How to transform businesses with ML in 2025
OWNML METHOD provides strategic knowledge and tools to create successful business solutions with Machine Learning, in accelerated time.
Excellent ★★★★★ 5/5 (see reviews)
Self-paced course
50+ video lessons (17h)
Team access
to boost collaboration
Practical tools
to support your journey
An advanced program to create business solutions with ML
87% of ML projects don’t make it to production. Don’t let this happen to you! OWNML METHOD is a complete program to equip your team with Machine Learning skills and tools to create transformative solutions in accelerated time.
Everything you need to know
About OWNML METHOD
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OWNML METHOD is a unique methodology based on 15 years of expertise, to create transformative business solutions with Machine Learning.
It is taught via an online training program that contains 17 hours of video lessons, packed with practical and actionable content. Your team can watch at their own pace and have lifetime access. This gives them the time they need to put what they learn into practice, using the provided tools.
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You will initiate ML projects aligned with business objectives and your team will take them to production in record time. This is possible thanks to a step-by-step methodology based on unique tools and taught with concrete examples.
On an individual level, your team members will become key figures for transformational ML projects in your organization.
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This program was designed for transformation and innovation teams in companies of > 1K employees looking to exploit the value of their data and the potential of ML for their organization.
It’s accessible to non-data experts, and no prior knowledge is required.
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When you enroll your team in OWNML METHOD, you’re fully protected by our Satisfaction Guarantee. If you don’t feel like you’ve received value and decide to cancel within the next 14 days, just let us know, and we’ll immediately refund you the full amount (minus processing fees of 2.9% if you’re paying by credit card).
Your instructor shares his hard-earned experience
Louis Dorard is the author of the Machine Learning Canvas. He has distilled 15 years of practical experience into the OWNML METHOD. He trained 500+ professionals in person and organized 11 industry ML conferences with 1,000s attendees from 30+ countries.
Louis worked as Senior Solutions Engineer and Manager at Dataiku, as Teaching Fellow at UCL School of Management, and as an independent ML consultant (see some of his references below). He holds a PhD in ML.
Why follow OWNML METHOD?
Step-by-step process
Find the best ML use cases for your organization. Write detailed specifications and a bulletproof implementation plan. Follow a cross-industry standard process broken down into 9 phases of 4 tasks.
Exclusive tools and guides
Get access to our unique planning and management tools (Prediction Task Canvas, ML Canvas, ML Project Checklist, Modeling Workflow, and ML System Architecture diagrams) plus complete how-to guides.
Practical content
Case studies, demo projects, and explanations on how they were created. Hands-on videos to understand implementation concepts.
Contents
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Align possibilities with business objectives. Find Machine Learning use cases that maximize impact & feasibility.
Introduction to ML
What is Machine Learning?
No-code ML demos & example use cases
Concepts and terminology
Possibilities and limitations of ML models
Types of use cases that create great value
Detailed use cases
What is Deep Learning?
When ML fails
ML value framework
Value Propositions powered by ML
Connecting predictions to value propositions
The Prediction Task Canvas
Monitoring value in production
[New] Process Mining: finding ML opportunities in your processes
[New] Strategies for delivering ML solutions to your users’ doorstep
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Master the Machine Learning Canvas and turn your ML solution idea into a detailed plan. This course will teach you the techniques that innovation teams have used to change the way they think about ML project planning.
Introduction to the Machine Learning Canvas
Why use the MLC?
Overview of the 10 boxes that make up the MLC
Structure of the MLC
Value vs cost of ML solutions: anticipating costs with the MLC
Listing requirements with the MLC
Data for ML: features, sources, and collection
Impact simulation
Building models and making predictions
Towards a flawless design
4 common mistakes with the MLC
Dealing with feedback loops
What a bullet-proof MLC looks like (+ case studies)
Characteristics of a great MLC
Hands-on (remote) collaboration tips: cloud document vs virtual whiteboard
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Avoid pitfalls and prioritize work efficiently with the OWNML Checklist. This tells you, in detail, what you need to do and in which order. End-to-end ML projects are broken down into 9 phases of 4 tasks each. Lessons:
ML pipelines: understanding the structure of data pipelines that create ML models.
Software components that need to be built when creating an ML system: architecture diagram + explanations.
The investor’s approach to ML projects: principles behind the checklist, used to prove value early while minimizing risks and costs of ML projects.
How to use the checklist + detailed explanations of what it contains.
Workflow of a model builder.
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Our methodology to implement a Minimum Viable Product with no-code/low-code and minimize time to value.
DATA: create your training dataset
Common data aggregation and transformation operations
Create feature sets and label sets while preventing data leakage
Prepare data for ML with a no-code data wrangler
[New] Low-code data wrangling in Python/SQL with notebooks and GenAI
BASELINE: create your own models in record time with AutoML
Build classification and regression models with no code
How to split data into training/validation/test sets while maximizing performance & test reliability
How to deal with time in ML
Analyze predictions and errors
Select the best model
[New] Low-code modeling with notebooks and GenAI
MVM: turn your baseline into a Minimum Viable Model you can trust
Explain predictions, understand errors, and improve data prep
Parametrize & optimize data prep
Analyze model behavior & performance: from accuracy to business metrics
Optimize modeling & decisions with practical tweaks
MVP: turn your MVM into a Product / Solution
Apply your MVM to new inputs
Automate predictions
Batch vs real-time predictions
Deploy to production
Deliver predictions & recommended decisions with simple end-user interfaces
Build a dashboard to monitor prediction accuracy and KPIs
Automate model retraining at the right frequency
Implement feedback loops
Tips from a Product Manager to get traction and grow usage
“This framework has helped us expedite the entire process from use case idea to MVP deployment, beyond our expectations.”
OWNML METHOD (2025)
Our reference training program: equip your team with the skills and tools to create transformation business solutions with Machine Learning.
Up to 10 licenses with lifetime access & course updates
4 modules on all steps from discovery to implementation
Online platform with 17h of videos + slides + summaries
FAQ
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ONWML pioneered industry-leading tools such as the Machine Learning Canvas, which is used by over 10,000 companies. The METHOD teaches how to use it and our other exclusive tools to build high-value Machine Learning solutions for business in accelerated time.
Our content condenses knowledge that was built from years of experience. Your time is valuable, and you’ll save tons of it:
Focus on what matters most in a business context. Skip technicalities while keeping important concepts in mind.
Avoid mistakes that could cost months of work to you and your team.
Get answers to your most important questions.
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OWNML METHOD targets innovators and business transformation professionals. There is no technical requirement beyond familiarity with spreadsheet software such as Excel.
You’ll gain the knowledge and skills to find the best ML use cases for your organization and implement Minimum Viable Products with no-code tools.
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This training program focuses on transforming your organization by creating and using your own predictive models, trained on your own tabular data. In this sense, it can be considered “Artifical Intelligence”. It also focuses on tabular data (i.e. Excel-like data) and on two types of models: classification and regression.
This program is not about using pre-trained AI models.
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This training program focuses on tabular data, commonly found in IT systems that support businesses and operational processes across various industries and departments.
Examples per industry:
Retail & CPG: Point-of-sale (POS) systems, inventory management, and loyalty program databases.
Manufacturing: Manufacturing Execution Systems (MES), Quality Management Systems (QMS), and supply chain data.
Banking / Financial Services: Core banking systems, transaction records, fraud detection systems, and risk management platforms.
Insurance: Claims management systems, policy administration data, and underwriting records.
Logistics: Warehouse Management Systems (WMS), Transportation Management Systems (TMS), and fleet management data.
Software: Product usage analytics, customer support ticketing systems, and subscription billing platforms.
Examples per department:
Marketing: Data from Customer Relationship Management (CRM) systems, campaign performance metrics, and customer segmentation.
Supply Chain: Enterprise Resource Planning (ERP) data, demand planning systems, and supplier performance tracking.
Finance: General ledger systems, budgeting and forecasting tools, and financial reporting platforms.
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Yes! You can contact us to request an invoice before or after making your payment. The invoice will be from a French company, it will state the amount in USD and its conversion in Euros. There will be no added sales tax.
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Yes! Payment can be made in USD or in Euros. Please contact us to let us know your preference and the bank details you need (e.g. ACH, Wire, IBAN, SWIFT).
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Yes! Please get in touch.
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The pricing given on this webpage is for a bundle of up to 10 licenses.
You will be given individual access to the platform via the email address used for registration (which must be tied to a specific individual and not role-based). Please reply to the confirmation email with a list of names and email addresses for your team members, and they’ll be given access to the platform. They’ll be able to choose a password when logging in for the first time.
Please note that our platform can track individual progress and issue named course completion certificates if you request them.
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Sure. Contact us!