pytorch
In a project I am working on, I had to push the model trained on a GPU into production. The problem started when I tried to make an inference script for the model and execute it in a CPU-only device to test the script. Habituated to the .to(device) method,
DataInProduction
Unveiling the essence of data accuracy, completeness, consistency, and timeliness.
Deep Learning
One of the most underrated skills any data professional should have when working with strings
RoadMaps
In the last article, Part 1 of this road map, we briefly discussed the starting tools and directions for machine learning. We discussed a simple plan that I believed would help beginners establish a solid foundation. After talking about this, at the end of the article, I said I was
MLOps
How do we determine the criteria for "good" and "useful" data? Where can we collect the data from? How much data should we collect? How can things go wrong in this process?...
RoadMaps
Think of it as starting out as a level 1 character. Try to understand the basic game mechanics, the environment and your allegiance. As you progress through the game, you level up and...
AI tools
Explore our summary of DeepLearning.AI's ChatGPT course. Learn to engineer effective prompts and use cases including summarizing, inferring, transforming, expanding, and chatbot creation.
Join me in my journey through the world of Data and ML...
Discover how to fool CNNs with unexpected images, like a cat that's actually a goldfish! Learn how to generate adversaries for convolutional neural networks (CNNs) with this informative article.
Someone trying to start their ML journey would be confused about where to start. There are thousands of roadmaps in the world, some starting from Math, some starting from ML online courses, then backtracking to Math, some directly starting from Deep Learning before everything etc. You should have probably come
Get an in-depth review of the Intro to ML Engineering course by Andrew Ng. Read this no-nonsense review before starting your MLOps specialization, to optimize your learning.
Maximize your ML project success with our clear guide to efficient scoping. Learn the essential steps and gain the knowledge you need to start your project with confidence.
You have likely used chatGPT. Imagine how thoroughly the data was cleaned for the model. Let's delve deeper into production data management.
Hello Everyone! This is Akhil Theerthala, with another installment of my MLOps series. Going back through my old notes, rewriting them, and reading them with fresh eyes has been a fantastic learning experience. Whenever you revisit the notes, I hope you are struck by the same enthusiasm I felt when
Ever wondered about the standard practices used for improving machine learning models or finding the right area to improve your models? The answers are here.
Get familiar with the processes typically used and the difficulties encountered when creating machine learning models for use in real-world settings.
Hello everyone! Welcome back to the MLOps series. Here I will keep uploading my notes for the Machine Learning Engineering for Production Specialization offered by DeepLearning.AI on Coursera. We are currently in the first course, Introduction to Machine Learning in Production, which is taught by the legend, Andrew Ng
Hello Everyone! While my work with the MLOps specialization is going on, I also plan to recall my old notes on CNNs and RNNs from the Deep Learning specialization. This series of articles majorly focuses on the course-4 of the specialization titled Convolutional Neural Networks. Hope you enjoy these notes,
Learn about the fundamental stages that are involved in deploying a Machine learning model. This article is based on Andrew Ng's "Introduction to Machine learning in production" course.
