Archive - Machine Learning for Developers

ChatGPT Alternatives That Deserve Your Attention (ML4Devs, Issue 21)

Several Large Language Models are emerging: Google Bard/LaMDA, Meta's LLaMA, Amazon's Multimodal-CoT, HuggingFace's Bloom, and open-source ChatLLaMA.

Mar 9, 2023 •

Satish Chandra Gupta

February 2023

Who Cares if Big Data Is Dead! (ML4Devs, Issue 20)

What really matters is the quality of the data, the data quotient of the organization, and the motives behind using data analytics.

Feb 23, 2023 •

Satish Chandra Gupta

The Rise of ChatGPT: Separating Hype from Reality (ML4Devs, Issue 19)

ChatGPT is part of OpenAI's GPT-3 family of large language models. It is immensely powerful and can also confidently hallucinate. Learn what it can and…

Feb 8, 2023 •

Satish Chandra Gupta

December 2022

MLOps: All-in-One Platform vs Piecemeal Tools (ML4Devs, Issue 18)

Should you choose an all-in-one MLOps platform from your cloud provider or cobble together a solution from piecemeal tools?

Dec 21, 2022 •

Satish Chandra Gupta

November 2022

SQL Renaissance (ML4Devs, Issue 17)

SQL is dead. Long live SQL.

Nov 25, 2022 •

Satish Chandra Gupta

Which Data Pipeline Orchestration Tool Is Right For You? (ML4Devs, Issue 16)

With diverse data sources, multi-cloud, and data mesh scenarios becoming increasingly common, a misfit data pipeline orchestration tool can multiply…

Nov 11, 2022 •

Satish Chandra Gupta

October 2022

Chasm of AI Security Between Research and Production (ML4Devs, Issue 15)

AI research continues to amaze us, but are those safe to use in products and services? Concerns about AI aligning with human goals have become real.

Oct 21, 2022 •

Satish Chandra Gupta

What Are Data, Machine Learning, and MLOps Pipelines? (ML4Devs, Issue 14)

Data pipelines get data to the warehouse or lake. Machine Learning pipelines transform data before training or inference. MLOps pipelines automate ML…

Oct 8, 2022 •

Satish Chandra Gupta

September 2022

AI is Like Teenage Sex… (ML4Devs, Issue 13)

Don't fall for the "AI-first" hype. Focus on being "data-first" and ML/AI will naturally follow.

Sep 23, 2022 •

Satish Chandra Gupta

Should You Care About MLOps? Why and How Much? (ML4Devs, Issue 12)

The answer, as usual, is: it depends.

Sep 9, 2022 •

Satish Chandra Gupta

August 2022

Machine Learning vs Traditional Software Development (ML4Devs, Issue 11)

You define logic in traditional programs. Machine Learning extracts logic (models) from the data.

Aug 18, 2022 •

Satish Chandra Gupta

MLOps for Continuous Integration, Delivery, and Training (ML4Devs, Issue 10)

Overview of MLOps, ML Pipeline, and ML Maturity Levels for continuous training, integration, and deployment.

Aug 5, 2022 •

Satish Chandra Gupta

#nojs-banner { position: fixed; bottom: 0; left: 0; padding: 16px 16px 16px 32px; width: 100%; box-sizing: border-box; background: red; color: white; font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; font-size: 13px; line-height: 13px; } #nojs-banner a { color: inherit; text-decoration: underline; } This site requires JavaScript to run correctly. Please turn on JavaScript or unblock scripts