Build and Deploy a Machine Learning Model for your Application with AWS SageMaker

Artificial Intelligence has indeed come into its own these last couple of years, with advanced applications like ChatGPT finding widespread acceptance by the general public. While AI has many applications across industries, it is Machine Learning in which a model is trained on data to make intelligent decisions that have been the most common use case.

The Machine Learning (ML) lifecycle generally involves selecting/deploying a model, training it, and testing/refining it until it reaches a decision-making ability of minimum error. This can be a time-consuming and expensive process to do with on-prem or private cloud architecture, which is where managed services like AWS SageMaker comes into play.

AWS SageMaker

ML Models generally require an infrastructure that is capable of potent processing and storage, which can be a drain on a company’s resources unless they look at other options like SageMaker.

SageMaker is a fully managed service by Amazon Web Services that eliminates much of the manual work involved in building, training and deploying machine learning models.

From AWS Website.

With SageMaker, AWS is responsible for provisioning the infrastructure, while data scientists can focus on the operational aspects of making the ML model as accurate and production-ready as possible. It also provides ready-made and optimized algorithms that can be run on massive amounts of data to generate results. This dramatically reduces the market-ready time for ML models and enables businesses to gain an advantage over their competitors.

Some of the advantages that AWS SageMaker brings are:

  • Widespread support for various ML algorithms like linear regression, XGBoost, etc., and support for customized ones that can be uploaded to the service. This gives businesses a lot of flexibility in choosing algorithms suited to their business cases.
  • Speed of deployment in which ML models can quickly launch onto a production endpoint once tested.
  • Built-in tools for testing and fine-tuning the performance and accuracy of models that can be used to make the models as accurate as possible and track their accuracy over time.
  • Native integration with AWS data sources like S3 and DynamoDB, which is also fully managed and with the full power of the AWS infrastructure behind them.

At the same, companies that adopt SageMaker should keep the following points in mind:

  • Security in AWS is built on a shared responsibility model. While AWS will secure the underlying infrastructure, it is up to the customer to ensure their ML environment is secured as per best practices. They should also understand any regulatory requirements for data residency before uploading data to the cloud.
  • Like all managed services, costs can compound over time unless monitored, especially for large or multiple models used for an extended period. Customers should fully understand the costing model of AWS SageMaker.
  • SageMaker might lack some of the features for data visualization that other machine learning tools in the data science market might contain. Customers should understand their use cases and map them to SageMaker features before deciding to move ahead.
  • As with all managed services, there is a learning curve to SageMaker that should be considered. Customers should ensure that they have the necessary technical expertise in AWS before they invest heavily in it.
  • SageMaker is an AWS service, and customers with a multi-cloud environment should also take that into account otherwise, they might find themselves with a cloud vendor lock-in that might prove very costly in the long run.

The future of Machine Learning is the Cloud

As the future move towards widespread AI adoption in all industries, companies will move toward a more managed services model to ensure faster and more efficient deployment of machine learning models. Choosing AWS SageMaker as a service means AWS will handle the underlying infrastructure. At the same time, companies can focus on the more exciting side of deploying models and making them as accurate as possible.

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