Artificial intelligence and machine learning are essential technologies that may help businesses find new ways to boost sales, cut expenses, improve processes, and better understand their customers. AWS intends to democratize machine learning and make it accessible to developers regardless of experience or organization size by providing robust computation, high-speed networking, and scalable, high-performance storage solutions on demand for each machine learning project.
Developers and data scientists are pushing the frontiers of technology by adopting larger and more powerful deep learning models, which is driving up the cost of running the underlying infrastructure to train and deploy these models. AWS is developing high-performance, low-cost machine learning processors to help customers expedite their AI/ML transformation. AWS Inferentia is the first machine learning chip designed from the ground up by AWS for cloud machine learning inference at the lowest cost.
For machine learning inference, Amazon EC2 Inf1 instances powered by Inferentia deliver 2.3x greater performance and up to 70% cheaper cost than current generation GPU-based EC2 instances. It is designed specifically for training deep learning models. AWS enables customers to efficiently train and deploy deep learning models in production with outstanding performance and throughput at much lower costs thanks to these advances. AWS Trainium is its second machine learning chip, and it will be ready in late 2021.
Customers from various sectors have used Inferentia to run their machine learning applications in production and have experienced significant performance and cost benefits. AirBnB’s customer support platform enables millions of hosts and guests worldwide to have intelligent, scalable, and excellent service experiences. Natural language processing (NLP) models that powered its chatbots were deployed using Inferentia-based EC2 Inf1 instances. This resulted in a 2x speed boost over GPU-based instances.
Machine learning is an iterative process that requires teams to swiftly design, train, and deploy applications and train, retrain, and experiment to improve the models’ prediction accuracy. Object recognition, natural language processing (NLP), picture classification, conversational AI, and time-series data are just a few of the new deep learning use cases rapidly growing in size and complexity, rising from millions to billions of parameters in a few years. The expense of training and implementing these complicated and sophisticated models is enormous in terms of infrastructure. As businesses grow and aim to deliver superior real-time experiences to their customers, costs can quickly escalate to the point where they are prohibitively expensive.
AWS Inferentia and AWS Trainium are cloud-based machine learning infrastructure services that can help. They enable enterprises to get started quickly and extend their AI/ML initiatives by providing on-demand access to computation, high-performance networking, and big data storage, seamlessly coupled with ML operations and higher-level AI services. The design of Inferentia is optimized for high speed, throughput, and low latency, making it perfect for ML inference deployment at scale.
Each AWS Inferentia chip has four NeuronCores, which implement a high-performance systolic array matrix multiply engine, significantly accelerating common deep learning operations like convolution and transformers. NeuronCores also include a substantial on-chip cache, which reduces external memory accesses and increases performance by lowering latency and increasing throughput. Leading machine learning frameworks, such as TensorFlow and PyTorch, are natively supported by AWS Neuron, the software development kit for Inferentia. Developers can continue to use their favorite frameworks and lifecycle development tools. They can build and deploy many of their trained models on Inferentia by modifying just one line of code, with no additional application code changes.
Any firm can accelerate innovation and embrace the complete machine learning lifecycle at scale with AWS cloud-based machine learning infrastructure options suitable for diverse skill levels. Organizations can now radically improve the customer experience and how they do business with a cost-effective, high-performance cloud-based machine learning infrastructure as machine learning becomes more prevalent. Cloud-based machine-learning infrastructure is providing previously unattainable capabilities, making it far more accessible to non-expert practitioners. That’s why AWS customers are already employing Amazon EC2 Inf1 instances powered by Inferentia to power their recommendation engines and chatbots and gain actionable insights from client feedback.
Nitish is a computer science undergraduate with keen interest in the field of deep learning. He has done various projects related to deep learning and closely follows the new advancements taking place in the field.