AWS provides a set of flexible services designed to enable companies to more rapidly and reliably build and deliver products using DevOps practices. This session addresses the challenges of productionising ML models, and introduces DevOps for a machine learning - a set of practices that combines ML process and DevOps practices. Join us to hear the best practices of leveraging Amazon SageMaker Pipelines and AWS CI/CD tools to automate ML processes.
37:36As Generative AI revolutionises the global landscape, the focus shifts towards the essential skills required to harness this emerging potential. In this session, AWS AI/ML Specialist Developer Advo...
32:09Learn about the 6 key trends driving Machine Learning Innovation across Australian and New Zealand Industries.....
1:15:11AWS Tech Leaders Assembly connects technical executives in an open forum with Australia's leading technology businesses sharing their knowledge and learning with interactive networking opportunities.
57:16Artificial intelligence (AI) applied through machine learning (ML) will be one of the most transformational technologies of our generation, tackling some of humanity’s most challenging problems, ...
57:27With Transcribe Call Analytics, you can get valuable intelligence such as customer and agent sentiment, call drivers, and conversation characteristics such as non-talk time, interruptions, loudness...
1:05:41Watch this webinar where our subject matter experts, and customer TEG, will discuss how businesses can use machine learning to gain insights on consumer buying and browsing behaviour, increasing...
58:28In this session, we will cover various Amazon SageMaker features with focus on its deployment capabilities. Amazon SageMaker provides multiple features to manage resources and optimise inference...
55:24Organisations are looking to leverage the broad set of in-house analytics skills to help scale the adoption of machine learning to drive business outcomes. SageMaker Canvas provides a visual point...
58:43Understanding and extracting data from non-structured data to accelerate back office processing is a major challenge for organisations across industry. Artificial Intelligence (AI) can automate docu..
58:52Machine Learning Operationalisation (MLOps) is a collection of best practices to efficiently operationalise models. It’s not just technology—it’s about processes and people, thinking about the...
1:29:47Kick-start your ML journey by going hands-on with AWS DeepRacer. Presenter: Jacob Cantwell, Solution Architect, AWS
38:24In this session, participants will be introduced to concepts and terminologies of machine learning, and hear how businesses can use AWS AI services to innovate faster.
1:00:17Join our keynote to hear about advancements in the field of software development, the evolution of tools and practices such as CI/CD, and how machine learning is making software development...
38:18Find out how to kick start your AWS training journey with practical advice, resources, and Certification pathway information to be shared by our AWS experts.
20:27Join us for a fireside chat with Melanie Botha, Head of Training and Certification ANZ, at Amazon Web Services, and Brooke Jamieson, AWS Community Builder and Head of Enablement AI/ML and Data at...
54:50In this session, we share how to leverage Amazon SageMaker platform to tackle the challenges of building a ML pipeline, from data labelling, feature engineering, model training, tuning, to deployment.
56:40Cloud technology and machine learning are driving forces behind technological disruption affecting every organisation, making it critical to businesses’ efforts to transform and innovate at speed.
54:50As people around the world start to return to their offices, employers have significant responsibilities to prepare and deliver a safe working environment.
55:30In this webinar you’ll be introduced to Amazon Kendra and provided with insights into how it can enhance employee productivity, accelerate projects and drive opportunities.
