Posts in category: Amazon AWS

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including data preparation, feature engineering, model building, deployment, continuous monitoring, and retraining. For many enterprises, a lot of these steps are still manual and loosely integrated with each other. Therefore, it’s important to automate the end-to-end ML lifecycle, which enables frequent experiments to drive better business outcomes. Data preparation is one of the crucial steps in this lifecycle, because the ML model’s accuracy depends on the quality of the training dataset.

Data scientists and ML engineers spend somewhere between 70–80% of their time collecting, analyzing, cleaning, and transforming data required for model training. Amazon SageMaker Data Wrangler is a fully managed capability of Amazon SageMaker that makes it faster for data scientists and ML engineers to analyze and prepare data for their ML projects with little to no code. When it comes to operationalizing an end-to-end ML lifecycle, data preparation is almost always the first step in the process. Given that there are many ways to build an end-to-end ML pipeline, in this post we discuss how you can easily integrate Data Wrangler with some of the well-known workflow automation and orchestration technologies.

Solution overview

In this post, we demonstrate how users can integrate data preparation using Data Wrangler with Amazon SageMaker Pipelines, AWS Step Functions, and Apache Airflow with Amazon Managed Workflow for Apache Airflow (Amazon MWAA). Pipelines is a SageMaker feature that is a purpose-built and easy-to-use continuous integration and continuous delivery (CI/CD) service for ML. Step Functions is a serverless, low-code visual workflow service used to orchestrate AWS services and automate business processes. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to operate end-to-end data and ML pipelines.

For demonstration purposes, we consider a use case to prepare data to train an ML model with the SageMaker built-in XGBoost algorithm that will help us identify fraudulent vehicle insurance claims. We used a synthetically generated set of sample data to train the model and create a SageMaker model using the model artifacts from the training process. Our goal is to operationalize this process end to end by setting up an ML workflow. Although ML workflows can be more elaborate, we use a minimal workflow for demonstration purposes. The first step of the workflow is data preparation with Data Wrangler, followed by a model training step, and finally a model creation step. The following diagram illustrates our solution workflow.

In the following sections, we walk you through how to set up a Data Wrangler flow and integrate Data Wrangler with Pipelines, Step Functions, and Apache Airflow.

Set up a Data Wrangler flow

We start by creating a Data Wrangler flow, also called a data flow, using the data flow UI via the Amazon SageMaker Studio IDE. Our sample dataset consists of two data files: claims.csv and customers.csv, which are stored in an Amazon Simple Storage Service (Amazon S3) bucket. We use the data flow UI to apply Data Wrangler’s built-in transformations such categorical encoding, string formatting, and imputation to the feature columns in each of these files. We also apply custom transformation to a few feature columns using a few lines of custom Python code with Pandas DataFrame. The following screenshot shows the transforms applied to the claims.csv file in the data flow UI.

Finally, we join the results of the applied transforms of the two data files to generate a single training dataset for our model training. We use Data Wrangler’s built-in join datasets capability, which lets us perform SQL-like join operations on tabular data. The following screenshot shows the data flow in the data flow UI in Studio. For step-by-step instructions to create the data flow using Data Wrangler, refer to the GitHub repository.

You can now use the data flow (.flow) file to perform data transformations on our raw data files. The data flow UI can automatically generate Python notebooks for us to use and integrate directly with Pipelines using the SageMaker SDK. For Step Functions, we use the AWS Step Functions Data Science Python SDK to integrate our Data Wrangler processing with a Step Functions pipeline. For Amazon MWAA, we use a custom Airflow operator and the Airflow SageMaker operator. We discuss each of these approaches in detail in the following sections.

Integrate Data Wrangler with Pipelines

SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct SageMaker integration. Along with the SageMaker model registry, Pipelines improves the operational resilience and reproducibility of your ML workflows. These workflow automation components enable you to easily scale your ability to build, train, test, and deploy hundreds of models in production; iterate faster; reduce errors due to manual orchestration; and build repeatable mechanisms. Each step in the pipeline can keep track of the lineage, and intermediate steps can be cached for quickly rerunning the pipeline. You can create pipelines using the SageMaker Python SDK.

A workflow built with SageMaker pipelines consists of a sequence of steps forming a Directed Acyclic Graph (DAG). In this example, we begin with a processing step, which runs a SageMaker Processing job based on the Data Wrangler’s flow file to create a training dataset. We then continue with a training step, where we train an XGBoost model using SageMaker’s built-in XGBoost algorithm and the training dataset created in the previous step. After a model has been trained, we end this workflow with a RegisterModel step to register the trained model with the SageMaker model registry.

Installation and walkthrough

To run this sample, we use a Jupyter notebook running Python3 on a Data Science kernel image in a Studio environment. You can also run it on a Jupyter notebook instance locally on your machine by setting up the credentials to assume the SageMaker execution role. The notebook is lightweight and can be run on an ml.t3.medium instance. Detailed step-by-step instructions can be found in the GitHub repository.

You can either use the export feature in Data Wrangler to generate the Pipelines code, or build your own script from scratch. In our sample repository, we use a combination of both approaches for simplicity. At a high level, the following are the steps to build and run the Pipelines workflow:

1. Generate a flow file from Data Wrangler or use the setup script to generate a flow file from a preconfigured template.
2. Create an Amazon Simple Storage Service (Amazon S3) bucket and upload your flow file and input files to the bucket. In our sample notebook, we use the SageMaker default S3 bucket.
3. Follow the instructions in the notebook to create a Processor object based on the Data Wrangler flow file, and an Estimator object with the parameters of the training job.
   1. In our example, because we only use SageMaker features and the default S3 bucket, we can use Studio’s default execution role. The same AWS Identity and Access Management (IAM) role is assumed by the pipeline run, the processing job, and the training job. You can further customize the execution role according to minimum privilege.
4. Continue with the instructions to create a pipeline with steps referencing the Processor and Estimator objects, and then run the pipeline. The processing and training jobs run on SageMaker managed environments and take a few minutes to complete.
5. In Studio, you can see the pipeline details monitor the pipeline run. You can also monitor the underlying processing and training jobs from the SageMaker console or from Amazon CloudWatch.

Integrate Data Wrangler with Step Functions

With Step Functions, you can express complex business logic as low-code, event-driven workflows that connect different AWS services. The Step Functions Data Science SDK is an open-source library that allows data scientists to create workflows that can preprocess datasets and build, deploy, and monitor ML models using SageMaker and Step Functions. Step Functions is based on state machines and tasks. Step Functions creates workflows out of steps called states, and expresses that workflow in the Amazon States Language. When you create a workflow using the Step Functions Data Science SDK, it creates a state machine representing your workflow and steps in Step Functions.

For this use case, we built a Step Functions workflow based on the common pattern used in this post that includes a processing step, training step, and RegisterModel step. In this case, we import these steps from the Step Functions Data Science Python SDK. We chain these steps in the same order to create a Step Functions workflow. The workflow uses the flow file that was generated from Data Wrangler, but you can also use your own Data Wrangler flow file. We reuse some code from the Data Wrangler export feature for simplicity. We run the data preprocessing logic generated by Data Wrangler flow file to create a training dataset, train a model using the XGBoost algorithm, and save the trained model artifact as a SageMaker model. Additionally, in the GitHub repo, we also show how Step Functions allows us to try and catch errors, and handle failures and retries with FailStateStep and CatchStateStep.

The resulting flow diagram, as shown in the following screenshot, is available on the Step Functions console after the workflow has started. This helps data scientists and engineers visualize the entire workflow and every step within it, and access the linked CloudWatch logs for every step.

Installation and walkthrough

To run this sample, we use a Python notebook running with a Data Science kernel in a Studio environment. You can also run it on a Python notebook instance locally on your machine by setting up the credentials to assume the SageMaker execution role. The notebook is lightweight and can be run on a t3 medium instance for example. Detailed step-by-step instructions can be found in the GitHub repository.

You can either use the export feature in Data Wrangler to generate the Pipelines code and modify it for Step Functions or build your own script from scratch. In our sample repository, we use a combination of both approaches for simplicity. At a high level, the following are the steps to build and run the Step Functions workflow:

1. Generate a flow file from Data Wrangler or use the setup script to generate a flow file from a preconfigured template.
2. Create an S3 bucket and upload your flow file and input files to the bucket.
3. Configure your SageMaker execution role with the required permissions as mentioned earlier. Refer to the GitHub repository for detailed instructions.
4. Follow the instructions to run the notebook in the repository to start a workflow. The processing job runs on a SageMaker-managed Spark environment and can take few minutes to complete.
5. Go to Step Functions console and track the workflow visually. You can also navigate to the linked CloudWatch logs to debug errors.

Let’s review some important sections of the code here. To define the workflow, we first define the steps in the workflow for the Step Function state machine. The first step is the data_wrangler_step for data processing, which uses the Data Wrangler flow file as an input to transform the raw data files. We also define a model training step and a model creation step named training_step and model_step, respectively. Finally, we create a workflow by chaining all the steps we created, as shown in the following code:

from stepfunctions.steps import Chain 
from stepfunctions.workflow import Workflow 
import uuid 

workflow_graph = Chain([data_wrangler_step, training_step,model_step ]) 
branching_workflow = Workflow( name = "Wrangler-SF-Run-{}".format(uuid.uuid1().hex),definition = workflow_graph, role = iam_role ) 
branching_workflow.create()

In our example, we built the workflow to take job names as parameters because they’re unique and need to be randomly generated during every pipeline run. We pass these names when the workflow runs. You can also schedule Step Functions workflow to run using CloudWatch (see Schedule a Serverless Workflow with AWS Step Functions and Amazon CloudWatch), invoked using Amazon S3 Events, or invoked from Amazon EventBridge (see Create an EventBridge rule that triggers a Step Functions workflow). For demonstration purposes, we can invoke the Step Functions workflow from the Step Functions console UI or using the following code from the notebook.

# Execute workflow 
execution = branching_workflow.execute( 
			inputs=  { “ProcessingJobName”: processing_job_name, # Unique processing job name, 
				   “TrainingJobName”: training_job_name, # Unique training job name, 
				   “ModelName” : model_name # Unique model name 
				 } 
	) 
execution_output = execution.get_output(wait=True)

Integrate Data Wrangler with Apache Airflow

Another popular way of creating ML workflows is using Apache Airflow. Apache Airflow is an open-source platform that allows you to programmatically author, schedule, and monitor workflows. Amazon MWAA makes it easy to set up and operate end-to-end ML pipelines with Apache Airflow in the cloud at scale. An Airflow pipeline consists of a sequence of tasks, also referred to as a workflow. A workflow is defined as a DAG that encapsulates the tasks and the dependencies between them, defining how they should run within the workflow.

We created an Airflow DAG within an Amazon MWAA environment to implement our MLOps workflow. Each task in the workflow is an executable unit, written in Python programming language, that performs some action. A task can either be an operator or a sensor. In our case, we use an Airflow Python operator along with SageMaker Python SDK to run the Data Wrangler Python script, and use Airflow’s natively supported SageMaker operators to train the SageMaker built-in XGBoost algorithm and create the model from the resulting artifacts. We also created a helpful custom Data Wrangler operator (SageMakerDataWranglerOperator) for Apache Airflow, which you can use to process Data Wrangler flow files for data processing without the need for any additional code.

The following screenshot shows the Airflow DAG with five steps to implement our MLOps workflow.

The Start step uses a Python operator to initialize configurations for the rest of the steps in the workflow. SageMaker_DataWrangler_Step uses SageMakerDataWranglerOperator and the data flow file we created earlier. SageMaker_training_step and SageMaker_create_model_step use the built-in SageMaker operators for model training and model creation, respectively. Our Amazon MWAA environment uses the smallest instance type (mw1.small), because the bulk of the processing is done via Processing jobs, which uses its own instance type that can be defined as configuration parameters within the workflow.

Installation and walkthrough

Detailed step-by-step installation instructions to deploy this solution can be found in our GitHub repository. We used a Jupyter notebook with Python code cells to set up the Airflow DAG. Assuming you have already generated the data flow file, the following is a high-level overview of the installation steps:

1. Create an S3 bucket and subsequent folders required by Amazon MWAA.
2. Create an Amazon MWAA environment. Note that we used Airflow version 2.0.2 for this solution.
3. Create and upload a requirements.txt file with all the Python dependencies required by the Airflow tasks and upload it to the /requirements directory within the Amazon MWAA primary S3 bucket. This is used by the managed Airflow environment to install the Python dependencies.
4. Upload the SMDataWranglerOperator.py file to the /dags directory. This Python script contains code for the custom Airflow operator for Data Wrangler. This operator can be used for tasks to process any .flow file.
5. Create and upload the config.py script to the /dags directory. This Python script is used for the first step of our DAG to create configuration objects required by the remaining steps of the workflow.
6. Finally, create and upload the ml_pipelines.py file to the /dags directory. This script contains the DAG definition for the Airflow workflow. This is where we define each of the tasks, and set up dependencies between them. Amazon MWAA periodically polls the /dags directory to run this script to create the DAG or update the existing one with any latest changes.

The following is the code for SageMaker_DataWrangler_step, which uses the custom SageMakerDataWranglerOperator. With just a few lines of code in your DAG definition Python script, you can point the SageMakerDataWranglerOperator to the Data Wrangler flow file location (which is an S3 location). Behind the scenes, this operator uses SageMaker Processing jobs to process the .flow file in order to apply the defined transforms to your raw data files. You can also specify the type of instance and number of instances needed by the Data Wrangler processing job.

# Airflow Data Wrangler operator 
from SMDataWranglerOperator import SageMakerDataWranglerOperator 
preprocess_task = SageMakerDataWranglerOperator( task_id='DataWrangler_Processing_Step', 
                                                 dag=dag, 
                                                 flow_file_s3uri = flow_uri, 
                                                 processing_instance_count=2, 
                                                 instance_type='ml.m5.4xlarge', 
                                                 aws_conn_id="aws_default", 
                                                 config=config)

The config parameter accepts a dictionary (key-value pairs) of additional configurations required by the processing job, such as the output prefix of the final output file, type of output file (CSV or Parquet), and URI for the built-in Data Wrangler container image. The following code is what the config dictionary for SageMakerDataWranglerOperator looks like. These configurations are required for a SageMaker Processing processor. For details of each of these config parameters, refer to sagemaker.processing.Processor().

{
	"sagemaker_role": #required SageMaker IAM Role name or ARN,
	"s3_data_type": #optional;defaults to "S3Prefix"
	"s3_input_mode": #optional;defaults to "File",
	"s3_data_distribution_type": #optional;defaults to "FullyReplicated",
	"kms_key": #optional;defaults to None,
	"volume_size_in_gb": #optional;defaults to 30,
	"enable_network_isolation": #optional;defaults to False,
	"wait_for_processing": #optional;defaults to True,
	"container_uri": #optional;defaults to built - in container URI,
	"container_uri_pinned": #optional;defaults to built - in container URI,
	"outputConfig": {
		"s3_output_upload_mode": #optional;defaults to EndOfJob
		"output_content_type": #optional;defaults to CSV
		"output_bucket": #optional;defaults to SageMaker Default bucket
		"output_prefix": #optional;defaults to None.Prefix within bucket where output will be written
	}
}

Clean up

To avoid incurring future charges, delete the resources created for the solutions you implemented.

1. Follow these instructions provided in the GitHub repository to clean up resources created by the SageMaker Pipelines solution.
2. Follow these instructions provided in the GitHub repository to clean up resources created by the Step Functions solution.
3. Follow these instructions provided in the GitHub repository to clean up resources created by the Amazon MWAA solution.

Conclusion

This post demonstrated how you can easily integrate Data Wrangler with some of the well-known workflow automation and orchestration technologies in AWS. We first reviewed a sample use case and architecture for the solution that uses Data Wrangler for data preprocessing. We then demonstrated how you can integrate Data Wrangler with Pipelines, Step Functions, and Amazon MWAA.

As a next step, you can find and try out the code samples and notebooks in our GitHub repository using the detailed instructions for each of the solutions discussed in this post. To learn more about how Data Wrangler can help your ML workloads, visit the Data Wrangler product page and Prepare ML Data with Amazon SageMaker Data Wrangler.

About the authors

Rodrigo Alarcon is a Senior ML Strategy Solutions Architect with AWS based out of Santiago, Chile. In his role, Rodrigo helps companies of different sizes generate business outcomes through cloud-based AI and ML solutions. His interests include machine learning and cybersecurity.

Ganapathi Krishnamoorthi is a Senior ML Solutions Architect at AWS. Ganapathi provides prescriptive guidance to startup and enterprise customers helping them to design and deploy cloud applications at scale. He is specialized in machine learning and is focused on helping customers leverage AI/ML for their business outcomes. When not at work, he enjoys exploring outdoors and listening to music.

Anjan Biswas is a Senior AI Services Solutions Architect with focus on AI/ML and Data Analytics. Anjan is part of the world-wide AI services team and works with customers to help them understand, and develop solutions to business problems with AI and ML. Anjan has over 14 years of experience working with global supply chain, manufacturing, and retail organizations and is actively helping customers get started and scale on AWS AI services.

Read More

In the end, it came down to 213 thousandths of a second! That was the difference between the two best times in the finale of the first AWS AWS DeepRacer Student Wildcard event hosted in Ottawa, Canada this May.

I watched in awe as 13 students competed in a live wildcard race for the AWS DeepRacer Student League, the first global autonomous racing league for students offering educational material and resources to get hands on and start with machine learning (ML).

Students hit the starting line to put their ML skills to the test in Canada’s capital where members of parliament cheered them on, including Parliamentary Secretary for Innovation, Science and Economic Development, Andy Fillmore. Daphne Hong, a fourth-year engineering student at the University of Calgary, won the race with a lap time of 11:167 seconds. Not far behind were Nixon Chan from University of Waterloo and Vijayraj Kharod from Toronto Metropolitan University.

Daphne was victorious after battling nerves earlier in the day when she took practice runs as she struggled turning the corners and quickly adjusted her model. “After seeing how the physical track did compared to the virtual one throughout the day, I was able to make some adjustments and overcome those corners and round them as I intended, so I’m super, super happy about that,” said a beaming Daphne after being presented with her championship trophy.

Daphne also received a $1,000 Amazon Canada Gift Card, while the second and third place racers — Nixon Chan and Vijayraj Kharod — got trophies and $500 gift cards. The top two contestants now have a chance to race virtually in the AWS DeepRacer Student League finale in October. “The whole experience feels like a win for me,” said DeepRacer participant Connor Hunszinger from the University of Alberta.

The event not only highlighted the importance of machine learning education to Canadian policymakers, but also made clear that these young Canadians could be poised to do great things with their ML skills.

The road to the Ottawa Wildcard

This Ottawa race is one of several wildcard events taking place around the world this year as part of the AWS DeepRacer Student League to bring students together to compete live in person. The top two finalists in each Wildcard race will have the opportunity to compete in the AWS DeepRacer Student League finale, with a chance of winning up to $5,000 USD towards their tuition. The top three racers from the student league finale in October will advance to the global AWS DeepRacer League Championship held at AWS re:Invent in Las Vegas this December.

Students who raced in Ottawa began their journey this March when they competed in the global AWS DeepRacer Student League by submitting their model to the virtual 3D simulation environment and posting times to the leaderboard. From the student league, the top student racers across Canada were selected to compete in the wildcard event. Students trained their models in preparation for the event through the virtual environment and then applied their ML models for the first time on a physical track in Ottawa. Each student competitor was given one three-minute attempt to complete their fastest lap with only the speed of the car being controlled.

“Honestly, I don’t really consider my peers here my competitors. I loved being able to work with them. It seems more like a friendly, supportive and collaborative environment. We were always cheering each other on,” says Daphne Hong, AWS DeepRacer Student League Canada Wildcard winner. “This event is great because it allows people who don’t really have that much AI or ML experience to learn more about the industry and see it live with these cars. I want to share my findings and my knowledge with those around me, those in my community and spread the word about ML and AI.”

Building access to machine learning in Canada

Machine learning talent is in hot demand, making up a large portion of AI job postings in Canada. The Canadian economy needs people with the skills recently on display at the DeepRacer event, and Canadian policymakers are intent on building an AI talent pool.

According to the World Economic Forum, 58 million jobs will be created by the growth of machine learning in the next few years, but right now, there are only 300,000 engineers with the relevant training to build and deploy ML models.

That means organizations of all types must not only train their existing workers with ML skills, but also invest in training programs and solutions to develop those capabilities for future workers. AWS is doing its part with a multitude of products for learners of all levels.

• AWS Artificial Intelligence and Machine Learning Scholarship, a $10 million education and scholarship program, aimed at preparing underserved and underrepresented students in tech globally for careers in the space.
• AWS DeepRacer, the world’s first global autonomous racing league, open to developers globally to get started in ML with a 1/18^th scale race car driven by reinforcement learning. Developers can compete in the global racing league for prizes and rewards.
• AWS DeepRacer Student, a version of AWS DeepRacer open to students 16 years and older globally with free access to 20 hours of ML educational content and 10 hours of compute resources for model training monthly at no cost. Participants can compete in the global racing league exclusively for students to win scholarships and prizes.
• Machine Learning University, self service ML training courses with learn at your own pace educational content built by Amazon’s ML scientists.

Cloud computing makes access to machine learning technology a lot easier, faster — and fun, if the AWS DeepRacer Student League Wildcard event was any indication. The race was created by AWS, as an enjoyable, hands-on way to make ML more widely accessible to anyone interested in the technology.

Get started with your machine learning journey and take part in the AWS DeepRacer Student league today for your chance to wins prizes and glory.

About the author

Nicole Foster is Director of AWS Global AI/ML and Canada Public Policy at Amazon, where she leads the direction and strategy of artificial intelligence public policy for Amazon Web Services (AWS) around the world as well as the company’s public policy efforts in support of the AWS business in Canada. In this role, she focuses on issues related to emerging technology, digital modernization, cloud computing, cyber security, data protection and privacy, government procurement, economic development, skilled immigration, workforce development, and renewable energy policy.

Read More

Logistics and transportation companies track ETA (estimated time of arrival), which is a key metric for their business. Their downstream supply chain activities are planned based on this metric. However, delays often occur, and the ETA might differ from the product’s or shipment’s actual time of arrival (ATA), for instance due to shipping distance or carrier-related or weather-related issues. This impacts the entire supply chain, in many instances reducing productivity and increasing waste and inefficiencies. Predicting the exact day a product arrives to a customer is challenging because it depends on various factors such as order type, carrier, origin, and distance.

Analysts working in the logistics and transportation industry have domain expertise and knowledge of shipping and logistics attributes. However, they need to be able to generate accurate shipment ETA forecasts for efficient business operations. They need an intuitive, easy-to-use, no-code capability to create machine learning (ML) models for predicting shipping ETA forecasts.

To help achieve the agility and effectiveness that business analysts seek, we launched Amazon SageMaker Canvas, a no-code ML solution that helps companies accelerate solutions to business problems quickly and easily. SageMaker Canvas provides business analysts with a visual point-and-click interface that allows you to build models and generate accurate ML predictions on your own—without requiring any ML experience or having to write a single line of code.

In this post, we show how to use SageMaker Canvas to predict shipment ETAs.

Solution overview

Although ML development is a complex and iterative process, we can generalize an ML workflow into business requirements analysis, data preparation, model development, and model deployment stages.

SageMaker Canvas abstracts the complexities of data preparation and model development, so you can focus on delivering value to your business by drawing insights from your data without a deep knowledge of the data science domain. The following architecture diagram highlights the components in a no-code or low-code solution.

The following are the steps as outlined in the architecture:

1. Download the dataset to your local machine.
2. Import the data into SageMaker Canvas.
3. Join your datasets.
4. Prepare the data.
5. Build and train your model.
6. Evaluate the model.
7. Test the model.
8. Share the model for deployment.

Let’s assume you’re a business analyst assigned to the product shipment tracking team of a large logistics and transportation organization. Your shipment tracking team has asked you to assist in predicting the shipment ETA. They have provided you with a historical dataset that contains characteristics tied to different products and their respective ETA, and want you to predict the ETA for products that will be shipped in the future.

We use SageMaker Canvas to perform the following steps:

1. Import our sample datasets.
2. Join the datasets.
3. Train and build the predictive machine maintenance model.
4. Analyze the model results.
5. Test predictions against the model.

Dataset overview

We use two datasets (shipping logs and product description) in CSV format, which contain shipping log information and certain characteristics of a product, respectively.

The ShippingLogs dataset contains the complete shipping data for all products delivered, including estimated time shipping priority, carrier, and origin. It has approximately 10,000 rows and 12 feature columns. The following table summarizes the data schema.

The ProductDescription dataset contains metadata information of the product that is being shipped in the order. This dataset has approximately 10,000 rows and 5 feature columns. The following table summarizes the data schema.

Prerequisites

An IT administrator with an AWS account with appropriate permissions must complete the following prerequisites:

1. Deploy an Amazon SageMaker domain. For instructions, see Onboard to Amazon SageMaker Domain.
2. Launch SageMaker Canvas. For instructions, see Setting up and managing Amazon SageMaker Canvas (for IT administrators).
3. Configure cross-origin resource sharing (CORS) policies in Amazon Simple Storage Service (Amazon S3) for SageMaker Canvas to enable the upload option from local disk. For instructions, see Give your users the ability to upload local files.

Import the dataset

First, download the datasets (shipping logs and product description) and review the files to make sure all the data is there.

SageMaker Canvas provides several sample datasets in your application to help you get started. To learn more about the SageMaker-provided sample datasets you can experiment with, see Use sample datasets. If you use the sample datasets (canvas-sample-shipping-logs.csv and canvas-sample-product-descriptions.csv) available within SageMaker Canvas, you don’t have to import the shipping logs and product description datasets.

You can import data from different data sources into SageMaker Canvas. If you plan to use your own dataset, follow the steps in Importing data in Amazon SageMaker Canvas.

For this post, we use the full shipping logs and product description datasets that we downloaded.

1. Sign in to the AWS Management Console, using an account with the appropriate permissions to access SageMaker Canvas.
2. On the SageMaker Canvas console, choose Import.
   
3. Choose Upload and select the files ShippingLogs.csv and ProductDescriptions.csv.
4. Choose Import data to upload the files to SageMaker Canvas.
   

Create a consolidated dataset

Next, let’s join the two datasets.

1. Choose Join data.
   
2. Drag and drop ShippingLogs.csv and ProductDescriptions.csv from the left pane under Datasets to the right pane.
   The two datasets are joined using ProductID as the inner join reference.
3. Choose Import and enter a name for the new joined dataset.
4. Choose Import data.
   

You can choose the new dataset to preview its contents.

After you review the dataset, you can create your model.

Build and train model

To build and train your model, complete the following steps:

1. For Model name, enter ShippingForecast.
2. Choose Create.
   In the Model view, you can see four tabs, which correspond to the four steps to create a model and use it to generate predictions: Select, Build, Analyze, and Predict.
3. On the Select tab, select the ConsolidatedShippingData you created earlier.You can see that this dataset comes from Amazon S3, has 12 columns, and 10,000 rows.
4. Choose Select dataset.
   
   SageMaker Canvas automatically moves to the Build tab.
5. On the Build tab, choose the target column, in our case ActualShippingDays.
   Because we’re interested in how many days it will take for the goods to arrive for the customer, SageMaker Canvas automatically detects that this is a numeric prediction problem (also known as regression). Regression estimates the values of a dependent target variable based on one or more other variables or attributes that are correlated with it.Because we also have a column with time series data (OrderDate), SageMaker Canvas may interpret this as a time series forecast model type.
6. Before advancing, make sure that the model type is indeed Numeric model type; if that’s not the case, you can select it with the Change type option.
   

Data preparation

In the bottom half of the page, you can look at some of the statistics of the dataset, including missing and mismatched values, unique values, and mean and median values.

Column view provides you with the listing of all columns, their data types, and their basic statistics, including missing and mismatched values, unique values, and mean and median values. This can help you devise a strategy to handle missing values in the datasets.

Grid view provides you with a graphical distribution of values for each column and the sample data. You can start inferring relevant columns for the training the model.

Let’s preview the model to see the estimated RMSE (root mean squared error) for this numeric prediction.

You can also drop some of the columns, if you don’t want to use them for the prediction, by simply deselecting them. For this post, we deselect the order*_**id* column. Because it’s a primary key, it doesn’t have valuable information, and so doesn’t add value to the model training process.

You can choose Preview model to get insights on feature importance and iterate the model quickly. We also see the RMSE is now 1.223, which is improved from 1.225. The lower the RMSE, the better a given model is able to fit a dataset.

From our exploratory data analysis, we can see that the dataset doesn’t have a lot of missing values. Therefore, we don’t have to handle missing values. If you see a lot of missing values for your features, you can filter the missing values.

To extract more insights, you can proceed with a datetime extraction. With the datetime extraction transform, you can extract values from a datetime column to a separate column.

To perform a datetime extraction, complete the following steps:

1. On the Build tab of the SageMaker Canvas application, choose Extract.
2. Choose the column from which you want to extract values (for this post, OrderDate).
3. For Value, choose one or more values to extract from the column. For this post, we choose Year and Month.The values you can extract from a timestamp column are Year, Month, Day, Hour, Week of year, Day of year, and Quarter.
4. Choose Add to add the transform to the model recipe.
   

SageMaker Canvas creates a new column in the dataset for each of the values you extract.

Model training

It’s time to finally train the model! Before building a complete model, it’s a good practice to have a general idea about the performances that our model will have by training a quick model. A quick model trains fewer combinations of models and hyperparameters in order to prioritize speed over accuracy. This is helpful in cases like ours where we want to prove the value of training an ML model for our use case. Note that the quick build option isn’t available for models bigger than 50,000 rows.

Now we wait anywhere from 2–15 minutes for the quick build to finish training our model.

Evaluate model performance

When training is complete, SageMaker Canvas automatically moves to the Analyze tab to show us the results of our quick training, as shown in the following screenshot.

You may experience slightly different values. This is expected. Machine learning introduces some variation in the process of training models, which can lead to different results for different builds.

Let’s focus on the Overview tab. This tab shows you the column impact, or the estimated importance of each column in predicting the target column. In this example, the ExpectedShippingDays column has the most significant impact in our predictions.

On the Scoring tab, you can see a plot representing the best fit regression line for ActualshippingDays. On average, the model prediction has a difference of +/- 0.7 from the actual value of ActualShippingDays. The Scoring section for numeric prediction shows a line to indicate the model’s predicted value in relation to the data used to make predictions. The values of the numeric prediction are often +/- the RMSE value. The value that the model predicts is often within the range of the RMSE. The width of the purple band around the line indicates the RMSE range. The predicted values often fall within the range.

As the thickness of the RMSE band on a model increases, the accuracy of the prediction decreases. As you can see, the model predicts with high accuracy to begin with (lean band) and as the value of actualshippingdays increases (17–22), the band becomes thicker, indicating lower accuracy.

The Advanced metrics section contains information for users that want a deeper understanding of their model performance. The metrics for numeric prediction are as follows:

• R2 – The percentage of the difference in the target column that can be explained by the input column.
• MAE – Mean absolute error. On average, the prediction for the target column is +/- {MAE} from the actual value.
• MAPE – Mean absolute percent error. On average, the prediction for the target column is +/- {MAPE} % from the actual value.
• RMSE – Root mean square error. The standard deviation of the errors.

The following screenshot shows a graph of the residuals or errors. The horizontal line indicates an error of 0 or a perfect prediction. The blue dots are the errors. Their distance from the horizontal line indicates the magnitude of the errors.

R-squared is a statistical measure of how close the data is to the fitted regression line. The higher percentage indicates that the model explains all the variability of the response data around its mean 87% of the time.

On average, the prediction for the target column is +/- 0.709 {MAE} from the actual value. This indicates that on average the model will predict the target within half a day. This is useful for planning purposes.

The model has a standard deviation (RMSE) of 1.223. As you can see, the model predicts with high accuracy to begin with (lean band) and as the value of actualshippingdays increases (17–22), the band becomes thicker, indicating lower accuracy.

The following image shows an error density plot.

You now have two options as next steps:

• You can use this model to run some predictions by choosing Predict.
• You can create a new version of this model to train with the Standard build option. This will take much longer—about 4–6 hours—but will produce more accurate results.

Because we feel confident about using this model given the performances we’ve seen, we opt to go ahead and use the model for predictions. If you weren’t confident, you could have a data scientist review the modeling SageMaker Canvas did and offer potential improvements.

Note that training a model with the Standard build option is necessary to share the model with a data scientist with the Amazon SageMaker Studio integration.

Generate predictions

Now that the model is trained, let’s generate some predictions.

1. Choose Predict on the Analyze tab, or choose the Predict tab.
2. Choose Batch prediction.
3. Choose Select dataset, and choose the dataset ConsolidatedShipping.csv.

SageMaker Canvas uses this dataset to generate our predictions. Although it’s generally not a good idea not to use the same dataset for both training and testing, we’re using the same dataset for the sake of simplicity. You can also import another dataset if you desire.

After a few seconds, the prediction is done and you can choose the eye icon to see a preview of the predictions, or choose Download to download a CSV file containing the full output.

You can also choose to predict values one by one by selecting Single prediction instead of Batch prediction. SageMaker Canvas then shows you a view where you can provide the values for each feature manually and generate a prediction. This is ideal for situations like what-if scenarios—for example, how does ActualShippingDays change if the ShippingOrigin is Houston? What if we used a different carrier? What if the PackageWeight is different?

Standard build

Standard build chooses accuracy over speed. If you want to share the artifacts of the model with your data scientist and ML engineers, you may choose to create a standard build next.

First add a new version.

Then choose Standard build.

The Analyze tab shows your build progress.

When the model is complete, you can observe that the RMSE value of the standard build is 1.147, compared to 1.223 with the quick build.

After you create a standard build, you can share the model with data scientists and ML engineers for further evaluation and iteration.

Clean up

To avoid incurring future session charges, log out of SageMaker Canvas.

Conclusion

In this post, we showed how a business analyst can create a shipment ETA prediction model with SageMaker Canvas using sample data. SageMaker Canvas allows you to create accurate ML models and generate predictions using a no-code, visual, point-and-click interface. Analysts can take this to the next level by sharing their models with data scientist colleagues. The data scientists can view the SageMaker Canvas model in Studio, where they can explore the choices SageMaker Canvas made to generate ML models, validate model results, and even take the model to production with a few clicks. This can accelerate ML-based value creation and help scale improved outcomes faster.

• To learn more about using SageMaker Canvas, see Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas.
• For more information about creating ML models with a no-code solution, see Announcing Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts.

About the authors

Rajakumar Sampathkumar is a Principal Technical Account Manager at AWS, providing customers guidance on business-technology alignment and supporting the reinvention of their cloud operation models and processes. He is passionate about the cloud and machine learning. Raj is also a machine learning specialist and works with AWS customers to design, deploy, and manage their AWS workloads and architectures.

Meenakshisundaram Thandavarayan is a Senior AI/ML specialist with a passion to design, create and promote human-centered Data and Analytics experiences. He supports AWS Strategic customers on their transformation towards data driven organization.

Read More

This is a guest post co-written with Mutisya Ndunda from Trumid.

Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, increase the efficiency and accuracy of operational workflows, and enhance performance by supporting multiple aspects of the trading process.

Trumid is a financial technology company building tomorrow’s credit trading network—a marketplace for efficient trading, information dissemination, and execution between corporate bond market participants. Trumid is optimizing the credit trading experience by combining leading-edge product design and technology principles with deep market expertise. The result is an integrated trading solution delivering a full ecosystem of protocols and execution tools within one intuitive platform.

The bond trading market has traditionally involved offline buyer/seller matching processes aided by rules-based technology. Trumid has embarked on an initiative to transform this experience. Through its electronic trading platform, traders can access thousands of bonds to buy or sell, a community of engaged users to interact with, and a variety of trading protocols and execution solutions. With an expanding network of users, Trumid’s AI and Data Strategy team partnered with the AWS Machine Learning Solutions Lab. The objective was to develop ML systems that could deliver a more personalized trading experience by modeling the interest and preferences of users for bonds available on Trumid.

These ML models can be used to speed up time to insight and action by personalizing how information is displayed to each user to ensure that the most relevant and actionable information a trader may care about is prioritized and accessible.

To solve this challenge, Trumid and the ML Solutions Lab developed an end-to-end data preparation, model training, and inference process based on a deep neural network model built using the Deep Graph Library for Knowledge Embedding (DGL-KE). An end-to-end solution with Amazon SageMaker was also deployed.

Benefits of graph machine learning

Real-world data is complex and interconnected, and often contains network structures. Examples include molecules in nature, social networks, the internet, roadways, and financial trading platforms.

Graphs provide a natural way to model this complexity by extracting important and rich information that is embedded in the relations between entities.

Traditional ML algorithms require data to be organized as tables or sequences. This generally works well, but some domains are more naturally and effectively represented by graphs (such as a network of objects related to each other, as illustrated later in this post). Instead of coercing these graph datasets into tables or sequences, you can use graph ML algorithms to both represent and learn from the data as presented in its graph form, including information about constituent nodes, edges, and other features.

Considering that bond trading is inherently represented as a network of interactions between buyers and sellers involving various types of bond instruments, an effective solution needs to harness the network effects of the communities of traders that participate in the market. Let’s look at how we leveraged the trading network effects and implemented this vision here.

Solution

Bond trading is characterized by several factors, including trade size, term, issuer, rate, coupon values, bid/ask offer, and type of trading protocol involved. In addition to orders and trades, Trumid also captures “indications of interest” (IOIs). The historical interaction data embodies the trading behavior and the market conditions evolving over time. We used this data to build a graph of timestamped interactions between traders, bonds, and issuers, and used graph ML to predict future interactions.

The recommendation solution comprised four main steps:

• Preparing the trading data as a graph dataset
• Training a knowledge graph embedding model
• Predicting new trades
• Packaging the solution as a scalable workflow

In the following sections, we discuss each step in more detail.

Preparing the trading data as a graph dataset

There are many ways to represent trading data as a graph. One option is to represent the data exhaustively with nodes, edges, and properties: traders as nodes with properties (such as employer or tenure), bonds as nodes with properties (issuer, amount outstanding, maturity, rate, coupon value), and trades as edges with properties (date, type, size). Another option is to simplify the data and use only nodes and relations (relations are typed edges like traded or issued-by). This latter approach worked better in our case, and we used the graph represented in the following figure.

Graph of relations between traders, bonds and bond issuers

Additionally, we removed some of the edges considered obsolete: if a trader interacted with more than 100 different bonds, we kept only the last 100 bonds.

Finally, we saved the graph dataset as a list of edges in TSV format:

t987	trade-old		i55198
t995	trade-old		i55306
t987	trade-recent	i24528
t995	trade-recent	i49181
t987	ioi-recent		i24523
t995	ioi-old 		i49178
…
i49611	issued-by		XXX
i46569	issued-by		YYY
i46507	issued-by		ZZZ

Training a knowledge graph embedding model

For graphs composed only of nodes and relations (often called knowledge graphs), the DGL team developed the knowledge graph embedding framework DGL-KE. KE stands for knowledge embedding, the idea being to represent nodes and relations (knowledge) by coordinates (embeddings) and optimize (train) the coordinates so that the original graph structure can be recovered from the coordinates. In the list of available embedding models, we selected TransE (translational embeddings). TransE trains embeddings with the objective of approximating the following equality:

Source node embedding + relation embedding = target node embedding (1)

We trained the model by invoking the dglke_train command. The output of the training is a model folder containing the trained embeddings.

For more details about TransE, refer to Translating Embeddings for Modeling Multi-relational Data.

Predicting new trades

To predict new trades from a trader with our model, we used the equality (1): add the trader embedding to the trade-recent embedding and looked for bonds closest to the resulting embedding.

We did this in two steps:

1. Compute scores for all possible trade-recent relations with dglke_predict.
2. Compute the top 100 highest scores for each trader.

For detailed instructions on how to use the DGL-KE, refer to Training knowledge graph embeddings at scale with the Deep Graph Library and DGL-KE Documentation.

Packaging the solution as a scalable workflow

We used SageMaker notebooks to develop and debug our code. For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing. SageMaker processing allows you to run a script remotely on a chosen instance type and Docker image without having to worry about resource allocation and data transfer. This was simple and cost-effective for us, because the GPU instance is only used and paid for during the 15 minutes needed for the script to run.

For detailed instructions on how to use SageMaker processing, see Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation and Processing.

Results

Our custom graph model performed very well compared to other methods: performance improved by 80%, with more stable results across all trader types. We measured performance by mean recall (percentage of actual trades predicted by the recommender, averaged over all traders). With other standard metrics, the improvement ranged from 50–130%.

This performance enabled us to better match traders and bonds, indicating an enhanced trader experience within the model, with machine learning delivering a big step forward from hard-coded rules, which can be difficult to scale.

Conclusion

Trumid is focused on delivering innovative products and workflow efficiencies to their community of users. Building tomorrow’s credit trading network requires continuous collaboration with peers and industry experts like the AWS ML Solutions Lab, designed to help you innovate faster.

For more information, see the following resources:

• For a background on graph ML and use cases, refer to How AWS uses graph neural networks to meet customer needs.
• For more information about graph ML tools, explore DGL-KE, DGL, and Amazon Neptune.
• Deliver product recommendations with Amazon Personalize.
• Collaborate with the AWS Machine Learning Solutions Lab.

About the authors

Marc van Oudheusden is a Senior Data Scientist with the Amazon ML Solutions Lab team at Amazon Web Services. He works with AWS customers to solve business problems with artificial intelligence and machine learning. Outside of work you may find him at the beach, playing with his children, surfing or kitesurfing.

Mutisya Ndunda is the Head of Data Strategy and AI at Trumid. He is a seasoned financial professional with over 20 years of broad institutional experience in capital markets, trading, and financial technology.  Mutisya has a strong quantitative and analytical background with over a decade of experience in artificial intelligence, machine learning and big data analytics. Prior to Trumid, he was the CEO of Alpha Vertex, a financial technology company offering analytical solutions powered by proprietary AI algorithms to financial institutions. Mutisya holds a bachelor’s degree in Electrical Engineering from Cornell University and a master’s degree in Financial Engineering from Cornell University.

Isaac Privitera is a Senior Data Scientist at the Amazon Machine Learning Solutions Lab, where he develops bespoke machine learning and deep learning solutions to address customers’ business problems. He works primarily in the computer vision space, focusing on enabling AWS customers with distributed training and active learning.

Read More