Let’s Architect! Learn About Machine Learning on AWS
Machine learning (ML) systems have the remarkable ability to continuously learn and adapt, improving their performance over time as they are exposed to more data… This self-learning capability ensures that organizations can stay ahead of the curve, responding dynamically to changing market condit…
A data-driven approach empowers businesses to make informed decisions based on accurate predictions and forecasts, leading to improved operational efficiency and resource optimization. Machine learning (ML) systems have the remarkable ability to continuously learn and adapt, improving their performance over time as they are exposed to more data. This self-learning capability ensures that organizations can stay ahead of the curve, responding dynamically to changing market conditions and customer preferences, ultimately driving innovation and enhancing competitiveness.
By leveraging the power of machine learning on AWS, businesses can unlock benefits that enhance efficiency, improve decision-making, and foster growth.
AWS re:Invent 2023 – Zero to machine learning: Jump-start your data-driven journey
In this session, see how organizations with constrained resources (budgets, skill gaps, time) can jump start their data-driven journey with advanced analytics and ML capabilities. Learn AWS Working Backwards best practices to drive forward data-related projects that address tangible business value. Then dive into AWS analytics and AI/ML capabilities that simplify and expedite data pipeline delivery and business value from ML workloads. Hear about low-code no-code (LCNC) AWS services within the context of a complete data pipeline architecture.
Figure 1. See an architecture to analyze customer churn using AWS services
Introduction to MLOps engineering on AWS
As artificial intelligence (AI) continues to revolutionize industries, the ability to operationalize and scale ML models has become a critical challenge. This session introduces the concept of MLOps, a discipline that builds upon and extends the widely adopted DevOps practices prevalent in software development. By applying MLOps principles, organizations can streamline the process of building, training, and deploying ML models, ensuring efficient and reliable model lifecycle management. By mastering MLOps, organizations can bridge the gap between AI development and operations, enabling them to unlock the full potential of their ML initiatives.
Figure 2. MLOps maturity level will help to assess your organization and understand how to reach the next level.
Behind-the-scenes look at generative AI infrastructure at Amazon
To power generative AI applications while keeping costs under control, AWS designs and builds machine learning accelerators like AWS Trainium and AWS Inferentia. This session introduces purpose-built ML hardware for model training and inference, and shows how Amazon and AWS customers take advantage of those solutions to optimize costs and reduce latency.
You can learn from practical examples showing the impact of those solutions and explanations about how these chips work. ML accelerators are not only beneficial for generative AI workloads; they can also be applied to other use cases, including representation learning, recommender systems, or any scenario with deep neural network models.
Figure 3. Discover the technology that powers our AI services
How our customers are implementing machine learning on AWS
The following resources drill down into the ML infrastructure that’s used to train large models at Pinterest and the experimentation framework built by Booking.com.
The Pinterest video discusses the strategy to create an ML development environment, orchestrate training jobs, ingest data into the training loop, and accelerate the training speed. You can also learn about the advantages derived from containers in the context of ML and how Pinterest decided to set up the entire ML lifecycle, including distributed model training.
The second resource covers how Booking.com accelerated the experimentation process by leveraging Amazon SageMaker for data analysis, model training, and online experimentation. This resulted in shorter development times for their ranking models and increased speed for the data science teams.
Take me to Booking.com blog post
Figure 4. Let’s discover how Pinterest is using AWS services for machine learning workloads
SageMaker Immersion Day
Amazon SageMaker Immersion Day helps customers and partners provide end-to-end understanding of building ML use cases. From feature engineering to understanding various built-in algorithms, with a focus on training, tuning, and deploying the ML model in a production-like scenario, this workshop guides you to bring your own model to perform lift-and-shift from on-premises to the Amazon SageMaker platform. It further demonstrates more advanced concepts like model debugging, model monitoring, and AutoML.
Figure 5. Train, tune and deploy your workload using Amazon SageMaker
See you next time!
Thanks for reading! With this post, introduced you to the art of possibility on using AWS machine learning services. In the next blog, we will talk about cloud migrations.
To revisit any of our previous posts or explore the entire series, visit the Let’s Architect! page.
Author: Luca Mezzalira