In this keynote I will give you a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities. I'll present new technology that makes ML more accessible, and I'll explain in simple terms the limitations to what can be achieved. Finally, I'll discuss pragmatic considerations of real-world applications and I'll give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.
Prediction APIs are democratizing Machine Learning. They make it easier for developers to build smart features in their apps by abstracting away some of the complexities of building and deploying predictive models. In this talk we’ll look at the possibilities and limitations of ML, how to use Prediction APIs, how to prepare data to send to them, and how to assess performance.
In this presentation we’ll see current use cases of Artificial Intelligence in the form of tools and of high-stakes autonomous systems. We’ll see...
- how Machine Learning-powered predictions are used to make decisions
- when AI alone can make better decisions that humans
- whether that’s enough to trust AI to be autonomous.
(Présentation donnée au Forum de l'Intelligence Artificielle à Bordeaux - slides en Anglais)
Des exemples de use cases dont vous pourrez vous inspirer, et de plateformes de ML-as-a-Service pour vous faciliter le human learning du machine learning, l'expérimentation, et le déploiement en production!
In this presentation, we’ll go over real-world use cases of Machine Learning and Artificial Intelligence in web and mobile applications, and we’ll explain how they work. We’ll discuss opportunities for startups in all domains to create value from data (big or small) and to create innovative, predictive features in their applications.
We’ll review existing technologies that make Machine Learning accessible, in particular with automatic selection of algorithms, auto-tuning of parameters, and auto-scaling. Deep Learning (a subset of Machine Learning techniques which is getting a lot of press due to recent advances and successes) is also being made accessible without costly hardware and, in certain cases, without requiring specialized knowledge.
The main message for developers is that they can easily use the power of machine intelligence without having to rely on a team of Data Scientists. This will be illustrated in more detail with concrete use cases: priority detection and image categorization.
Future of AI-powered automation in businessLouis Dorard
Starting from examples of current use cases of AI in business and in everyday life, we'll see what the future holds and we'll mention questions to address when giving autonomy to intelligent machines. We'll also aim at demystifying how AI works, in particular how machines can use data to automatically learn business rules and actions to perform in different contexts.
Pragmatic machine learning for the real worldLouis Dorard
A description of machine learning and prediction APIs followed by some real-world considerations on the deployment of predictive models and their integration in apps and businesses. These are illustrated with a churn prediction example.
In this presentation you'll find some information about Microsoft Azure Machine Learning and how it compares to PredictionIO, an open source solution for creating prediction servers. I also gave an exclusive presentation of the Machine Learning Canvas, which you must fill in before any attempt at implementing a predictive system!
Video coming soon...
How can you ensure that your work and use of ML gets the most impact in the domain you apply it to? From collaborating with all stake-holders to simulating how predictions will really be used, evaluating them domain-side and deploying models at scale in production, I’ll share some of the lessons I’ve learnt when it comes to integrate ML in real-world applications. Also, I’ll review some research problems and new open source software aimed at making it easier to create, experiment with, and operationalise predictive models.
Le Big Data entre dans une nouvelle phase où le prédictif est roi. Plutôt que de chercher à collecter une big quantité de données, on se concentre maintenant sur comment utiliser les données de façon à avoir un big impact. D'autant plus que les technologies Big Data deviennent désormais accessibles à des experts métiers qui peuvent les mettre à profit dans leurs domaines respectifs.
Présentation donnée le 11 juin 2014 au Node à Bordeaux lors de la 1ère #datanight.
Prediction APIs are democratizing Machine Learning. They make it easier for developers to build smart features in their apps by abstracting away some of the complexities of building and deploying predictive models. In this talk we’ll look at the possibilities and limitations of ML, how to use Prediction APIs, how to prepare data to send to them, and how to assess performance.
In this presentation we’ll see current use cases of Artificial Intelligence in the form of tools and of high-stakes autonomous systems. We’ll see...
- how Machine Learning-powered predictions are used to make decisions
- when AI alone can make better decisions that humans
- whether that’s enough to trust AI to be autonomous.
(Présentation donnée au Forum de l'Intelligence Artificielle à Bordeaux - slides en Anglais)
Des exemples de use cases dont vous pourrez vous inspirer, et de plateformes de ML-as-a-Service pour vous faciliter le human learning du machine learning, l'expérimentation, et le déploiement en production!
In this presentation, we’ll go over real-world use cases of Machine Learning and Artificial Intelligence in web and mobile applications, and we’ll explain how they work. We’ll discuss opportunities for startups in all domains to create value from data (big or small) and to create innovative, predictive features in their applications.
We’ll review existing technologies that make Machine Learning accessible, in particular with automatic selection of algorithms, auto-tuning of parameters, and auto-scaling. Deep Learning (a subset of Machine Learning techniques which is getting a lot of press due to recent advances and successes) is also being made accessible without costly hardware and, in certain cases, without requiring specialized knowledge.
The main message for developers is that they can easily use the power of machine intelligence without having to rely on a team of Data Scientists. This will be illustrated in more detail with concrete use cases: priority detection and image categorization.
Future of AI-powered automation in businessLouis Dorard
Starting from examples of current use cases of AI in business and in everyday life, we'll see what the future holds and we'll mention questions to address when giving autonomy to intelligent machines. We'll also aim at demystifying how AI works, in particular how machines can use data to automatically learn business rules and actions to perform in different contexts.
Pragmatic machine learning for the real worldLouis Dorard
A description of machine learning and prediction APIs followed by some real-world considerations on the deployment of predictive models and their integration in apps and businesses. These are illustrated with a churn prediction example.
In this presentation you'll find some information about Microsoft Azure Machine Learning and how it compares to PredictionIO, an open source solution for creating prediction servers. I also gave an exclusive presentation of the Machine Learning Canvas, which you must fill in before any attempt at implementing a predictive system!
Video coming soon...
How can you ensure that your work and use of ML gets the most impact in the domain you apply it to? From collaborating with all stake-holders to simulating how predictions will really be used, evaluating them domain-side and deploying models at scale in production, I’ll share some of the lessons I’ve learnt when it comes to integrate ML in real-world applications. Also, I’ll review some research problems and new open source software aimed at making it easier to create, experiment with, and operationalise predictive models.
Le Big Data entre dans une nouvelle phase où le prédictif est roi. Plutôt que de chercher à collecter une big quantité de données, on se concentre maintenant sur comment utiliser les données de façon à avoir un big impact. D'autant plus que les technologies Big Data deviennent désormais accessibles à des experts métiers qui peuvent les mettre à profit dans leurs domaines respectifs.
Présentation donnée le 11 juin 2014 au Node à Bordeaux lors de la 1ère #datanight.
Simple machine learning for the masses - Konstantin DavydovPAPIs.io
Using Google's Cloud Machine Learning Services, users can set up an entire Machine Learning pipeline quickly and with limited or no Machine Learning expertise. It is also possible to build applications on top of the Prediction API that allow for non-technical users to leverage the power of Machine Learning to help solve real world problems.
By using black-box Machine Learning via Google’s Machine Learning Services, it is possible to build an end-to-end Machine Learning pipeline with little to no ML expertise. The service automatically handles complex tasks such as data preprocessing, feature selection, classifier selection, parameter tuning, model evaluation, model hosting, and model updating.
As an example of the type of apps that can be built on top of the Prediction API, SmartAutofill spreadsheets add-on allows for easy, one-click application of Machine Learning directly from a Google spreadsheet.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
From Data to AI with the Machine Learning CanvasLouis Dorard
The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities.
This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I’ll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We’ll see how to fill it in iteratively on a churn prevention example.
An introduction to Machine Learning and how to use it in your apps, thanks to Predictive APIs that abstract away part of the complexity. In this talk, I present the different types of Predictive APIs there are and I also show how one can create their own based on open source software.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
Simple machine learning for the masses - Konstantin DavydovPAPIs.io
Using Google's Cloud Machine Learning Services, users can set up an entire Machine Learning pipeline quickly and with limited or no Machine Learning expertise. It is also possible to build applications on top of the Prediction API that allow for non-technical users to leverage the power of Machine Learning to help solve real world problems.
By using black-box Machine Learning via Google’s Machine Learning Services, it is possible to build an end-to-end Machine Learning pipeline with little to no ML expertise. The service automatically handles complex tasks such as data preprocessing, feature selection, classifier selection, parameter tuning, model evaluation, model hosting, and model updating.
As an example of the type of apps that can be built on top of the Prediction API, SmartAutofill spreadsheets add-on allows for easy, one-click application of Machine Learning directly from a Google spreadsheet.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
From Data to AI with the Machine Learning CanvasLouis Dorard
The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities.
This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I’ll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We’ll see how to fill it in iteratively on a churn prevention example.
An introduction to Machine Learning and how to use it in your apps, thanks to Predictive APIs that abstract away part of the complexity. In this talk, I present the different types of Predictive APIs there are and I also show how one can create their own based on open source software.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
This session is based on my research and experiments in Prompt Engineering and is 100% relevant for cloud developers who investigate adding some LLM-powered features to their solutions. It's a guide to building proper prompts for AI to get desired results fast and cost-efficient.
A business level introduction to Artificial Intelligence - Louis Dorard @ PAP...PAPIs.io
Artificial Intelligence and Machine Learning are becoming increasingly accessible. Starting from example use cases, I’ll aim at demystifying how they work and how they improve businesses in 3 areas: increasing the number of customers, serving them better, and serving them more efficiently. I’ll show how machines can use data to automatically learn business rules and make predictions, that can then be used to make better decisions. I’ll introduce the main concepts of ML, its possibilities, its limitations, and I’ll give tips on framing the right problems for your company to tackle.
Louis Dorard is the author of Bootstrapping Machine Learning, a co-founder of PAPIs, and an independent consultant. His goal is to help people use new machine learning technologies to make their apps and businesses smarter. He does this by writing, speaking and teaching.
This is the presentation by Architect Simon Ha, from Steinberg Architects, that accompanied a webinar discussing the following issues:
Learn the design, economic, and policy issues in high-density multi-family housing design in podium (light wood frame over concrete) construction and high-rise construction.
In many of our growing cities, there has been a proliferation of medium to high density housing production. The form of the new housing is typically five to seven stories with a mix of Type I concrete podium at the base and Type V or III light wood construction above.
The webinar originally aired on 11/6/17
Take the next big step in big data: designing a user experience that enables end users to easily understand and consume information and insights. Presented at the BigData Summit conference in Kansas City, November 2014.
Rethinking residential real estate: 2023 and beyondAppFolio
The past few years have accelerated underlying trends in how people live, work, and play within the built environment. At the same time, technology has democratized access to capital and information, changing the way residents use physical space. Value is shifting away from the assets themselves toward those who understand the needs of specific end users and can use technology to deliver comprehensive, on-demand solutions. With all of these changes, there is an urgent need for operators to think differently about their properties, customers, and competition.
In this webinar, Dror Poleg shares his perspectives based on two decades of experience working across four continents alongside the world’s leading real estate investors. Watch now to receive key insights on how to take advantage of emerging opportunities and transform your organization, project, venture, or career.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
14. –McKinsey & Co. (2011)
“A significant constraint on
realizing value from big data will
be a shortage of talent,
particularly of people with deep
expertise in statistics and machine
learning.”
34. –Katherine Barr, Partner at VC-firm MDV
"Pairing human workers with
machine learning and automation
will transform knowledge work
and unleash new levels of human
productivity and creativity."
83. • Who: SaaS company selling monthly subscription
• Question asked:“Is this customer going to leave
within 1 month?”
• Input: customer
• Output: no-churn (negative) or churn (positive)
• Data collection: history up until 1 month ago
• Baseline: if no usage for more than 15 days then
churn
85. Customer representation:
• basic info (age, income, etc.)
• usage of service (# times used app, avg time spent,
features used, etc.)
• interactions with customer support (how many,
topics of questions, satisfaction ratings)
86. Taking action to prevent churn:
• contact customers (in which order?)
• switch to different plan
• give special offer
• no action?
87. Measuring accuracy:
• #TP (we predict customer churns and he does)
• #FP (we predict customer churns but he doesn’t)
• #FN (we predict customer doesn’t churn but he does)
• Compare to baseline
88. Estimating Return On Investment:
• Taking action for #TP and #FP customers has a cost
• We earn #TP * success rate * revenue /cust. /month
• Compare to baseline
91. PREDICTIONS OBJECTIVES DATA
Context
Who will use the predictive system / who will be
affected by it? Provide some background.
Value Proposition
What are we trying to do? E.g. spend less time on
X, increase Y...
Data Sources
Where do/can we get data from? (internal
database, 3rd party API, etc.)
Problem
Question to predict answers to (in plain English)
Input (i.e. question "parameter")
Possible outputs (i.e. "answers")
Type of problem (e.g. classification, regression,
recommendation...)
Baseline
What is an alternative way of making predictions
(e.g. manual rules based on feature values)?
Performance evaluation
Domain-specific / bottom-line metrics for
monitoring performance in production
Prediction accuracy metrics (e.g. MSE if
regression; % accuracy, #FP for classification)
Offline performance evaluation method (e.g.
cross-validation or simple training/test split)
Dataset
How do we collect data (inputs and outputs)?
How many data points?
Features
Used to represent inputs and extracted from
data sources above. Group by types and
mention key features if too many to list all.
Using predictions
When do we make predictions and how many?
What is the time constraint for making those predictions?
How do we use predictions and confidence values?
Learning predictive models
When do we create/update models? With which data / how much?
What is the time constraint for creating a model?
Criteria for deploying model (e.g. minimum performance value — absolute,
relative to baseline or to previous model)
IDEASPECSDEPLOYMENT
94. PREDICTIONS OBJECTIVES DATA
BACKGROUND End-user Value prop Sources
ENGINE SPECS ML problem Perf eval Preparation
INTEGRATION Using pred Learning modelINTEGRATION Using pred Learning model
95. Why fill in ML canvas?
• Target the right problem for your company
• Choose right algorithm, infrastructure, or ML
solution
• Guide project management
• Improve team communication
98. • Need examples of inputs AND outputs
• Need enough examples
99. • ML to create value from data
• 2 phases: TRAIN and PREDICT
• Predictive APIs make it more accessible
• Good data is essential
• What do we do with predictions?
• Measure performance with accuracy, time and
bottom-line
• Also: deploy, maintain, improve…