I know your inbox is already flooded with AI and ML newsletters, and it occupies 90% of every conference you attend, but beneath all the noise there’s real strategic value available to those that can use it correctly. However, despite how people may describe it, machine learning isn’t magic. It’s a tool that requires a level of infrastructure, expertise, and resources. This is where Amazon SageMaker AI comes in, offering a pathway to ML implementation that won’t require you to sacrifice your budget, timeline, or sanity.
SageMaker AI is Amazon’s ML platform that streamlines the development process from data preparation to deployment. For CTOs looking to accelerate AI adoption, SageMaker removes the technical barriers that have historically made AI/ML initiatives costly, time-consuming, and painful by limiting the infrastructure headaches and providing an environment to experiment and build AI solutions. For example, according to AWS, the total cost of ownership (TCO) for Amazon SageMaker for a medium-sized business is 85% lower than running on EC2 or EKS, making it not only faster to develop AI solutions but also more economical.
I also want to emphasise that AI is more than just chatbots and picture generation. There are numerous other tasks such as error detection, demand forecasting, or customer prediction with clear, measurable ROI that your organisation will benefit from. This is where Amazon SageMaker AI shines.
Reducing complexity and time-to-value
SageMaker AI allows you to be as hands-on or as hands-off as you like. This lets you run proof-of-concept projects quickly and cheaply, but with the ability to go deeper for your production features.
If you just want to bring your data, SageMaker Canvas will choose the best model for the data you’ve provided, covering cases for numerical, textual, or image data.
If you want to be a bit more hands-on, but still with some foundations to build on, SageMaker provides a suite of built-in algorithms and pre-trained models to help you get into the interesting work quickly. Whether you want to predict future behaviour based on past data, detect anomalous data, classify images, or summarise text, you can find a built-in algorithm offered by SageMaker immediately to deploy your first model with the confidence that you have the right algorithm for the job. You can then use the Amazon SageMaker SDK to build on these.
Finally, if you want full control of the model and training (but none of the infrastructure), SageMaker still supports you with custom containers.
You can then tune your model to perfection using automatic model tuning, rather than spending hours manually experimenting with different parameters yourself, and as you’d expect, all of this is happening on AWS’ managed infrastructure, so no time spent performing security patching of compute resources and no huge up-front costs or commitments to computation.
Controlling running costs
So - you’ve put together your first ML feature. How do we make sure it isn’t going to cost a fortune? Fortunately, SageMaker AI has some important features to help here.
Training
Training your models can be the most expensive part, but with managed spot training, by using spare EC2 capacity in the AWS cloud, you can cut costs by up to 90%. It can be as simple as changing a value to “true” to enable, and if you can use checkpoints to let your training jobs pick up where they left off, the impact of spot interruptions can be low.
Inference
No more running a huge ML instance to serve requests that come in a few times a day. With serverless endpoints, you can test out and build your first AI products without explaining to finance why your dev environment cost more than production last month.
Once that’s up and running, you can minimise the costs of production with the inference recommender, which helps you right-size your provisioned instance instead of the classic “we’ll go with a bigger one just to be safe”, by benchmarking various deployment configurations to identify the optimal price-performance ratio for your specific model.
Conclusion
Amazon SageMaker provides the practical foundation needed to get machine learning into production. It might just be the platform that helps you deliver on those AI goals without sacrificing your budget or your team’s sanity.
Ready to start building your AI/ML supported applications on AWS? Book a free chat to find out how we can help.
This blog is written exclusively by The Scale Factory team. We do not accept external contributions.
