The AdTech industry moves quickly. Competition is tough, and companies must manage massive amounts of data in real time to remain competitive. Every choice, whether about ad placement, user targeting, or bid modifications, must be made fast.
An AWS report indicates that organizations utilizing SageMaker experience up to a 50% decrease in the time their data teams need to access analytics and AI. This shift allows for greater attention toward innovative initiatives and strategic projects.
AWS SageMaker shines in this demanding environment. It benefits AdTech organizations by automating machine learning procedures, cutting infrastructure costs, and improving ad performance.
How can SageMaker achieve these results, and why is it important for the future of AdTech?
AdTech platforms routinely manage millions of impressions every second, thus making instant decision-making an absolute necessity. Conventional strategies depend on pre-built models that require periodic updating, often resulting in outdated understandings and lost revenue. Through real-time inference, AWS SageMaker enables advertisers and demand-side platforms (DSPs) to analyze user interactions immediately, displaying the most effective advertisement at the most opportune moment.
For example, a DSP utilizing SageMaker can dynamically adjust bids based on user behavior, increasing click-through rates (CTR) and return on ad spend (ROAS) while reducing wasted impressions.
AWS SageMaker has been engineered to adapt to evolving demands, allowing teams to develop and implement models without being constrained by storage limits or computing capacity. Its seamless integration with Amazon S3, AWS Glue, and other services ensures efficient data management.
To understand how to effectively harness data and transform ad operations from a cost liability into a source of income, check out our article “Using AdTech Efficiently.” This, alongside SageMaker’s distributed training, enables model training on terabytes of advertising performance data without the requirement of expensive on-site infrastructure.
Building and running ML infrastructure can be expensive. You typically need powerful GPUs, extensive storage, and substantial networking. AWS SageMaker lowers these barriers by offering a managed environment for training, deployment, and monitoring of models.
Its Spot Training feature can trim training expenses by as much as 90% by tapping into unused AWS compute resources. This puts smaller AdTech providers on more equal footing with larger competitors. For guidance on securing these workloads, refer to Kanda’s AWS Secure Workloads Guide for best practices in compliant ML deployments.
Illicit activities, such as artificial clicks, bot traffic, and ad spoofing, cost advertisers billions of dollars every year. AWS SageMaker assists AdTech businesses in setting up real-time fraud detection systems that can identify suspicious behavior before it impacts advertising budgets.
For example, an anomaly detection model based on SageMaker can discern traffic patterns indicative of automated bot-driven clicks, allowing teams to significantly decrease fraudulent advertising expenses.
Successful AdTech companies use machine learning to personalize ad experiences. SageMaker’s AutoML capabilities helps businesses refine their lookalike audience models, improving relevance and engagement. Meanwhile, reinforcement learning has proven effective for optimizing marketing campaigns by analyzing past ad interactions.
AutoML process managed by Autopilot
Source: AWS
Determine the main problems you want to solve—whether it’s optimizing bids, refining audience segmentation, or tackling fraud. Many industries face similar issues, as shown in The Future of FinTech and Banking with AI, ML, and Blockchain, which demonstrates how ML approaches can be adapted across sectors.
AWS SageMaker offers a suite of services for different ML requirements:
After training, make sure models continue to perform effectively. SageMaker Model Monitor provides ongoing performance reviews and can trigger automatic retraining if accuracy declines.
Source: AWS
To keep ML models updated, organizations can automate retraining and deployment with SageMaker Pipelines. This ensures models stay current with changing consumer behaviors without constant manual intervention.
If you’re unsure how to move forward, Kanda’s AI and Machine Learning Services offer a complete solution—from data engineering to large-scale ML implementations.
FreeWheel, a Comcast company, helps media organizations increase ad revenue by integrating ad management solutions.
Needing an elastic and cost-effective infrastructure to support rapid growth and major live events, they migrated key AdTech platforms to AWS.
By running workloads on Amazon EC2, it can now scale in 15 minutes—down from 6 months—to handle traffic spikes during events like the Olympics and the FIFA World Cup, while maintaining 99.9% uptime and providing less than 300 milliseconds of latency for ad-serving.
To predict ad inventory for both digital video and linear TV, FreeWheel uses Amazon SageMaker to build an end-to-end distributed machine learning pipeline, forecasting months ahead for billions of daily ad-serving records. As a result, it reduced overall efforts by 60%, cut costs by 50%, and increased the efficiency and accuracy of its predictions.
By also adopting AWS Graviton–based compute instances and AWS Savings Plans, FreeWheel saves up to 20% in compute costs, reinforcing how AWS solutions empower global, high-traffic AdTech platforms.
Kanda specializes in building powerful, AI-driven AdTech solutions. Our experience with AWS and ML-based projects enables us to implement meaningful innovation that aligns with your strategic goals.
AI will have a huge impact on the future of advertising technology. Companies that successfully implement machine learning will have a significant competitive advantage, while those that postpone risk falling behind. AWS SageMaker enables AdTech organizations of all sizes to access, scale, and run powerful ML capabilities at a more reasonable cost.
Whether your focus is refining ad targeting, reducing fraud, or optimizing bids, SageMaker is central to AI-powered advertising.
