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Krishna Yogi
Krishna Yogi

304 Followers

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Enhancing Machine Learning Pipelines with Advanced Monitoring Techniques

In the dynamic landscape of machine learning, maintaining optimal model performance and reliability is paramount. Advanced monitoring strategies have emerged as crucial tools to ensure the dependability and accuracy of machine learning pipelines. …

Mlops

8 min read

Enhancing Machine Learning Pipelines with Advanced Monitoring Techniques
Enhancing Machine Learning Pipelines with Advanced Monitoring Techniques
Mlops

8 min read


Sep 14

Advanced Feature Engineering Techniques — Part II

Let's continue the advanced feature engineering techniques in this blog. If you haven’t read Part 1, make sure to read it before continuing with Part 2. Link: https://krishnayogi.medium.com/advanced-feature-engineering-82e7e1e32b48 Outliers In data analysis and feature engineering, identifying and handling outliers is a crucial step to ensure that your machine-learning models are robust…

Feature Engineering

9 min read

Advanced Feature EngineeringTechniques — Part II
Advanced Feature EngineeringTechniques — Part II
Feature Engineering

9 min read


Sep 13

Advanced Feature Engineering — Part I

Feature engineering is transforming raw data into informative and relevant input variables to enhance the performance of machine learning models. All machine learning algorithms use some input data to create outputs. This input data comprises features, which are usually in the form of structured columns. Algorithms require features with some…

Feature Engineering

9 min read

Advanced Feature Engineering — Part I
Advanced Feature Engineering — Part I
Feature Engineering

9 min read


Sep 8

Rules for Machine Learning, Testing & Debugging

Are you considering integrating machine learning into your product or system? Here are a few essential tips to keep in mind for a successful and efficient machine-learning journey: Rules for Machine Learning Start Simple: Don’t rush into machine learning right away. Begin with basic heuristics or rules to get your product running. …

Mlops

6 min read

Rules for Machine Learning, Testing & Debugging
Rules for Machine Learning, Testing & Debugging
Mlops

6 min read


Sep 8

A Comprehensive Guide to Data Engineering

Explore the essential principles, technologies, and best practices in data engineering to build robust data pipelines, ensure data quality, and drive insights for your organization. Introduction In the rapidly evolving landscape of data science and analytics, data engineering plays a pivotal role as the backbone of data-driven decision-making. It serves as…

Data Engineering

15 min read

A Comprehensive Guide to Data Engineering
A Comprehensive Guide to Data Engineering
Data Engineering

15 min read


Sep 8

Introduction to Tensor Slicing

When working on machine learning applications like object detection and natural language processing (NLP), there are situations where you need to manipulate tensors by extracting specific sections (slices) of them. One such scenario arises in model architectures involving routing, where a layer determines which training examples should be forwarded to…

TensorFlow

7 min read

Introduction to Tensor Slicing
Introduction to Tensor Slicing
TensorFlow

7 min read


Aug 26

The Power of Feature Crosses: Real-Life Use Cases and Code

In the realm of machine learning and data science, the concept of “feature crosses” holds the potential to transform how we perceive and utilize data. Feature crosses, also known as interaction features, allow us to combine existing components to create new ones that capture synergies and interactions. These interactions often…

Feature Engineering

8 min read

The Power of Feature Crosses: Real-Life Use Cases and Code
The Power of Feature Crosses: Real-Life Use Cases and Code
Feature Engineering

8 min read


Aug 25

Google Data Flow vs. Google Data Fusion

In today’s data-driven era, organizations are inundated with vast amounts of data that hold the key to valuable insights. To unlock this potential, efficient data processing is paramount. Google, a leader in cloud computing, offers two powerful solutions for data processing and preparation: Google Data Flow and Google Data Fusion…

Google Cloud Platform

8 min read

Google Data Flow vs. Google Data Fusion
Google Data Flow vs. Google Data Fusion
Google Cloud Platform

8 min read


Jul 10

Changing Google Vertex AI Workbench Notebook Python Version

Tips for running TensorFlow & TensorFlow Data Validation Libraries by downgrading Python version to 3.7 start with Terminal on ‘Managed Jupyter notebooks’ if you get this message just hit build and move on

Google Cloud Platform

2 min read

Changing Google Vertex AI Workbench Notebook Python Version
Changing Google Vertex AI Workbench Notebook Python Version
Google Cloud Platform

2 min read


Jul 5

MLops for Batch Prediction with Vertex ai

Running Production-ready ML pipelines with cloud functions and cloud scheduler The world of ML Engineering is evolving rapidly with powerful features being offered by cloud providers like Google Cloud, AWS, etc. The goal of Batch prediction is to run a prediction job in a batch mode and store the prediction…

Ml Pipeline

3 min read

MLops for Batch Prediction with Vertex ai
MLops for Batch Prediction with Vertex ai
Ml Pipeline

3 min read

Krishna Yogi

Krishna Yogi

304 Followers

Data Scientist, NLP Enthusiast, Blockchain Evangelist..

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