Build AI-powered applications without any machine learning expertise (Level 100)

Ever wonder how data scientists at digital freight marketplace Convoy are able to find the most cost-efficient trucking routes or how healthcare Startup SyntheticGestalt is taking the drug discovery process from 4 years down to 9 hours to accelerate scientific research? They were early adopters of Amazon SageMaker, Amazon’s machine learning service that lets you quickly build, train, and deploy ML models. In this session, learn best practices and the top 10 things Startups need to accelerate their AI/ML journey.

Allie K. Miller, Global Head of Machine Learning BD, Startups & VC, AWS

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Build AI-powered applications without any machine learning expertise (Level 100)

6 steps to success with generative AI

Maximize the business value of generative AI

7 leading generative AI use cases

The top 5 generative AI questions on every executive’s mind

Answering your 4 biggest questions about generative AI security

Putting Responsible AI into Practice

Achieving transformative business results with artificial intelligence

The 3 keys to successful AI and ML outcomes

Understanding the costs of generative AI

Drive business growth with personalization

Automate document data extraction & analysis

Improve customer experience by adding AI to your contact center

4 ways to revolutionize marketing and sales with generative AI

Why your business needs ML-powered search

Optimize your digital value chain with machine learning

Artificial Intelligence for small and medium business

Predicting energy consumption for cost savings with Amazon Forecast

Building an audio classifier with Amazon SageMaker

Detect social media fake news with graph machine learning

Build a real time air quality anomaly detector using AWS Lookout For Metrics