Marktechpost
MAY 30, 2024
Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes lessons on vector search and text embeddings, practical demos, and a hands-on lab.
Mlearning.ai
AUGUST 18, 2023
TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). As an ML engineer you’re in charge of some code/model. for our demo project: By decorating a method as @task, you create a CMD interface for it automatically — without introducing argparse.
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Chatbots Life
MAY 12, 2023
As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.
AWS Machine Learning Blog
APRIL 11, 2023
We explain the process and network flow, and how to easily scale this architecture to multiple accounts and Amazon SageMaker domains. Steps 1–4 are covered in more detail in Part 2 of this series, where we explain how the custom Lambda authorizer works and takes care of the authorization process in the access API Gateway.
The MLOps Blog
MAY 9, 2023
One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. Kale v0.7.0.
The MLOps Blog
OCTOBER 20, 2023
And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. There, you can use infographics, custom visualizations, and broader ways to explain your ideas. Data on its own is not sufficient for a cohesive story.
The MLOps Blog
NOVEMBER 14, 2023
Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.
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