The machine learning market is projected to be $209.91 billion by 2029 and AWS is at the epicenter of it all. Few realize how far we’ve come since the first neural networks could barely do basic tasks in the 50’s. Machine learning isn’t just changing industries, it’s changing the way we make decisions, analyze data and even communicate.
While trends like automated machine learning and responsible AI are becoming buzzwords, AWS has been quietly but powerfully supporting these advancements. From scalable ML infrastructure to cutting edge algorithms, AWS services are driving innovation across industries but it’s the under the radar developments that are making the biggest impact in 2024.
In this article, we’ll dive deep into the top ML trends shaping the landscape and explore how AWS is positioning itself to support these seismic shifts.
Tracking the latest ML trends and building AWS skills to keep up with these trends involves a combination of multiple efforts, which can be time-consuming. Using this curated list of top trends and supporting AWS services, you can quickly identify the skills you want. By pursuing certifications like the AWS Certified Machine Learning Specialty, you can enhance your expertise and align your skills with industry demands. Do check it out.
Top ML Trends to Know in 2024
In 2024, several factors have contributed to the changing ML trends. The following ten trends reflect a combination of technological advancements, societal concerns, and evolving business needs.
Table of content:
ML Democratization
ML democratization is the business idea to make ML technologies accessible to a broader audience, beyond just data scientists and ML experts. Among the many benefits of machine learning (ML), here are a few notable ones:
- Accelerated business innovation
- Enhanced decision making
- Improved operational efficiency
No-code ML platforms, AutoML, and the rise of citizen developers are some of the trends ushering in ML democratization. AWS supports these trends with multiple services, where the SageMaker group of services forms the core. SageMaker streamlines the entire ML lifecycle with no code or minimal code.
Here are some of these services:
Responsible ML
Responsible ML is a set of principles and practices that ensure the ethical and responsible use of ML algorithms. This involves taking into account the potential risks and implications of using ML algorithms and taking steps to mitigate those risks.
Figure. Risks with ML
AWS provides several services and tools that enable organizations to build, deploy, and manage responsible MLsystems.
Here are some examples:
ML Security and Data Protection
ML security and data protection are more important than ever before. As data, including sensitive data, grows by leaps and bounds every minute and as laws become stricter, organizations must take all measures to shield models from attacks and leaks. AWS offers various services for security and data protection, including identity management, encryption, threat detection, compliance, and monitoring tools to ensure robust protection across cloud environments.
Multimodal ML
In multimodal learning, the ML model learns and processes information from multiple modalities such as tables, text, images, voice, and video. By combining various modalities, the models improve accuracy and applications in real-world scenarios.
AWS offers different multimodal foundational models in Bedrock and SageMaker. In addition, you can combine different Amazon services to create a multimodal ML model. Just Walk Out is another latest technology from Amazon that uses a multimodal foundational model.
Quantum ML
Quantum machine learning (QML) combines ML and quantum computing to speed up data processing, create robust algorithms, improve accuracy in ML models, and increase security. You can leverage quantum computers to find patterns in data to train ML algorithms to extract information hidden in a complicated network.
AWS Bracket provides a developmental environment that allows users to explore and test quantum algorithms, circuits, and workflows using different quantum hardware technologies. It also has a built-in Jupyter Notebook interface for running and visualizing quantum algorithms. Further, it integrates with other AWS services such as Amazon S3, Amazon SageMaker, and Amazon ECS to provide a complete development environment for quantum computing.
In addition to Bracket, Amazon offers AWS Quantum Solutions Lab, which accelerates quantum computing innovation by uniting experts from AWS, academia, and industry.
GPU Acceleration
GPUs are specialized hardware accelerators designed to optimize graphic and compute-intensive applications, including AI, machine learning, and high-performance computing (HPC). GPU acceleration is changing how we handle data, making complex tasks faster and more efficient. GPUs help developers and data scientists train complex models on massive data sets.
AWS supports GPUs in many ways:
- EC2 instances: the following EC2 instances are powered with GPU acceleration
- Amazon Elastic Inference lets you attach just the right amount of GPU-powered inference acceleration to any Amazon EC2 instance. This is also available for Amazon SageMaker notebook instances and endpoints, bringing acceleration to built-in algorithms and deep learning environments.
- SageMaker supports GPU-based instances for training and inference.
ML for Blockchain
The integration of blockchain with ML added new values in terms of creating a secure, decentralized network transaction and administrative system. A blockchain is a decentralized digital ledger that allows transparent information sharing with a business network. Blockchain is gaining popularity due to its security and transparency in all its transactions within the network. As all users have a copy of the blockchain, it’s almost impossible to tamper with data without being detected by others.
AWS offers the following dedicated blockchain services:
Note: QLDB is no longer available for new customers and will reach the end of support on 07/31/2025. Users need to replace Amazon QLDB with Amazon Aurora PostgreSQL for audit use cases.
Industry-specific Trends
ML for Healthcare
ML has been increasingly used in healthcare for improving patient outcomes, diagnostic accuracy, and drug discovery, and in many other healthcare fields. ML algorithms can help you identify patterns and draw insights from large medical datasets.
AWS enables healthcare organizations to easily deploy analytics and ML tools for healthcare-related solutions.
In addition to these services, SageMaker can assist in processing medical data, personalizing patient care, and accelerating drug discovery, and offering tools specifically designed for the healthcare domain.
To know more about these services, see AWS Health Data Portfolio.
Edge ML for IoT
Emerging Internet of Things (IoT) applications such as self-driving cars, wearable devices, security cameras, and smart home appliances, and many others are delay-sensitive and computationally expensive, requiring real-time processing of massive amounts of data generated by distributed end users. Edge ML enables the training and deployment of ML models directly on edge devices, thereby processing data locally, and improving efficiency, privacy, and responsiveness across various applications and industries.
Using AWS services for ML with IoT devices can significantly enhance data collection, processing, analysis, and decision-making.
Here’s how you can leverage the following AWS IoT services in an ML context:
To know more, see Choosing an AWS IoT service.
ML for Time-series Forecasting
Time-series forecasting has been a fundamental technique for decades, but its demand has been increasing significantly in recent years due to the explosion of data. Time series forecasting is the ML approach to predict future activities by analyzing historical data.
Conclusion
The top ten ML trends discussed in the blog highlight how encompassing ML has become, touching every sphere of our lives, and how AWS provides support to help organizations implement them. As technology grows, so does the demand for skilled professionals. Whizlabs video lectures and guided labs can help you stay ahead of the competition. For hands-on experience with different AWS services, check our AWS hands-on labs and AWS sandboxes.
