2014 ML ML Engineer 
Towards AI
AUGUST 25, 2023
As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI.
FEBRUARY 28, 2023
These problems are why, despite the early promise and floods of investment, technologies like self-driving cars have been just one year away since 2014. As a result, the AI production gap, the gap between “that’s neat” and “that’s useful,” has been much larger and more formidable than ML engineers first anticipated.
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AWS Machine Learning Blog
NOVEMBER 16, 2023
Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.
TheSequence
JULY 12, 2023
After my post-doc I went to work for a string of small European startups before moving to the US in 2014 and joining Instacart where I led engineering teams dealing with payments and orders, and dabbled in MLOps. In 2018, I joined Cruise and cofounded the ML Infrastructure team there. This required large end-to-end pipelines.
Snorkel AI
MARCH 9, 2023
The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Kubernetes has also become an appealing option for ML pipelines due to many of the reasons above. The Job Abstraction K8s users often initiate 1-off workloads (like for ML training) using a job object.
Snorkel AI
MARCH 9, 2023
The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Kubernetes has also become an appealing option for ML pipelines due to many of the reasons above. The Job Abstraction K8s users often initiate 1-off workloads (like for ML training) using a job object.
AWS Machine Learning Blog
JUNE 3, 2024
Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. SageMaker Canvas provides ML data transforms to clean, transform, and prepare your data for model building without having to write code. Choose Export.
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