Machine Learning is transforming every industry with innovative techniques receiving deserved attention. But turning innovation into value requires integrating into practical technology products, often with the leadership of product managers. We'll talk about how to help your friendly neighborhood Product Owner: identify where ML can make a difference, develop metrics to validate and refine it, identify data to feed it, prioritize work to develop it, and structure teams to deliver it in a satisfying way.
As Agile become mainstream increasingly organizations are looking to double down on the role of the Product Owner encouraging them to manage the intersection between technology and the business. But Product Ownership is a difficult role as it tries to balance the needs of the business with the reality of software delivery. Also, for many organizations there is some ‘confusion’ with existing roles of business analyst, product manager or even project manager. What does the product owner do anyway?
In this talk Dave West, Product Owner and CEO Scrum.org, the home of Scrum and Professional Scrum Trainer with Prowareness Rob van Lanen describe the genesis of the Product Owner role and how many organizations are dealing with the challenges of slotting this key role into existing product, project and release roles. They will introduce some techniques such as user centric design, and hypophysis based development and describe how approaches such as Lean Startup and pragmatic marketing are providing product owners with a tool box to do their job.
Recorded Webinar can be found at :-https://www.scrum.org/resources/who-product-owner-anyway
Agile Adoption Patterns And AntipatternsNaresh Jain
This document discusses patterns and anti-patterns for adopting agile practices. It will cover successful approaches like training, pilot projects, and working with experienced agile consultants. It also discusses key practices for a successful adoption as well as anti-patterns that should be avoided, such as practices that could undermine becoming an agile software development organization.
The document introduces scrum and agile methodologies as a way to improve over traditional software development processes. It notes common problems with traditional approaches like time overruns, cost overruns, bad planning, and demotivated teams. Scrum and agile are presented as iterative approaches that emphasize collaboration, regular planning, and focus on delivering business value frequently through working software. Key aspects of scrum like the product owner, scrum master, sprints, daily standups, and product backlogs are summarized as ways scrum addresses the issues of traditional approaches.
Rishi Chaddha introduces lean software development principles. He discusses the origins of lean from the Toyota Production System and its focus on eliminating waste. The presentation then covers the seven principles of lean software development which include eliminating waste, building quality in, deferring commitment, delivering fast, respecting people, and optimizing the whole. Kanban and various agile practices are presented as tools that can be mixed and matched to implement lean ideas.
This presentation features the activity of "Diverge," which is the second stage of Google Ventures' Design Sprint (DS) Methodology. The presentation contains visual checklists as well as three case studies to facilitate application of the Design Sprint (DS) Methodology when solving big problems as well as testing new ideas.
SXSW 2013: Get Agile! Scrum for UX, Design & DevelopmentFabrique
This document discusses strategies for implementing agile Scrum practices in an agency setting. Some key points include using a "Super Sprint 0" for extensive planning before starting sprints. It also recommends fully integrating different disciplines like design and development into single teams to build products together in each sprint. Specific tips provided include customizing story templates to specify which disciplines are involved, allowing flexibility in quality standards, and using physical artifacts in shared team spaces to facilitate collaboration. Challenges of varying team composition and availability are addressed, as are roles like the Scrum Master and Product Owner.
All agile development begins with the sales process. Internally, adopting agile approaches require the support of top management and project managers. External clients have to be sold on the agile approach and convinced to sign a contract that allow for agile development. Sales teams have to be able to convince external clients that the agile approach is the best for their project.
Paul Klipp has been selling the agile process internally and to outside clients since 2004 with considerable success. In this presentation, he'll discuss how to sell the benefits of agile development to internal stakeholders and to outside clients and will provide an overview of different approaches to agile contracts.
This document discusses how traditional project management approaches can fall short for complex work, and introduces Agile product development using Scrum as a framework. It explains that Scrum focuses on maximizing business value through collaborative customer engagement and empirical process improvement over comprehensive planning. Scrum is presented as a practical method for complex work where needs may change, using short development cycles called sprints to iteratively deliver working software or products.
If you work in product management, product development or just in technology or software at all, you’ve probably heard of the term ‘MVP’ or Minimum Viable Product. Everyone is using it these days. In this talk I'll explain what an MVP is, why I have a love and hate relationship with it, and how to apply it to your product development.
This document discusses implementing Scrum in Indonesian banks and addresses some common misconceptions. It explains how Scrum can comply with Bank Indonesia regulations regarding software development processes. It presents ways to map Scrum events like sprints to required processes and how Scrum supports risk management practices. It also discusses how Scrum allows for planning at various levels and that documentation is still required beyond just the product backlog.
The main deliverable of Event 3 ("Decide") is a storyboard that would be subsequently used for prototyping the desired customer experience of pre-qualified customers. Before preparing a storyboard, however, the best conceptual solution (sketch) has to be selected and, if necessary, remixed with the best features of alternatives to produce an "ideal (strongest) solution." This presentation visually summarizes the required process while providing three case studies to facilitate understanding of the process of "Decide."
The document provides an overview of agile estimating and planning techniques. It discusses agile principles like iterative development, self-organizing teams, and rapid delivery of working software. It also covers topics like writing user stories, estimating story points, calculating velocity, product backlog design, sprint planning, daily standups, and sprint reviews. The goal is to teach best practices for agile planning and estimation.
The document provides information on agile frameworks like Scrum. It discusses why agile is useful for IT projects, highlighting problems with traditional approaches like high failure rates. Scrum is presented as a lightweight agile framework with core elements like transparency, collaboration and simplicity. An overview of Scrum roles, events, artifacts and principles is given. The document also covers topics like iterative development, value-based prioritization and timeboxing. Visuals of a Scrum board and diagrams are included to illustrate Scrum processes and frameworks.
After an introduction to the basic tenets of Agile and some Agile practices, this presentation to Richmond SPIN (Software Process Improvement Network) talks about ways to convince your organization or clients to use Agile software development practices. Based on a presentation given at Agile 2009 by Arin Sime, Senior Consultant with OpenSource Connections.
The document provides an overview of Agile methodology. It defines Agile as an incremental and iterative approach to project development that values individuals, collaboration, adaptability and working software. The key aspects of Agile include short iterations, frequent delivery of working software, adaptive planning, self-organizing teams, daily stand-ups and retrospectives. Benefits of Agile include improved visibility, productivity and ability to manage changing priorities. The document recommends starting with Agile by identifying issues to solve, creating a visual board and improving through small steps and continuous learning.
Pair Programming, TDD and other impractical thingsMarcello Duarte
"Why should we write our tests first? Isn't that going to slow my development?" "What? Assigning a single task to 2 developers? How is that efficient? What a waste of resources!" "Look, in the perfect world your advises are great, but I have a project to finish here." In this talk Marcello explores efficiency in contrast to effectiveness. He looks into how practices, traditionally accepted as efficient, sometimes turn out to be less effective than a few "impractical" things he has come across.
The product backlog is a prioritized list of features, stories, or tasks that need to be completed for a product. It is created and maintained by the product owner. Items in the backlog are estimated by the team and assigned a business value by the product owner. The backlog is a living document that evolves over time based on changes to requirements, new ideas, or technical challenges. Effective product backlog definition requires collaboration between multiple roles through iterative and incremental processes.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
Your Raw Data is Ready - Introduction to Analytics Engineering | SMX Advanced...Christopher Gutknecht
In this SMX Advanced 2022 session, Christopher talks about the potential of working with raw data and how to properly approach the task of transforming raw data into high-quality reusable tables in your data warehouse. dbt as a transformation framework plays a key role in delivering quality and structure for this process. The chance for search marketers is to acquire data modeling skills and learn to build their own custom data products around Google Ads and Google Analytics. This talk is not just for inhouse-teams, also for agencies seeking to extend their services into data management.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
This document summarizes the key steps and outcomes of a project to build an end-to-end recommendation system for a power utility company. The system was designed to integrate machine learning models with mobile and call center systems to recommend ancillary products to customers. The project involved exploring customer data, developing machine learning models through an iterative process, and operationalizing the models by building APIs and automated workflows. The new system provided recommendations via microservices and represented an improvement over the utility's previous manual, less rigorous approach to data science and modeling.
Video: http://videos.re-work.co/videos/464-agile-deep-learning
Deep Learning has been called the ‘new electricity’ — transforming every industry. Innovative architectures and applications receive deserved attention. But to turn innovation into value requires integrating deep learning into practical technology products. Such products, including Spotify's, are often developed following the principles of agile. This talk focuses on approaching deep learning in an agile way and on integrating deep learning into the agile cadence of a modern software development organization.
Effective Instrumentation Strategies for Data-driven Product Management Pawan Kumar Adda
Everyone wants to drive product decisions based on data. But that is the end goal, an intent. This goal needs a strategy and sustainable execution plan that would empower the companies and its employees to become data informed while making decisions. Enter Product Instrumentation. In this session, we will explore what is product instrumentation, why it is needed, and how you can get started with it.
SDD2017 - 03 Abed Ajraou - putting data science in your business a first uti...Dario Mangano
This document discusses putting data science solutions into business practices. It emphasizes the importance of starting with a clear business problem rather than just focusing on the data. It also recommends adopting the right technology, mindset, and methodology. For methodology, it advocates an iterative approach using techniques like exploratory analysis, feature engineering, and machine learning algorithms like gradient boosting. It also discusses automating machine learning tasks and gaining efficiency through collaborative data science platforms.
A practical guide for startups to drive growth and innovation.
Denver Startup Week Product Track presentation by Argie Angeleas, Taylor Names, Matt Reynolds
Product Management in the Era of Data ScienceMandar Parikh
My slide-deck from a webinar on the same topic for the Institute of Product Leadership, April 4th, 2017
What does it take to build killer products in the “AI-first” era? What makes for a great Data Science-driven product and how do great Product Managers leverage Data Science to drive value for customers? Find out how to avoid the pitfalls of hype-chasing Data Science tactics. Learn how to work with Data Science and Engineering to build a compelling product and solve real problems.
Mandar takes a practitioner’s approach to present his recipe for success for building Data Science-driven products that drive enduring value for customers.
Managing an Experimentation Platform by LinkedIn Product LeaderProduct School
Main Takeaways:
-Establishing a culture of experimentation at scale
-Developing the product vision and strategy
-Backlog prioritization based on Impact Score formula
This talk will focus on Techniques, metrics and different tests (code, models, infra and features/data) that help the developers of machine learning systems to achieve CD.
The product development cycle for startups - everything from coming up with an idea,to validating it, building it, launching it, and measuring how well the thing you built performed against your hypothesis!
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Your Roadmap, Your Product Story & Datadriven Product ManagementProduct School
From this presentation you will find out more about becoming a Data-Driven Product Manager.
Get a FREE copy of our Product Book here: https://prdct.school/2BSES8J
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
How to be a Good Machine Learning PM by Google Product ManagerProduct School
In this presentation you will learn:
-Machine Learning definition and the different types of problems it can solve
-Framework to decide if your specific problem could or should be solved with Machine Learning
-The role that a Product Manager plays in each part of the Machine Learning lifecycle
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Elemica
The document discusses master data management (MDM). It defines MDM as combining data governance practices with software tools to achieve a single version of the truth across systems. It then lists several market trends driving increased adoption of MDM, including MDM in the cloud, growing MDM software sales, rising information volumes, increased recognition of data's importance, and costs of poor data quality. The document also outlines how MDM can generate value in areas like customer/supplier relationships, engineering productivity, inventory costs, and procurement costs. Finally, it discusses common data issues that MDM can help solve and provides examples of potential solutions.
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
BDW17 London - Abed Ajraou - First Utility - Putting Data Science in your Bus...Big Data Week
Data Science is now well established in our businesses, and everyone considers data as a key asset and critical for our competitiveness.
However, Data Science is not easy to manage, very often projects failed and the investment made is not seeing as profitable.
The aim of this talk is to share the knowledge in different areas:
* avoid classical mistakes in Data Science
* use the right Big Data technology
* apply the right methodology
* make the Data Science team more efficient
Leveraging AI the Right Way (for Product Managers)David Murgatroyd
Artificial Intelligence is transforming almost every kind of product as innovative techniques receive deserved attention. But careful leadership from Product Managers is crucial in turning that innovation into something that’s not only valuable but that also respects your own values. This talk provides frameworks to identify where AI can impact our products in the ways we want and to maximize that impact throughout the product life cycle.
Applying machine learning to a particular business need becomes more straightforward with each technological advance. But today’s businesses have a variety of needs which are too numerous to be addressed one-at-a-time and too different to be addressed one-size-fits-all. We examine three significant challenges to building an effective ML portfolio and ways to address them thru the framework of the ML product lifecycle.
Delivered at the 2017 Missions Conference of Park Street Church, Boston
Summary:
* In deciding if we're using tech well, ask if it's improving our relationship with Our Loved Ones, Our Skills and Gifts, Our Bodies, Our World, and Our God
* In deciding if our building tech is improving lives, ask if it's doing so for our users, our team, and ourselves.
* The way to build tech well, is to Know God better than Tech, Choose employers based on values, Seek purpose, not just craft or team, and Consider who’s underserved
Think about these things when choosing a job, especially in technology:
Purpose
Mastery
Autonomy
(these first three were well articulated by Daniel Pink in his book Drive)
Culture
Domain
Effectiveness
Compensation
The document discusses challenges and opportunities for combining multiple human language technology (HLT) systems to reduce errors. It provides an example of combining name matching systems, where the existing system is supplemented by a new system. The key points are:
1) Combining systems from different technologies can reduce errors by benefiting from each system's strengths.
2) The new system should address the same task as the existing system but use a different approach to find matches the existing system misses.
3) Systems should be combined when the existing system's error types are known and the new system can be easily integrated without destabilizing the overall system.
We all know normalization is crucial to delivering high quality search results. We don’t want uninteresting variations between the query and the document to lead to missed hits (e.g., “celebrity” v. “celebrities”). Normalization of dictionary words is well understood, but what if your application focuses on names? Whether you’re tackling patent examination, sports records, e-commerce, watchlist screening or many other topics, names are often the key. Can your users find “Abdul Jabbar, Karim” if they search for “Kareem AbdalJabar” or “كريم عبد الجبار”? Solr application architects have attempted to address this through custom integration of nickname lists, edit distance, case normalization, phonetic encoding and n-grams (see example #1 or example #2), but doing so requires significant effort and may not address all desired variations. A simpler approach is to use a Solr field type for names that handles these linguistic nuances behind-the-scenes. We’ll talk about how we built this sort of field type via a Solr plug-in for the Rosette Name Indexer. We’ll also discuss examples of use cases this has enabled, how it can be tuned if necessary, and how it connects to the broader trend of entity-centric search.
Linguistic Considerations of Identity Resolution (2008)David Murgatroyd
Identity resolution systems indicate if two individuals really are the same person. Identity retrieval systems help you find the individual you’re after. These systems appear anywhere from analysts’ desks to border crossings. But how do can you tell if a system's any good before it's deployed? You need to understand the problems it should tackle and how to measure how well it’s doing.
This talk considers metrics and data for evaluating identity resolution and retrieval systems. It also explores the linguistic challenges these systems face.
Entity extraction finds names in documents, providing important raw material for big decisions. But finding all mentions of the name “George Bush” is very different than finding all mentions of the 43rd US President. Making big decisions from big data is hopeless unless analytics advance from providing snippets of text to providing statements of truth. Such advances present challenges both of accuracy and of usability. We’ll explore these challenges and demonstrate ways of addressing them.
http://basistechweek.com/hlt.html
There's never been a more exciting time to be involved in Human Language Technology (HLT). Advances in algorithms, architectures, and applications are making real differences in fulfilling missions around the world. We'll use the perspective of one specific, end-to-end use case starting from primary source collection going all the way through finished intelligence to show the value and importance of moving your HLT thinking from strings to things, from configuration to adaption, from isolation to collaboration, and from small scale to Big Text. This perspective will serve as a guide to the other talks of the day which together will give you greater insight in applying HLT to your mission.
Measuring the Impact of Network Latency at TwitterScyllaDB
Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.
Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment.
How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
Advanced Techniques for Cyber Security Analysis and Anomaly DetectionBert Blevins
Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
Visit- https://onliveserver.com/linux-web-hosting/
Best Practices for Effectively Running dbt in Airflow.pdfTatiana Al-Chueyr
As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.
This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
1. How to Train
Your Product Owner
David Murgatroyd ( @dmurga)
MassTLC ML Dev Day
January 24, 2018
(please don’t sue me for copyright infringement, DreamWorks!)
3. Agile & Machine Learning
David Murgatroyd (@dmurga)
(ML Chapter Lead in Quest)
Your Product Owner
4. Agile & Machine Learning
David Murgatroyd (@dmurga)
(ML Chapter Lead in Quest)
Your Product Owner
Your Product
6. @dmurga
What do Product Owners Do?
Lead team in:
‣ establishing vision/hypotheses for
what product should become
‣ prioritizing work to get there
7. @dmurga
What do Product Owners Do?
Lead team in:
‣ establishing vision/hypotheses for
what product should become
‣ prioritizing work to get there
Your Product
10. @dmurga
Outside In … Now to Then
Machine Learning
Machine Learning
Your Product
Your Future Product
11. @dmurga
Outside In … Now to Then
Machine Learning
Problem Metrics Data Models Prioritizing OrganizingDesign
Machine Learning
Your Product
Your Future Product
15. @dmurga
Problem Metrics Data Models
Picking a Problem: Tweak It
Design
‣ What’s the business goal of your
product?
‣ What fuzzy decision does it make
that impacts that goal?
Your Product
16. @dmurga
‣ Perception: a person can do it in
less than a second.
‣ Prediction: done over and over
‣ Personalization: similar need but
met in different ways
Problem Metrics Data Models
Picking a Problem: Think It
Design
Your Product
?
19. @dmurga
1. The role of ‘why’ of ML output?
2. Coping with errors?
3. Designing for varied output?
4. New user behaviors that ML
enables?
Problem Metrics Data Models
Designing with ML
Design
Your Product
20. @dmurga
Design
‣ Trading off why vs. right
‣ Granularity of ‘why’
‣ Creepy vs. personal
‣ First step: designer gives concrete
example on concrete data
Problem Metrics Data Models
Design: the role of ‘why’
Your Product
21. @dmurga
‣ What quality is needed to user
maintain trust or delight them?
‣ Rank multiple outputs?
‣ Provide fallback behavior?
‣ Explicit feedback mechanisms?
Problem Metrics Data Models
Design: coping with errors
Design
Your Product
22. @dmurga
‣ Get realistic output samples from a
variety of users (e.g., synthetic,
persona)
‣ Watch for subtle assumptions
Problem Metrics Data Models
Design: coping with variety
Design
Your Product
23. @dmurga
‣ Users might anthropomorphize ML
Products
‣ Users might express more of
themselves or test the limits of the
systems
‣ Avoid “user bubbles” by encouraging
discovery and crafting metrics
Problem Metrics Data Models
Design: new user behavior
Design
Your Product
24. @dmurga
‣ Alignment to business value
‣ Effort to measure
‣ Useful for team’s decisions
‣ Useful for model’s training
Problem Metrics Data Models
Metrics: Properties
Design
Your Product
Machine Learning
25. @dmurga
Problem Metrics Data Models
Metrics: Matrix of Metrics
Design
Where /
What
Heuristic Modeled
Online Online Heuristic Online
Modeled
Offline Offline Heuristic Offline
Modeled
Your Product
Machine Learning
26. @dmurga
Problem Metrics Data Models
Metrics: Online vs Offline
Design
Online More aligned with business value
Offline Generally less effort to measure
Your Product
Machine Learning
27. @dmurga
Problem Metrics Data Models
Metrics: Heuristic vs Modeled
Design
Heuristic Modeled
More useful for
team’s decisions
interpretable
More useful for
training the model
directly
Your Product
Machine Learning
28. @dmurga
Problem Metrics Data Models
Metrics: Matrix of Metrics
Design
Where /
What
Heuristic Modeled
Online Online Heuristic Online
Modeled
Offline Offline Heuristic Offline
Modeled
Your Product
Machine Learning
29. @dmurga
‣ Just look at some data before/after
‣ Alignment to business value: OK
‣ Effort to measure: OK (one-off)
‣ Useful for team’s decisions: OK
‣ Useful for model’s training: Bad
Problem Metrics Data Models
Metrics: … vs Subjective
Design
Your Product
Machine Learning
30. @dmurga
1. What data do you need?
2. Where can you get it?
3. What biases does it carry?
Problem Metrics Data Models
Data
Design
Your Product
31. @dmurga
‣ Raw input data
‣ Metadata / reference
‣ Has metadata that is a source for
measurement
Problem Metrics Data Models
What data do you need?
Design
Your Product
32. @dmurga
‣ Data is the new Wireframe
‣ Product Owner provides example
inputs / outputs
‣ Use these to also vet metrics
Problem Metrics Data Models
Specifying with Data
Design
Your Product
Machine Learning
33. @dmurga
‣ Logging
‣ Proxies and other 3rd Parties
‣ Annotation
‣ Watch out for drift in the data set
‣ Take advantage of PO’s domain
knowledge!
Problem Metrics Data Models
Where can you get it?
Design
Your Product
34. @dmurga
‣ Is it skewed?
‣ Is it tainted?
‣ Is it likely to stereotype?
Problem Metrics Data Models
What biases does it carry?
Design
Your Product
35. @dmurga
1. Teach tasks rather than techniques
2. Teach trade-offs rather than tools
3. Start simple
Problem Metrics Data Models
Models
Design
Your Product
Machine Learning
36. @dmurga
‣ Classification
‣ Clustering
‣ Regression
‣ (and Embeddings if you must :- )
Problem Metrics Data Models
Models: Tasks
Design
Your Product
Machine Learning
37. @dmurga
‣ Simplicity
‣ Interpretability
‣ Confidence
‣ Accuracy
‣ Adaptability
‣ Speed
‣ Space
‣ Scale
Problem Metrics Data Models
Models: Trade-offs
Design
Your Product
Machine Learning
38. @dmurga
‣ Rules before baseline models
‣ Baselines before adapted models
‣ Adapted models before end-to-end
models
Problem Metrics Data Models
Models: Err toward Simplicity
Design
Your Product
Machine Learning
40. @dmurga
Outside In … Now to Then
Machine Learning
Problem Metrics Data Models Prioritizing OrganizingDesign
Machine Learning
Your Product
Your Future Product
47. @dmurga
Prioritizing: Prod Lifecycle
Prioritizing Organizing
Your Backlog
Stage Characteristics
Exploration by hand examples/rules
Pre-MVP (0.1%
/ early Beta)
measurable & inspectable
MVP (1%, Beta) accurate, not slow, &
documented
v1 (100% / GA) simple & fast
Post-v1 handle new domains
48. @dmurga
Prioritizing: Goals (OKRs)
Prioritizing Organizing
Your Backlog
Stage OKRs
Exploration amount of analysis
Pre-MVP (0.1%
/ early Beta)
having metrics, amount of
experiments
MVP (1%, Beta) moving core metrics,
amount of experiments
v1 (100% / GA) moving all metrics
Post-v1 moving all metrics on new
data
52. @dmurga
Organizing: Team Structure
Organizing
‣ Aligned to Product/Org Maturity
‣ Exploration: Centralized Team
‣ Pre-MVP: Cross-functional
‣ MVP to v1: Cross-functional with
separate work stream
Your Backlog
Prioritizing
53. @dmurga
Organizing: Team Structure
Prioritizing Organizing
‣ Aligned to Product/Org Maturity
‣ Exploration: Centralized Team
‣ Pre-MVP: Cross-functional
‣ MVP to v1: Cross-functional with
separate work stream
‣ Post v1: Dedicated Sibling Team
Your Backlog
54. @dmurga
‣ Applied ML Eng
‣ ML Tool Eng
‣ Core Researcher
Organizing: Roles
Prioritizing Organizing
55. @dmurga
Outside In … Now to Then
Machine Learning
Problem Metrics Data Models Prioritizing OrganizingDesign
Machine Learning
Your Product
Your Future Product
56. Thanks! Questions?
David Murgatroyd (@dmurga)
Suggestions:
What about different kinds of testing?
What are common features ML-based products have?
More on identifying metrics?
Machine Learning vs. Data Science?
Hiring in Boston,
NYC, London,
and Stockholm!