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Development

Featured in Development

MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

Corey Zumar offers an overview of MLflow – a new open source platform to simplify the machine learning lifecycle from Databricks. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment and for managing the deployment of models to production. MLflow is designed to be an open, modular platform.

All in development

Architecture & Design

Featured in Architecture & Design

An Engineer’s Guide to a Good Night’s Sleep

Increased microservices adoption, fueled by the move into the cloud where architectures and infrastructure can flex and be ephemeral, adds complexity every day to the systems we create and maintain. This takes place alongside operating models with autonomous and totally empowered teams, so each distributed system has its own tapestry of technical approaches, languages, and services.

All in architecture-design

AI, ML and Data Engineering

Featured in AI, ML & Data Engineering

Hands-on Feature Engineering for Natural Language Processing

Susan Li shares various NLP feature engineering techniques from Bag-Of-Words to TF-IDF to word embedding that includes feature engineering for both ML models and emerging DL approach. She covers the details including contextual & linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution, which are based on concepts from mathematics, information retrieval, NLP.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Randy Shoup on Creating High-Performance Cultures

In this podcast recorded at QCon London 2019, Shane Hastie, Lead Editor for Culture & Methods, spoke to Randy Shoup, VP of Engineering at WeWork about what is needed to create a high-performance culture.

All in culture-methods

DevOps

Featured in DevOps

Introduction to SMI (the Service Mesh Interface)

Brendan Burns talks about the generic interface for service mesh technology. The goal of this abstraction layer is to provide an easy-to-consume API that can be implemented by many different service mesh implementations (e.g. Istio, Linkerd, etc). Users are free to adopt service mesh concepts without being bound to any particular implementation. He covers the SMI specification and implementations.

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Dmitry Kandalov

Profile page created Apr 09, 2019

Topics

MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

An Engineer’s Guide to a Good Night’s Sleep

Hands-on Feature Engineering for Natural Language Processing

Randy Shoup on Creating High-Performance Cultures

Introduction to SMI (the Service Mesh Interface)

Helpful links

QCons from around the world

Choose your language

Dmitry Kandalov

Summary

MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

From Research to Production With PyTorch

The State of Serverless Computing

An Engineer’s Guide to a Good Night’s Sleep

Louise Poubel on the Robot Operating System

Key Takeaway Points and Lessons Learned from QCon New York 2019

Randy Shoup on Creating High-Performance Cultures

14th State of Agile Report Open

Q&A on the Book Right to Left: The Digital Leader's Guide to Lean and Agile

Hands-on Feature Engineering for Natural Language Processing

Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques

University Research Teams Open-Source Natural Adversarial Image DataSet for Computer-Vision AI

Introducing CircleCI Windows Support, a CI/CD Pipeline on a Windows Virtual Machine

Rook v1.0 Adds Support for Ceph Nautilus, EdgeFS, and NFS Operator

Pulumi Crosswalk: Infrastructure as Code for AWS

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Dmitry Kandalov

Summary