AWS Machine Learning Blog

After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]

Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]

Customer satisfaction is a potent metric that directly influences the profitability of an organization. With rapid technological advances in the past decade or so, it’s even more important to elevate customer focus in the following ways: Making your organization accessible to your customers across multiple modalities, including voice, text, social media, and more Providing your […]

Automated defect detection using computer vision helps improve quality and lower the cost of inspection. Defect detection involves identifying the presence of a defect, classifying types of defects, and identifying where the defects are located. Many manufacturing processes require detection at a low latency, with limited compute resources, and with limited connectivity. Amazon Lookout for […]

There have been many recent advancements in the NLP domain. Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior […]

Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic File System (Amazon EFS) volume, and […]

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]

Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large enterprises sometimes set up a center […]

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]

In the end, it came down to 213 thousandths of a second! That was the difference between the two best times in the finale of the first AWS AWS DeepRacer Student Wildcard event hosted in Ottawa, Canada this May. I watched in awe as 13 students competed in a live wildcard race for the AWS […]