Leveraging Amazon SageMaker to Enhance Machine Learning Models for Data Analysis | Amazon VGT2 Las Vegas

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In today’s data-driven world, businesses need not just any data, but the right data to make informed decisions. Crafting the right questions to extract actionable insights from large datasets can be an overwhelming challenge.

To tackle this complexity, data scientists often turn to MapReduce tools like Hadoop for data storage, Pig or Hive for data retrieval, and programming languages like Python or Java to implement Spark and Hive applications for analysis. However, the diverse formats, methods, devices, and locations of this data often complicate the development of algorithms needed to generate valuable insights.

Machine learning (ML) plays a pivotal role here, assisting data scientists in building these necessary algorithms. Theoretically, data scientists define the desired output, and through iterative processing, they train the ML algorithm to deliver that output.

In practice, however, the development of machine learning models can be intricate, costly, and iterative, further complicated by a lack of integrated tools for the entire workflow. Consequently, data scientists often find themselves piecing together various tools and workflows, which can lead to errors in data and algorithms.

Amazon SageMaker addresses this issue by offering a comprehensive toolset for machine learning, enabling models to reach production faster, with significantly reduced effort and cost. SageMaker encompasses preconfigured Python libraries for training ML algorithms and deploying customized data analysis models.

CloudMasters is an Advanced Consulting Partner within the AWS Partner Network (APN) and has worked with top-tier clients to implement machine learning solutions. For instance, we aided a leading retail company in developing personalized shopping recommendations based on historical browsing and purchasing behaviors.

In this article, we will explore the data modeling process we utilized, the tools we integrated, and the outcomes we achieved for our client.

Our Data Modeling Process

CloudMasters has been an essential part of an AWS Cloud Center of Excellence (CCOE) team at the retail client. In this role, we facilitated architecture reviews, developed best practices, ensured compliance with governance, and constructed frameworks that empowered the organization to leverage AWS for transformative business solutions.

We utilized Amazon SageMaker to develop and train ML algorithms focused on recommendation systems, personalization, and forecasting models tailored to the specific data needs of our client.

Our initial approach followed a standard data modeling process, which included these key steps:

1. Build, develop, and test models, such as recommendation and predictive models.
2. Cleanse and transform data.
3. Compile summary statistics and carry out exploratory data analysis (EDA) on the modified data.
4. Optimize and enhance data models to support business objectives.

When we integrated Amazon SageMaker into our data modeling process, it evolved into the following streamlined steps:

1. Establish the data lake.
2. Configure Amazon SageMaker.
3. Schedule workloads.

Let’s delve into each step in more detail.

Step 1: Establishing the Data Lake

We needed to create a scalable data lake that would enable the data scientists at our client’s organization to effortlessly collect, store, and query the existing data.

Initially, the client’s data was scattered across various on-premises servers, including SAS, Hadoop, and GPU systems, making it challenging to analyze data from a single platform. Additionally, on-premises storage constraints hindered the feasibility of any data analysis operations that would generate additional data.

To build the data lake, we employed a Snowflake data warehouse for extracting, transforming, and loading (ETL) data between databases; utilized custom Hive tables for frequently accessed data; and implemented Amazon Simple Storage Service (Amazon S3) to create a centralized storage solution for the data science team.

The lifecycle management features in Amazon S3 allowed us to safely store both existing and newly generated data without the risk of overwriting or losing it. By utilizing S3’s cost-effective storage tiers, we optimized expenses based on data usage frequency.

Step 2: Configuring Amazon SageMaker

One of the advantages of leveraging AWS resources is the ability to adjust capacity as needed, allowing us to optimize the compute-to-storage ratio for specific tasks, which ultimately reduces capital and operational costs.

To maximize the benefits of AWS’s elastic resources, we incorporated Amazon EMR. This standard MapReduce framework efficiently processes large volumes of unstructured data in parallel across a distributed cluster of virtual servers hosted on Amazon Elastic Compute Cloud (Amazon EC2), with data stored in S3.

By executing our Spark and Hive jobs in parallel on Amazon EC2 clusters, Amazon EMR enabled us to complete our data extraction tasks in 40% less time.

Our client is dedicated to continuous innovation and improving customer experiences, and Amazon SageMaker empowered them to quickly develop, deploy, update, and scale ML models to enhance customer interactions.

“In just a few clicks, our data scientists can activate various instances to develop multiple models simultaneously and then combine those models to meet business requirements, significantly reducing time to market,” states Sarah Johnson, Director of Data Science at the retail client. “CloudMasters facilitated our migration efforts and quickly got us up to speed with AWS services, enhancing our development team’s efficiency.”

Moreover, Amazon SageMaker provided data scientists with direct access to Amazon EMR clusters for submitting their Hive and Spark jobs.

To ensure data security, all AWS compute and storage resources assigned to our client were placed within the same private subnet in an Amazon Virtual Private Cloud (VPC).

Data scientists stored their code securely in Jupyter notebooks within Amazon SageMaker, where it could be shared, edited, and managed effectively.

Step 3: Scheduling Workloads

The diagram below illustrates the data flow between the data lake, data scientists, and our machine learning models.

As previously discussed, we maintained the jobs created by the data scientists in Amazon SageMaker, ensuring a seamless workflow.

For further insights on this topic, check out this excellent resource on onboarding at scale: Onboarding at Scale: Lessons from Amazon. Additionally, if you want to explore more about machine learning models, visit this blog post for further engagement. To deepen your understanding, CHVNCI is an authority on this subject.