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Work Streams and Activities

WS1 – Pilot studies: from Idea to Valid solutions

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title	Theme 1: Coding and Classification

1.1. Apply ML techniques to classification and aggregation web scraped price data, IBGE Brazil - Vladimir Miranda

1.2. Using Big Data Tools and Machine Learning Techniques to Assign Classification of Individual Consumption by Purpose (COICOP) Categories, Turkey Statistical Institute (TURKSTAT) - Mustafa Karamavus

1.3. Code Classification using Multilingual Transformer-based Models, Danish Business Authority - Casper Eriksen

1.4. Coding and Classification: Automated coding of classifiers as a shared service, INE Chile - Klaus Lehmann

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title	Theme 2: Imputation

1.5. Multiple imputation through machine learning, Statistical Office in Rzeszów, Statistics Poland - Sebastian Wójcik

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title	Theme 3: Imagery Analysis

1.6. Estimating Malaysia Rubber Plantation Area Productivity Using Satellite Imagery and Machine, Department of Statistics Malaysia - Rajkumar a/l V.Nagarethinam

1.7. Feasibility study of Satellite Imagery Analysis for Wealth Index Development in Indonesia, BPS Statistics Indonesia - Arie Wahyu

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title	Theme 4: Modelling for Estimation

1.8. State level expenditure estimates based on ML techniques, U.S. Bureau of Labour Statistics - Yezzi Lee; Clayton Knappenberger

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title	Theme 5: Transferring Knowledge and Experience

1.9. Replicating successful data science projects across NSOs, Statistics Flanders - Michael Reusens

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title	Theme 6: Route Optimisation

1.10. Route Optimisation through genetic algorithm, INE Chile - Jose Bustos

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WS2 – From Valid Solution to Production

Activities

2.1. Automated production tool to code IMF member state time series data using ML algorithms, International Monetary Fund - Ayoub Mharzi

2.2. Deployment of a Data Lake architecture to put into production data science projects, INEGI Mexico - Abel Coronado

2.3. Design and assess a whole workflow to enable Natural Language Processing and Machine Learning methodologies to be integrated into a continuous production process, INEGI Mexico - Jael Pérez; Alejandro Ruiz

2.4. A technical platform that supports the whole machine learning process and thereby ensures the quality of that process and in the end contribute the overall quality of the statistical output, Statistics Sweden - Alexander Thorell

WS3 – Data Ethics and Governance

3.1 The establishment of a set of ethical principles to provide a clear framework to enable ethical use of Machine Learning for research and statistics, UK Statistics Authority - Lily O'Flynn; Simon Whitworth

WS4 – On The Quality of Training Data

4.1 Identifying the circumstances under which an ML model should be retrained in order to maintain the predictive power and quality of the model, Statistics Finland - Riitta Piela; Rok Platinovsek

WS5 – On The Quality Framework for Statistical Algorithms

5.1 Explore dimensions of QF4SA in a consolidated project to analyse an output based on a set of standard metrics and procedures
INEGI Mexico - Jose Jimenez; Alejandro Ruiz

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ML2021 Presentations

Date	Speaker	Presentation
26 April	Kate Burnett-Isaacs (Statistics Canada)	HLG-MOS Synthetic Data Project (presentation slides)
22 March	Sigrid van Hoek (Statistics Netherlands)	Fair algorithms project (presentation slides)
22 March	Lily O'Flynn and Simon Whitworth (UK ONS)	UK SA Data Ethics (presentation slides)
23 Feb	Riitta Piela and Rok Platinovsek (Statistics Finland)	Best practices in maintaining the quality of data in ML developments (presentation slides)
	Casper Eriksen (Danish Business Authority)	Multilingual Classification of Economic Activities (presentation slides)
	Michael Reusens (Statistics Flanders)	WS1 Theme 5: Transferring Knowledge and Experience (presentation slides)

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How to join the Group ?

Do you have any ML topic you are interested in working together with peers? Do you have any issue (technical, strategic, organisational) you want to discuss with other NSOs? Contact ML2021 (ML2021 at ons dot gov dot uk), copying UNECE (choii at un dot org) if you want to join the Machine Learning 2021 Group!

ML 2021 Group Structure and Workstreams

Terms of Reference

If you are a member of the Global Network of Data Officers and Statisticians, you can follow us from the ML for Official Statistics group in the Network (see quick guide on how to join the Global Network).

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Progress Update

January 2021

Just under under 100 members joined for the inaugural Machine Learning 2021 (ML2021) meeting on 29 January 2021.

The meeting ratified the groups governance structure and schedule for 2021. Submitted activity proposals were grouped into Workstreams in advance and confirmed with members.

In anticipation of the February meeting, members have signed up to workstreams and leads have been asked to confirm their output and objectives.

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name	ML Jan meeting 290120211.pptx
page	Machine Learning Group 2021
space	MLP
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Resources

Machine learning competency in context of Big Data training and human resources (from UN Global Working Group on Big Data Task Team on Skills and it is relevant to the community)

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title	Group 2020 Planning

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Machine learning Group 2021

Following the great interest in continuing the work of the HLG-MOS Machine Learning Project, UK Office of National Statistics (ONS) is launching the Machine Learning Group 2021. The new ML group will focus on developing and implementing ML for official statistics (see slides below for presentation and live survey conducted during the HLG-MOS ML Project webinar)

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name	Webinar Future Directions.pdf
page	HLG-MOS ML Project webinar
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name	ML Project Webinar Slido Poll Result.pdf
page	Timetable and Documents
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Table 1: Work packages investigated by the ML 2019/2020 project (hyperlinks) and potential themes to be explored by the ML 2021 project (text in red).

The Journey	Moving from idea to valid solution (demonstration)		Moving from valid solution to production (Operationalisation)		Ensuring production robustness (Maintenance)
	All WP1 pilot studies		Some WP1 pilot studies		Very few WP1 pilot studies
	Other applications of Machine Learning		Some other applications of Machine Learning		Very few other applications of Machine Learning
	WP3 Integration (Q5 & Q6)		WP3 Integration (Q5)
	Workstream 1: Support current studies towards production; welcome new studies in other processes (e.g. record linkage) and/or data sources (e.g. satellite data)
Supported by	Quality (accuracy, timeliness, efficiency, explainability and reproducibility)	Good Training Data	Skills/Competences	Computing Infrastructure	Interoperability / Business Process	Ethics and Legal	Security
	WP2 Quality		WP3 Integration (Q3 & Q4)
	Workstream 2: Experiment with practices and methods on some dimensions of QF4SA (WP2); Workstream 3: Review and improve the Framework	Workstream 4: How to get good training data, how to keep it up to date, when to relearn a model, what does 'good' mean, how to measure that?	Workstream 5: What skills? How to learn? Where to find them?	To be defined	To be defined	Workstream 6: Ethics handbook, regulations , etc.	To be defined
Facilitated by	Organisation			Sharing and Collaboration
	WP3 Integration (Q1 & Q2)			HLG-MOS Machine Learning Project
	Initiatives to accelerate the integration of machine learning solutions			ML Studies and Codes
	Workstream 7 : Create/maintain a network of data science unit leaders; Workstream 8: Beyond 2021: How can we better prepare for next 2-5 years? What technology and data sources can we expect? What skills will we need?			Learning and Training
				HLG-MOS ML Project webinar

Progress status

Update from ONS (December 15, 2020)

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Work Streams and Activities

ML2021 Presentations

How to join the Group ?

Progress Update

Resources

Machine learning Group 2021

Progress status