Posted on

MLOps for Enterprise AI

This is an article from DZone's 2022 Enterprise AI Trend Report.

For more:


Read the Report

There was a time when building machine learning (ML) models and taking them to production was a challenge. There were challenges with sourcing and storing quality data, unstable models, dependency on IT systems, finding the right talent with a mix of Artificial Intelligence Markup Language (AIML) and IT skills, and much more. However, times have changed. Though some of these issues still exist, there has been an increase in the use of ML models amongst enterprises. 

Posted on

GloVe and fastText — Two Popular Word Vector Models in NLP

Miklov et al. introduced the world to the power of word vectors by showing two main methods: Skip–Gram and Continuous Bag of Words (CBOW). Soon after, two more popular word embedding methods built on these methods were discovered. In this post, we’ll talk about GloVe and fastText, which are extremely popular word vector models in the NLP world.

Global Vectors (GloVe)

Pennington et al. argue that the online scanning approach used by word2vec is suboptimal since it does not fully exploit the global statistical information regarding word co-occurrences.

Posted on

Prepare Your Data for ML Training

The process to prepare data for machine learning model training looks somewhat similar to the process of preparing food ingredients to cook dinner. You know that in both cases it takes time, but then you are rewarded with a tasty dinner, or in this case, a great ML model.

I will not be diving into data science or discussing how to structure and transform data. It all depends on the use case, and there are so many ways to reformat data to get the most out of it. I would rather focus on a simple but practical example — how to split data into training and test datasets with Python.

Posted on

Object Detection Tutorial in TensorFlow: Real-Time Object Detection

Creating accurate machine learning models that are capable of identifying and localizing multiple objects in a single image has remained a core challenge in computer vision. But with recent advancements in deep learning, object detection applications are easier to develop than ever before. TensorFlow’s object detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. 

You can go through this real-time object detection video lecture where our deep learning training expert discusses how to detect an object in real time using TensorFlow.