A data-driven approach enables businesses to make informed decisions based on accurate predictions and forecasts, improving operational efficiency and resource optimization. Machine learning (ML) systems have the incredible ability to continuously learn and adapt, improving their performance over time as they are exposed to more data. This self-learning capability helps organizations stay ahead of the curve, dynamically responding to changing market conditions and customer preferences, and ultimately driving innovation to improve their competitive advantage.
By leveraging the power of machine learning on AWS, businesses can realize benefits that increase efficiency, improve decision-making, and drive growth.
In this session, we discuss how organizations with limited resources (budget, skills gaps, time) can jump-start their data-driven initiatives with advanced analytics and ML capabilities. Learn AWS Working Backwards best practices for driving data-related projects that address tangible business value. We then dive deep into AWS analytics and AI/ML capabilities that simplify and accelerate data pipeline delivery and derive business value from ML workloads. We discuss low-code, no-code (LCNC) AWS services within the context of a complete data pipeline architecture.
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Figure 1. View the architecture for analyzing customer churn using AWS services
As artificial intelligence (AI) continues to revolutionize industries, the ability to operationalize and scale ML models has become a key challenge. This session introduces the concept of MLOps, a discipline that builds on and extends the DevOps practices widely adopted in software development. By applying MLOps principles, organizations can streamline the process of building, training, and deploying ML models, enabling efficient and reliable model lifecycle management. Mastering MLOps helps organizations bridge the gap between AI development and operations to realize the full potential of their ML initiatives.
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Figure 2. MLOps maturity levels can help you assess your organization and understand how to get to the next level.
To power generative AI applications while controlling costs, AWS designs and builds machine learning accelerators such as AWS Trainium and AWS Inferentia. In this session, we introduce ML hardware built specifically for model training and inference, and show how Amazon and AWS customers can leverage these solutions to optimize costs and reduce latency.
You can learn from real-world examples that demonstrate the impact of these solutions and explanations of how the chips work. ML accelerators are not only useful for generative AI workloads, but can also be applied to other use cases such as representation learning, recommendation systems, and any scenario using deep neural network models.
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Figure 3. Details of the technologies supporting AI services
How customers are implementing machine learning on AWS
The following resources provide more information about the ML infrastructure used to train models at scale at Pinterest and the experimentation framework built by Booking.com.
In their video, Pinterest discusses strategies for creating an ML development environment, orchestrating training jobs, ingesting data into the training loop, and accelerating training speed. You also learn about the benefits of containers in the context of ML and how Pinterest decided to set up the entire ML lifecycle, including distributed model training.
The second resource explains how Booking.com leveraged Amazon SageMaker for data analysis, model training, and online experimentation to accelerate the experimentation process, resulting in reduced development time for ranking models and increased the velocity of their data science team.
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Go to Booking.com blog post
Figure 4. See how Pinterest uses AWS services for machine learning workloads
Amazon SageMaker Immersion Day helps customers and partners to gain an end-to-end understanding of building ML use cases. This workshop will focus on training, tuning, and deploying ML models in production-like scenarios, from feature engineering to understanding various built-in algorithms, and teach you how to bring your own models to perform lift-and-shift from on-premise to the Amazon SageMaker platform. Additionally, we will cover more advanced concepts like model debugging, model monitoring, and AutoML.
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Figure 5. Using Amazon SageMaker to train, tune, and deploy workloads
See you next time!
Thanks for reading. This post introduced you to the possibilities of using AWS machine learning services. In the next blog, we will talk about cloud migration.
To revisit previous posts or view the entire series, please visit our Let's Architect! page.
