At Credera, we partner with AWS in a multitude of different ways; either working together to deliver meaningful outcomes for our clients, teaming up to deliver an Immersion Day event, or planning our next industry-focused go-to-market.
When AWS came asked us to present at the AWS Deep Dive Days 'Unlock the Potential of AI & ML' in April, we welcomed the opportunity with open arms. We saw this event as the perfect opportunity to learn more about the latest developments of the AI/ML tools that are available today and to showcase Credera's real-time customer insights solution: Marketing Analytics Platform.
In this blog, we cover our experience of attending the event, including some of the main highlights, a brief summary of what we presented during the partner tracks, and a summary on some of the sessions we attended over the two-day period. Read next: Credera achieves AWS Premier Tier Services Partner status
What is the Marketing Analytics Platform?
Credera’s Marketing Analytics Platform addresses the problem of the increasingly limited possibilities of harnessing borrowed data due to data privacy regulations. Organisations gain the ability to own the analysis of their customers’ data by using the Marketing Analytics Platform, yielding immediate business benefits.
We presented this solution to a mixed audience, mostly made up of solution architects and those who have a particular focus on artificial intelligence and machine learning (AI and ML) as a specialisation.
Time to value can be accelerated by adopting a solution like MAP. Using an AWS-approved solution means you avoid the investment of time and resources needed to create something in-house from the ground up. This method can quickly help customers see real results, with a significant reduction in time and resources from the first moment a need is identified.
Inference patterns on AWS SageMaker
On the second day of the AWS Deep Dive Days event, we attended a session focusing on Inference patterns on AWS SageMaker.
Many organisations have started to optimise their ML workload deployments using MLOps mechanisms. Amazon SageMaker provides a powerful and flexible platform for building and deploying Machine Learning (ML) and Deep Learning (DL) models. This session offered a unique opportunity for us to learn how this works in the real world and how Amazon enabled the automation and reusability of ML solutions across several different business workloads from AWS experts.
During the session, we learned about best practices for deploying models using SageMaker inference, the multiple hosting options on deploying models and monitoring deployed models for biases, and deviations using a demo walk through. The demo deep dived on the process to build and train models with Amazon SageMaker and how to package, deploy, and manage them in a real-life scenario.
Using AI to automate content moderation and compliance
On day two of the event, we attended a session on automated content moderation and compliance using Amazon SageMaker, Amazon Rekognition, and Amazon Textract. As a result of the growing pool of information available to users and the growing data adoption of today (including social media), human-based content moderation alone cannot scale to meet safety, regulatory, and operational needs. This in turn leads to a poor user experience, high moderation costs, and brand risk.
During this session, we learned how AWS can be used to overcome content moderation using AWS services. AWS carried out a live implementation using Amazon Content moderation API, which identifies ten major topics and 35 subtopics.
In the demo that followed, we were shown how Amazon recognition and text rate the content (whether it is a picture, live video, or text) to moderate based on the likeness of the scores given by the models.
Machine Learning & Natural Language processing in Alexa
Another key highlight from day two of the event was a keynote session delivered on Amazon Alexa. Alexa is constantly learning from human data, with data and machine learning being the foundation of Alexa’s power.
In this session, we learned about the challenges of natural language generation and processing, and how recent advances in technology have seen the virtual assistant technology continue to grow in intelligence. Human language is complex, but today’s natural language generation capabilities are becoming increasingly more sophisticated and are changing how AI models think before responding to the queries.
This session reiterated the need for explainability of a model and showed why responsible AI models are key whilst we continue to grow in this space.
Closing thoughts
The agenda for the AWS Deep Dive Days event included a generous spread of interesting topics over the two-day period - each backed up with exceptional speakers. If you’re an advanced practitioner or have a general interest in the science of ML, this event provides a technical deep dive into the groundbreaking work that ML scientists within AWS and beyond are doing to advance the science of machine learning in areas such as natural language processing, bias, and more.
How we can help
As a Premier Consulting Partner, we strive to make an extraordinary impact for our clients by solving their business challenges whilst harnessing the power of AWS services to simplify, modernise, and scale solutions at pace. We have first-class expertise in AI/ML, MLOps, Data Analytics, and DevOps, and using our extensive industry knowledge, we aim to help our customers in Financial Services, Public Sector, and Energy, to continue to gain a competitive advantage.
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