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What you need to know about product management for AI

O'Reilly on Data

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

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How Block is accelerating engineering velocity through developer experience

CIO Business Intelligence

Block collects developer experience data with the help of DX , an engineering intelligence platform that helps streamline data collection and reporting, as well as enabling Block to benchmark itself against industry peers. Rather, Coburns team optimizes for fast experimentation and a metrics-driven approach.

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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates. Identifying Key Metrics for Conversion Rate Optimization Data collection and analysis are both essential processes for optimizing your conversion rate.

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Methods of Study Design – Experiments

Data Science 101

Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV.

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Some highlights from 2020

Data Science and Beyond

Well, no one has compiled a meta-post of my public work from 2020 (that I know of), so it’s finally time to publish it myself. I previously posted about my experiences with RLS offline data collection and visualisation of the collected data , and have since helped with quite a few RLS surveys. Remote work.

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Of Muffins and Machine Learning Models

Cloudera

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.