is +1 for the winner and -1 for the loser, and e_i is the error term due to unexplained variations. For a given set of m game observations and n drivers, we can formulate an (m * n) system of linear equations:

Driver ratings (r) is a solution to the normal equation via linear regression:

The following example code of Massey’s method and calculating driver rankings using Amazon SageMaker demonstrates the training process:

1.	import numpy as np
2.	import pandas as pd
3.	import statsmodels.api as sm
4.	from scipy.stats import norm 
5.	
6.	#example data comparing five drivers
7.	data = pd.DataFrame([[1, 0, 88, 90], 
8.	                     [2, 1, 87, 88],
9.	                     [2, 0, 87, 90],
10.	                     [3, 4, 88, 92],
11.	                     [3, 1, 88, 90],
12.	                     [1, 4, 90, 92]], columns=['Driver1', 'Driver2', 'Driver1_laptime', 'Driver2_laptime'])
13.	
14.	def init_linear_regressor_matrix(data, num_of_drivers, col_to_rank):
15.	    """initialize linear system matrix for regression"""
16.	    wins = np.zeros((data.shape[0], num_of_drivers))
17.	    score_diff = np.zeros(data.shape[0])
18.	
19.	    for index, row in data.iterrows():
20.	        idx1 = row["Driver1"]
21.	        idx2 = row["Driver2"]
22.	        if row['Driver1_laptime'] - row['Driver2_laptime'] > 0:
23.	            wins[(index)][(idx1)] = -1
24.	            wins[(index)][(idx2)] = 1
25.	            score_diff[(index)] = row['Driver1_laptime'] - row['Driver2_laptime']
26.	        else:
27.	            wins[(index)][(idx1)] = 1
28.	            wins[(index)][(idx2)] = -1
29.	            score_diff[(index)] = row['Driver2_laptime'] - row['Driver1_laptime']
30.	    wins_df = pd.DataFrame(wins)
31.	    wins_df[col_to_rank] = score_diff
32.	    return wins_df 	
33.	
34.	def massey(data, num_of_drivers, col_to_rank='delta'):
35.	    """Compute for each driver, adjacency matrix and aggregated scores, as input to the Massey Model"""
36.	
37.	    wins_df = init_linear_regressor_matrix(data, num_of_drivers, col_to_rank)
38.	    model = sm.OLS(
39.	        wins_df[col_to_rank], wins_df.drop(columns=[col_to_rank])
40.	    )
41.	    results = model.fit(cov_type='HC1')
42.	    rankings = pd.DataFrame(results.params)
43.	    rankings['std'] = np.sqrt(np.diag(results.cov_params()))
44.	    rankings['consistency'] = (norm.ppf(0.9)-norm.ppf(0.1))*rankings['std']
45.	    rankings = (
46.	        rankings
47.	        .sort_values(by=0, ascending=False)
48.	        .reset_index()
49.	        .rename(columns={"index": "Driver", 0: "massey"})
50.	    )
51.	    rankings = rankings.sort_values(by=["massey"], ascending=False)
52.	    rankings["massey_new"] = rankings["massey"].max() - rankings["massey"]
53.	    return rankings[['Driver', 'massey_new']]
54.	
55.	rankings = massey(data, 5)
56.	print(rankings)

The kings of the asphalt

Topping our list of rankings of fastest drivers are the esteemed Ayrton Senna, Michael Schumacher, Lewis Hamilton, Max Verstappen, and Fernando Alonso. This is delivered through the Fastest Driver insight, which produces a dataset ranking based on speed (or qualifying times) of all drivers from the present day back to 1983, by simply ranking drivers in descending order of Driver, Rank (integer), Gap to Best (milliseconds).

It’s important to note that to quantify a driver’s ability, we need to observe a minimum number of interactions. To factor this in, we only include teammates who have competed against each other in at least five qualifying sessions. A number of parameters and considerations have been put in place as an effective means of identifying various conditions with unfair comparisons, such as crashes, failures, age, career breaks, or weather conditions changing over qualifying sessions.

Furthermore, we noticed that if a driver re-joined F1 following a break of three years or more (such as Michael Schumacher in 2010, Pedro de la Rosa in 2010, Narain Karthikeyan in 2011, and Robert Kubica in 2019), this adds a 0.1 second advantage to driver relative pace. This is exemplified when drivers have a large age gap with their teammates, such as Mark Webber vs. Sebastian Vettel in 2013, Felipe Massa vs. Lance Stroll in 2017, and Kimi Räikkönen vs. Antonio Giovinazzi in 2019. From 1983–2019, we observe that competing against a teammate who is significantly older gives a 0.06-second advantage.

These rankings aren’t proposed as definitive, and there will no doubt be disagreement among fans. In fact, we encourage a healthy debate! Fastest Driver presents a scientific approach to driver ranking aimed at objectively assessing a driver’s performance controlling for car difference.

Lightweight and flexible deployment with Amazon SageMaker

To deliver the insights from Fastest Driver, we implemented Massey’s method on a Python web server. One complication was that the qualifying data consumed by the model is updated with fresh lap times after every race weekend. To handle this, in addition to the standard request to the web server for the rankings, we implemented a refresh request that instructs the server to download new qualifying data from Amazon Simple Storage Service (Amazon S3).

We deployed our model web server to an Amazon SageMaker model endpoint. This makes sure that our endpoint is highly available, because multi-instance Amazon SageMaker model endpoints are distributed across multiple Availability Zones by default, and have automatic scaling capabilities built in. As an additional benefit, the endpoints integrate with other Amazon SageMaker features, such as Amazon SageMaker Model Monitor, which automatically monitors model drift in an endpoint. Using a fully-managed service like Amazon SageMaker means our final architecture is very lightweight. To complete the deployment, we added an API layer around our endpoint using Amazon API Gateway and AWS Lambda. The following diagram shows this architecture in action.

The architecture includes the following steps:

1. The user makes a request to API Gateway.
2. API Gateway passes the request to a Lambda function.
3. The Lambda function makes a request to the Amazon SageMaker model endpoint. If the request is for rankings, the endpoint computes the UDC rankings using the currently available qualifying data and returns the result. If the request is to refresh, the endpoint downloads the new qualifying data from Amazon S3.

Summary

In this post, we described how F1 and the Amazon ML Solutions Lab scientists collaborated to create Fastest Driver, the first objective and data-driven model to determine who might be the fastest driver ever. This collaborative work between F1 and AWS has provided a unique view of one of the sport’s most enduring questions by looking back at its history on its 70th anniversary. Although F1 is the first to employ ML in this way, you can apply the technology to answer complex questions in sports, or even settle age-old disputes with fans of rival teams. This F1 season, fans will have many opportunities to see Fastest Driver in action and launch into their own debates about the sport’s all-time fastest drivers.

Sports leagues around the world are using AWS machine learning technology to transform the fan experience. The Guinness Six Nations Rugby Championship competition and Germany’s Bundesliga use AWS to bring fans closer to the action of the game and deliver deeper insights. In America, the NFL uses AWS to bring advanced stats to fans, players, and the league to improve player health and safety initiatives using AI and ML.

If you’d like help accelerating your use of ML in your products and processes, please contact the Amazon ML Solutions Lab program.

About the Authors

Rob Smedley has over 20 years of experience in the world of motorsport, having spent time at multiple F1 teams including Jordan, as a Race Engineer at Ferrari and most recently as Head of Vehicle Performance at Williams. He is now Director of Data systems at Formula 1, and oversees the F1 Insights program from a technical data side.

Colby Wise is a Data Scientist and manager at the Amazon ML Solutions Lab, where he helps AWS customers across numerous industries accelerate their AI and cloud adoption.

Delger Enkhbayar is a data scientist in the Amazon ML Solutions Lab. She has worked on a wide range of deep learning use cases in sports analytics, public sector and healthcare. Her background is in mechanism design and econometrics.

Guang Yang is a data scientist at the Amazon ML Solutions Lab where he works with customers across various verticals and applies creative problem solving to generate value for customers with state-of-the-art ML/AI solutions.

Ryan Cheng is a Deep Learning Architect in the Amazon ML Solutions Lab. He has worked on a wide range of ML use cases from sports analytics to optical character recognition. In his spare time, Ryan enjoys cooking.

George Price is a Deep Learning Architect at the Amazon ML Solutions Lab where he helps build models and architectures for AWS customers. Previously, he was a software engineer working on Amazon Alexa.

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