Becoming Human

MAY 11, 2023

The MLOps process can be broken down into four main stages: Data Preparation: This involves collecting and cleaning data to ensure it is ready for analysis. The data must be checked for errors and inconsistencies and transformed into a format suitable for use in machine learning algorithms.

Machine Learning 78

Machine Learning Machine Learning Cloud Computing DataOps 78

Pickl AI

NOVEMBER 21, 2024

Step 2: Numerical Computation in MATLAB Once the data is cleaned, you can use MATLAB for heavy numerical computations. You can load the cleaned data and use MATLAB’s extensive mathematical functions for analysis. Step 3: Statistical Analysis in R Finally, you can use R for advanced statistical modelling.

Python 52

Python Cloud Computing Machine Learning Machine Learning 52

Pickl AI

JANUARY 17, 2023

In a business environment, a Data Scientist is involved to work with multiple teams laying out the foundation for analysing data. This implies that as a Data Scientist, you would engage in collecting, analysing and cleaning data gathered from multiple sources.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Towards AI

FEBRUARY 11, 2025

Path to Maturity – in data engineering often looks like this: Junior: Ill fix it with code Mid-level: Ill build a system to prevent it Senior: Lets understand why this happens Lead: We need to change how we work Image by Author The best technical solution cant fix a broken process.

Data Quality 53

Data Quality Data Engineering Data Engineer Data Engineering 53

Pickl AI

OCTOBER 6, 2024

It can occur in bulk, where large batches of data are uploaded at once, or incrementally, where data is loaded continuously or at scheduled intervals. A successful load ensures Analysts and decision-makers access to up-to-date, clean data. Advantages: Speed: ELT processes can handle large volumes of data quickly.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52