AI and Machine Learning are key areas of investment, growth and differentiation for many companies. In recent years cloud technologies and managed services made building solutions using AI/ML affordable and easy to experiment with, propelling its fast adoption and lowering the barrier to engineers and data enthusiasts.
In this session, tailored to engineers and technical folks without prior data science knowledge, we will learn the fundamentals about AI and Machine Learning in AWS. We will have an overview of algorithms and the concepts of data collection and preparation. We will explore different AWS managed services for data science with different levels of abstraction, such as AWS SageMaker - the AI/ML Platform - but also the pre-trained AI/ML model APIs with ready-made capabilities such as AWS Transcribe (Speech-to-Text), Rekognition ( Image & Video Classification) and Comprehend ( Sentiment Analysis ).
|What can AWS tell us about Fake and Credible News?||AWS Community Summit UK 2019||October 2019|
|Deployment automation for an AWS Serverless project||AWS Community Day Germany 2019||September 2019|
|What can AWS tell us about fake and credible news media websites?||AWS Community Day Germany 2019||September 2019|
|How can you sell Security as a business priority?||Sales Engineering Finland Meetup||September 2019|
|The Hitchhiker’s Guide to the Cloud (AWS vs GCP vs Azure) and their AI/ML API’s capabilities||NDC Oslo||June 2019|
|What can Google Cloud AI-ML APIs tell us about news media websites?||ServerlessDays Helsinki 2019||April 2019|
|How establishing a Threat Modelling process can help you transition from DevOps to DevSecOps||DevOpsDays Copenhagen 2019||April 2019|
|Deployment automation for an AWS Serverless project: SAM vs CloudFormation vs Terraform||AWS Community Summit UK 2019||April 2019|
|A Walkthrough of public Cloud vendors (AWS vs GCP vs Azure) and their AI Capabilities||AI Helsinki March 2019||March 2019|