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Aman Mujeeb explores the A360 AI platform through the eys of a new data scientist, and compares the ease of use of A360 AI to an industry heavyweight: Amazon SageMaker Studio. The following is an adaptation of Aman’s post on Medium about his comparative exploration, and has been edited with the author’s permission for content and clarity.


I am an Industrial Engineer from Penn State with one year of experience in Data Science. I have worked on numerous projects involving Supervised Learning, Unsupervised Learning, Deep Learning, and the machine learning library TensorFlow. As a new data scientist, I wanted to find a machine learning model development and deployment platform that is easy to use and requires less code and time to complete routine data science tasks. As part of my work, I decided to compare the A360 AI platform to another platform commonly used by experienced data scientists, Amazon SageMaker Studio.

Summary of Findings

Comparison Use Case

Realtive Importance of Selected Features

Key Differences Between A360 AI and SageMaker Studio

In the following sections, I’ll delineate the key differences between Sagemaker Studio and A360 AI throughout the model training and deployment lifecycle:

  1. Launching the Workspace

  2. DevOps and IT Support

  3. Model Development, Hyperparameter Tuning, Logging and Saving

  4. Model Deployment

  5. Ease of Use

  6. Snapshot View

  7. Monitoring

  8. Workflow Timeline

1. Launching the Workspace

Creating an S3 Bucket in AWS

Uploading Data to a S3 Bucket in AWS

After creating an S3 bucket for your project in the AWS web management portal,  you can create a SageMaker instance by navigating to the Studio web page under the SageMaker control panel on AWS and selecting “Launch Sagemaker Studio.”

Launching SageMaker Studio from the Amazon SageMaker Website

Clicking the “Launch” button will take you to a page to set up a SageMaker domain, with both “Quick” and “Standard” setup options. These options are where you can choose a JupyterLab notebook workspace. After setting up the SageMaker domain, you’ll be able to launch it from the control panel.

Configuring SageMaker Domain

Launching the SageMaker Domain from the Control Panel

In A360 AI, creating an online data repo and project workspace can be done together. Every A360 AI project has a default data repo that stores model artifacts, experiment tracking data, and other information. These data repos can also be used for reading and writing training data. Currently, A360 AI data repos are 1:1 with S3 buckets, although the platform abstracts out the hard work. You can choose to create a new S3 bucket/data repo or specify an existing S3 bucket.

Creating a Project in A360 AI

Adding a Data Repo to a New Project in A360 AI

After creating a project, you can create a workspace and assign it to that project. A360 AI gives you a drop-down menu of available container images that you can use to provision a JupyterLab notebook instance. I chose to start up a JupyterLab notebook with the Tensorflow ML library installed by default. The notebook environment also has Scipy, Pandas, and other common data science libraries pre-installed. Of course, if I wanted to install other tools, I could do that through the Jupyter notebook using the !pip -install command. When setting up a workspace you can allocate CPU and memory for your notebook server with either preconfigured and custom options. I chose the preconfigured option with the smallest available compute cluster with 2 CPUs and 8 GB memory.

Creating a Workspace in A360 AI

Uploading Data Using A360 AI

Uploading Data Using A360 AI

Loading Data:

Like SageMaker Studio and AWS, A360 AI gives users the ability to upload data to an S3 bucket through a GUI. In this exercise I wanted to explore uploading data using command line tools and notebook code. To load data in SageMaker Studio, users can use boto3 to interact with S3 in the notebook environment. A360 AI provides an MDK (model development kit) for users to interact with data repos/S3 buckets.  In Amazon SageMaker Studio it took me 13 lines of code to load two CSV files (X and Y as shown below) whereas in A360 AI it only took me 7 lines of code.

A360 AI and SageMaker Studio both perform well in terms of launching the workspace but SageMaker Studio does require support from the DevOps team to get started, as I will explain in the next section.

2. DevOps and IT Support

While launching Amazon SageMaker Studio, certain permission levels and IAM access were needed, which for a data scientist is not easy to understand. If the AWS administrator and data scientist in a company are seperate people, they will need a lot of back and forth communication between them to ensure that permissions and access are correctly configured. Because of the seamless provisioning that is provided to data scientist users on A360, I did not face any such issue while creating or launching the workspace.

Permission Error in AWS

3. Model Development, Hyperparameter Tuning, Logging, and Saving

Training a Model and Logging Experiments and Runs in A360 AI

Training a Model and Logging Experiments and Runs in SageMaker Studio

Tracking experimentation in SageMaker Studio was similar to A360 AI in a few aspects. I was not able to log my desired metrics in a neatly-formatted table as I was unable to find the documentation online. A360 AI was easier to use in terms of creating experiments and logging data compared to SageMaker Studio.

Saving a Model in SageMaker Studio

4. Model Deployment

Deployment of a Inferencing Model to AWS in SageMaker

Inference Script in AWS

In A360 AI, once the model is saved and the experiment is created, users can publish the chosen model and send it for one final review through the UI. A requirements text file is used to ensure that the proper dependancies are installed in the container with the deployed model.

Model Console in A360 AI

Packaging a Model in A360 AI

Requirements for Inferencing with Deployed Model

Making predictions with the model requires a simple prediction script.

A360 AI Prediction Script

Deploying a model is also performed through a UI in A360. In this case, I deployed my model to a Kubernetes cluster on Amazon AWS.

Deploying a Model in A360 AI

Running inference – or making predictions – can be done using a simple REST command pointed at the specific API endpoint for that deployed model. I tested the deployment and endpoint from a Jupyter notebook in A360 AI.

Testing the Endpoint in A360 AI from a Jupyter Notebook

SageMaker imposes a 5MB limit on endpoint payloads delivered to the platform as documented here. The endpoint capability on A360 AI is defined by the users.

5. Monitoring

A360 AI provides an integrated monitoring dashboard for users to monitor the cloud endpoint resource usage, availability, and hit frequency as well as data and concept drift. Amazon SageMaker Studio requires extra code to log performance metrics and does not provide a dashboard.

Resource Usage Monitoring in A360 AI

Availability Monitoring in A360 AI

Hit Frequency Monitoring in A360 AI

A360 AI’s neat UI lets data scientists monitor their model without code. Amazon SageMaker Studio requires more than 300 lines of code to monitor model deployments, which can be difficult to understand and time consuming.

A360 AI is not only designed for data scientists but also for machine learning engineers, and allows them to easily deploy models and monitor infrastructure. SageMaker Studio is primarily designed for data scientists to develop models and is lacking the same infrastructure and deployment support.

SageMaker Notebook Code for Batch Visualization of Performance

SageMaker Monitoring Script

6. Snapshot View

A360 AI also allows users to collaborate with their team and makes it easy for the team to get a snapshot view of different stages of the ML workflow. The snapshot view provides a model overview, model artifacts, and model logs, all in one place.

Snapshot View in A360 AI

7. Ease of Use

SageMaker Studio requires going through a certain procedure of communication with S3 in order to load data, update data, and deploy a model. This also requires activation of boto3. Although clear documentation with use cases have been provided for SageMaker, a new user could be intimidated by the number of new procedures used for model workflows and the number of services provided by Amazon.

SageMaker Dashboard

SageMaker Dashboard

A360 AI has a seamless procedure of building, logging, and deploying a model. A360 AI does not require one to have extensive knowledge about AWS and makes a data scientist’s job much easier. Also, A30 AI’s documentation is fairly organized and does not require extensive amounts of modifications in terms of deploying the model. The welcome dashboard in A360 AI also shows all of the tasks a user can perform in a intuitive and simple layout. In the image below, the dashboard menu mirrors the sidebar panes under the Data Science console, with Projects, Notebook Servers (Workspaces), Models, Data, and Monitoring.

A360 AI Main Dashboard

A360 AI outperforms Amazon SageMaker Studio in terms of usability, model development, and deployment. The entire A360 AI platform is streamlined, simpler than SageMaker, and more intuitive.

8. Workflow Time Spent

Hours Spent on Example Workflow in Each Platform

Lines of Code Required for Example Workflow

Overall Performance

One Comment

  • Great Summarization of AWS Sagemaker and Core differentiators are very clear

    1. Data Scientists Productivity – Speed to secure enterprise deployment just not on AWS IaaS platform
    2. Management of AWS Services like S3 buckets independent of SageMaker or any other build tool – Clients can own their own AWS account
    3. AWS does not manage artifacts and version control them as part of enterprise CI/CD tool chain – makes it transparent and real time enabling continuous and advanced machine learning

    Aman, keep up the good work.

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