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AI, ML & Data Engineering

AI, ML and Data Engineering InfoQ Trends Report - August 2021

How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.

Roland Meertens Kimberly McGuire Srini Penchikala Rags Srinivas Anthony Alford
on Aug 09, 2021
Culture & Methods

GitHub’s Journey from Monolith to Microservices

This article explores GitHub's recent journey towards a microservices architecture. It takes a deeper look at GitHub’s historical and current state, goes over some internal and external factors, and discusses practical consideration points in how Github tackled their migration, including key concepts and best practices of implementing microservices architecture.

Sha Ma
on Jul 28, 2021
Culture & Methods

Data-Driven Decision Making – Optimizing the Product Delivery Organization

The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. Applying Hypotheses, CD Indicators and SRE’s SLIs / SLOs enables a software delivery organization to optimize for effectiveness, efficiency and service reliability.

Vladyslav Ukis
on Apr 20, 2020
Culture & Methods

Data-Driven Decision Making – Product Operations with Site Reliability Engineering

The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. In Operations, SRE’s SLIs and SLOs can be used to steer the reliability of services in production.

Vladyslav Ukis
on Mar 25, 2020 2
Culture & Methods

Article Series: Data-Driven Decision Making

The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making.

Vladyslav Ukis
on Mar 06, 2020
Culture & Methods

Q&A on the Book Agile Machine Learning

The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It explores how to apply agile practices for dealing with the unknowns of data and inferencing systems, using metrics as the customer.

Ben Linders Matthew Hurst
on Feb 12, 2020
Culture & Methods

Data-Driven Decision Making – Product Management with Hypotheses

The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. In Product Management, hypotheses can be used to steer the effectiveness of product decisions about feature prioritization.

Vladyslav Ukis
on Feb 02, 2020 3
Culture & Methods

How to Tell Compelling Stories Using Data: Q&A with Dr. Christine Bailey

The more evidence we have, the more likely our ideas are believed - or so we’re conditioned to think . But data doesn’t always engage people; this is where storytelling can help to combine data, insights, and emotion, said Dr. Christine Bailey. She presented techniques to tell compelling stories with data, and showed how that can increase our influence with external and internal stakeholders.

Ben Linders Christine Bailey
on Sep 25, 2019
Culture & Methods

Q&A on the Book Evidence-Based Management

The book Evidence-Based Management by Eric Barends and Denise Rousseau explores how to acquire evidence, appraise the quality of the data, apply it in your management decisions, and assess the impact of your decisions.

Ben Linders Denise Rousseau
on Mar 16, 2019
Culture & Methods

Scaling Agile in a Data-Driven Company

The IT department of Cerved Group experimented with Scrum, Kanban, Lean, SAFe, and Nexus, to learn what works for them and fine-tune and continuously improve their way of working. In their transformation, they focused on the culture and mindset to cultivate high-performing teams, to improve the quality of products for customers, and to help managers transforming themselves in servant leaders.

Antonello Mantuano Ben Linders
on Oct 25, 2018
Architecture & Design

Managing Data in Microservices

This article provides practical examples of how to manage data in microservices, with an emphasis on migrating from a monolithic database. It is recommended to build a monolith first, and only migrate to microservices after you actually require the scaling and other benefits they provide.

Randy Shoup Thomas Betts
on Apr 16, 2018 4
AI, ML & Data Engineering

Big Data Processing with Apache Spark - Part 5: Spark ML Data Pipelines

With support for Machine Learning data pipelines, Apache Spark framework is a great choice for building a unified use case that combines ETL, batch analytics, streaming data analysis, and machine learning. In this fifth installment of Apache Spark article series, author Srini Penchikala discusses Spark ML package and how to use it to create and manage machine learning data pipelines.

Srini Penchikala
on Sep 24, 2016 2