AWS Machine Learning Blog
JANUARY 5, 2024
This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.
MARCH 13, 2023
With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.
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Towards AI
APRIL 27, 2023
Nothing in the world motivates a team of ML engineers and scientists to spend the required amount of time in data annotation and labeling. Now if you see, it's a complete workflow optimization challenge centered around the ability to execute data-related operations 10x faster. It's a new need now.
Heartbeat
SEPTEMBER 6, 2023
When working on an ML project, we must compare various machine learning models with different hyperparameters and want to understand which models and hyperparameters are most effective for our use case. Image by Author If you want to end the experiment, you can use the end method of the Experiment object to mark the experiment as complete. #
TheSequence
MAY 24, 2023
I originally did a master's degree in physics focusing on astrophysics, but around that time, I noticed the breakthroughs happening in ML so I decided to switch the focus of my studies towards ML. data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ).
AWS Machine Learning Blog
NOVEMBER 9, 2023
Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.
Pickl AI
JANUARY 23, 2023
Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. Theano Theano is one of the fastest and simplest ML libraries, and it was built on top of NumPy. When used in GPU architectures, this framework can complete tasks 140 times faster.
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