AWS Machine Learning Migration

Amazon Web Services (AWS) offers a variety of machine learning products and services to enable developers and businesses to incorporate machine learning capabilities into their applications and workflows. Here are some notable AWS machine learning services:

  1. Amazon SageMaker
  2. Amazon Rekognition
  3. Amazon Comprehend

Want to learn more about what AWS has to offer in the machine learning space? Check out our comprehensive overview of all the AWS machine learning services.

AWS Machine Learning Migration Steps

Before you can get started on building a machine learning workload, you need to start with getting your data into the cloud.

As an AWS consultancy with years of experience under our belts, we offer a no cost, no obligation AWS Machine Learning Migration Assessment designed to help businesses looking to make the jump to ML, but who still need migrate their data to the cloud.

AWS Machine learning migration planning board

When planning a data migration to AWS with the goal of using AWS SageMaker, there are several important elements to consider. Here are the key aspects to keep in mind:

  1. Data Assessment - Start by assessing your existing data to understand its format, structure, quality, and volume. Determine if the data is suitable for use with SageMaker, as well as any necessary preprocessing or transformations that might be required.
  2. Data Storage - Identify the appropriate AWS storage service to store your data. Depending on the size and nature of your data, you can choose options like Amazon S3 (Simple Storage Service), Amazon EFS (Elastic File System), or Amazon EBS (Elastic Block Store).
  3. Data Transfer - Plan how you will transfer your data to AWS. You can use AWS DataSync, AWS Transfer Family, or other data transfer methods based on your specific requirements. Consider the time, network bandwidth, and cost implications of the transfer.
  4. Data Security - Ensure that your data remains secure during the migration process. Follow AWS best practices for data encryption, access control, and compliance. Consider using AWS Key Management Service (KMS) for encryption.
  5. Data Preparation - Prepare your data for training with SageMaker. This may involve data cleaning, feature engineering, normalization, and splitting your data into training, validation, and testing sets.
  6. Training & Model Development - Utilize SageMaker's capabilities to train and develop machine learning models. Leverage SageMaker's built-in algorithms or bring your own custom algorithms. Consider using SageMaker Notebooks for collaborative model development.
  7. Monitoring & Management - Once your model is trained, deploy it using SageMaker endpoints. This allows you to create a scalable and reliable inference endpoint to serve predictions.
  8. Cost Optimization - Implement monitoring and management practices to track the performance and health of your SageMaker models. Utilize Amazon CloudWatch and SageMaker's monitoring capabilities to detect issues and optimize model performance. This is a key step often missed by organizations first deploying machine learning.
  9. Documentation & Collaboration - Document the migration process, configurations, and workflows. Foster collaboration among your team members by utilizing AWS tools like AWS Identity and Access Management (IAM) for access control and AWS Service Catalog for sharing resources.

AWS Machine Learning Migration Consultation

Phew! That's a lot! This comprehensive list can be used by your team internally, but when first engaging bringing in an experienced AWS machine learning consulting partner like Metal Toad is part of best practices specific to AWS SageMaker (or one of the other 33+ AWS machine learning services) to ensure a smooth and successful data migration process. Here's why:

  • 85% of companies who responded to a DataRobot survey say the are struggling with IT governance, compliance and auditability requirements related to their AI/ML deployments.
  • 25%—the largest percentage for any single challenge—named IT security their top AI/ML challenge.
  • 87% of survey respondents struggling with long model deployment timelines.

As an added bonus, after completion of an AWS Machine learning migration consultation with Metal Toad, 90% of our customers qualify for AWS Proof of Concept funding, which can be up to $25K!

Have questions? Contact us today or send us an email to learn more about AWS machine learning migration.

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