3 benefits of using cloud environments for machine learning
Using cloud environments for machine learning can offer several benefits that can contribute to improved efficiency, scalability, and flexibility in your organisation.
Machine learning (ML) and unpacking its capabilities has proven to be a hot topic at many tech conferences, including Microsoft’s Ignite and AWS’s re:Invent. Many are keen to learn to utilise machine learning as a tool within the larger toolkit of artificial intelligence, to achieve intelligent behaviour in systems.
We’ve unpacked 3 main benefits for utilising your cloud environment for machine learning, which could easily drive innovation and efficiency:
1. Machine learning has better access to data
Machine learning, being data-centric, needs a huge amount of data —to train, to learn, to run. Cloud computing platforms offer robust, large-scale storage solutions that can seamlessly accommodate the massive amounts of data required for operating today’s sophisticated machine learning models.
In a cloud environment, ML applications can easily gain access to datasets large enough to deliver valuable insights and patterns, helping the application to ‘learn’ faster, and adapt quickly to changing stimulus and demands.
As we venture further into the data-intensive landscape of machine learning, the cloud is an indispensable ally, clearing the way for raw data to offer meaningful intelligence.
2. Cloud offers greater flexibility and scalability
Cloud computing platforms, like AWS and Microsoft, provide on-demand access to computing resources, allowing organisations to scale up or down based on computational requirements. During intensive training phases, organisations can allocate additional resources to ensure quick responses. For less demanding queries, resources can be scaled down to optimise cost efficiency.
The ability to allocate resources dynamically in the cloud not only ensures optimal performance but also reduces operational costs by eliminating the need for organisations to invest in and maintain on-site infrastructure that may not be used very much. This elasticity in resource provisioning is a key advantage, enabling businesses to adapt to changing computational demands —and fast.
3. Utilising the cloud unlocks faster processing
Machine learning workloads are resource-intensive and use significant processing power during both training and deployment. Cloud computing platforms are equipped to deal with this demand, as they leverage parallel processing and distributed computing capabilities to supercharge the execution of machine learning algorithms.
The cloud’s parallel processing capabilities enable the simultaneous execution of multiple tasks, cutting processing times. This accelerated speed is crucial for real-time decision-making, allowing organisations to derive insights quickly and respond promptly to changing conditions in more dynamic environments.
Cloud platforms may also integrate specialised hardware, such as Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs), which can help to boost machine learning workloads. These hardware accelerators enhance the speed and efficiency of machine learning tasks, contributing to faster model training and improved overall performance.
Final thoughts
Using the cloud’s scalability, flexibility, and processing power, businesses can access or build ML capabilities beyond what would be possible using traditional infrastructure.
The combination of machine learning and cloud computing can drive innovation, efficiency, and transformative insights for organisations that take advantage of this powerful pairing.
Are you ready to equip your team with the power to harness machine learning? Get started with our Machine Learning Pipeline on AWS course or our Microsoft Azure AI Fundamentals today.
