ML from Research to Production – Challenges, Best Practices and Tools [Guide]
Taking machine learning or AI into production takes a lot of patience, effort, and resources. AI models are great for predicting all sorts of things, from what movie you’ll like to whether your cat will scratch the furniture. But in most cases, AI models have a hard time making it into production.
In this article, we’ll discuss why it’s hard to get models to production, how you can take your machine learning experiments from research to production, and things to consider after your model is deployed to production...
Custom Object Detection using TensorFlow — (From Scratch)
In this tutorial, we’re going to create and train our own face mask detector using a pre-trained SSD MobileNet V2 model.
First we will create our own image dataset and later we will see how to train a Custom Model for Object Detection (Local and Google Colab!)...
An Overview of QuickSort Algorithm
Sorting is the process of organizing elements in a structured manner. Quicksort is one of the most popular sorting algorithms that uses nlogn comparisons to sort an array of n elements in a typical situation. Quicksort is based on the divide-and-conquer strategy. We’ll take a look at the Quicksort algorithm in this tutorial and see how it works.
Model Debugging Strategies – Machine Learning Guide
You can’t deploy software based on a buggy model. Debugging is a big part of model development and deployment in machine learning.
Is there one silver-bullet strategy that works for all types of models and applications? Not really, there are plenty of different strategies for model debugging, and we’re going to explore 10 of them in this article...
Why DVC is Better Than Git and Git-LFS in Machine Learning Reproducibility
In recent years, machine learning has been a recurring theme at many AI conferences and in the popular press. Yet, for a topic so widely discussed and hyped, surprisingly little is known about how it works under the hood. An important question to consider is how we can be certain that a particular model will perform as expected or even work at all? How can we be sure our models are reproducible?
Model Registry Makes MLOps Work – Here’s Why
Model Registry is a part of the machine learning lifecycle or MLOps. It is a service that manages multiple model artifacts, tracks, and governs models at different stages of the ML lifecycle. Model registry is a collaborative hub where teams can work together at different stages of the machine learning lifecycle, starting from the experimentation phase to the production phase. It makes approval, governance, and monitoring seamless and improves workflow performance...
Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in supervised learning. In order to make machine learning work well on new tasks, it might be necessary to design and train better features. As you may know, a “feature” is any measurable input that can be used in a predictive model — it could be the color of an object or the sound of someone’s voice...
Kubernetes Alerting | Best Practices in 2021
It’s easy to see why Kubernetes is one of the most popular container orchestration tools. It isn’t just for batch processing; it can handle real-time data, too. However, running Kubernetes means you’ve got to monitor the health of your cluster closely to make sure everything runs smoothly at all times. Alerting on metrics, Kubernetes events, logs and more is possible and often a requirement for today’s engineering teams.