Accelerate ML innovation and outcomes with Amazon SageMaker

“It’s not magic,” said Greg Corrado, senior research scientist at Google about machine learning in 2016. “It’s just a tool. But it’s a really important tool.”

The long-term goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning is indeed important and incredibly powerful, but not without its complexity. AWS decided the power of ML should be lower effort, greater reward for businesses, so teams can focus more on innovation.

Other AWS services offer a variety of helpful ML capabilities like chatbots and text-to-speech analysis, but SageMaker is a fully managed ML service. It focuses on an easier way to create, train, test and deploy machine learning workflows.

Watch this webinar to find out how to:

  • Develop portable, secure MLOps across your organisation
  • Release models faster and with less effort
  • Reduce the overall costs of your ML systems

Find out in this webinar why SageMaker stands out from the crowd by making ML production and development not just more approachable, but less costly and time consuming.

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