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.

Course rating:
Excellent ★★★★★ 5/5 (see reviews)

17 hours of
video lessons

Instant and lifetime access

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 the skills and tools to create successful Machine Learning business solutions — with no prior knowledge and in accelerated time.

Everything you need to know

About OWNML METHOD

  • OWNML METHOD is an exclusive 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.

  • 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, you’ll become key figures for transformational ML projects in your organization.

  • 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.

  • 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, example ML Canvases, demo projects, and explanations on how they were created. Hands-on demos to understand implementation concepts.

Contents

  • 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

  • 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

  • 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.

  • 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.
— Hangga Prayoga, R&D Unit Leader at NYK Group

Ready to take action?

OWNML METHOD - New Year Offer (2025)
$9,750.00
One time

Our reference training program: equip your team with the skills and tools to create transformative business solutions with Machine Learning. Special offer ending January 31, 2025.


✓ 10 licenses with lifetime access & course updates
✓ 4 modules on all steps from discovery to implementation
✓ Online platform with 17h of videos + slides + summaries
✓ LIMITED AVAILABILITY: 3 x 1h coaching with Louis
✓ LIMITED AVAILABILITY: 1-year free access to OWNML-GPT beta

Interested in OWNML METHOD? Book a call with an expert.

Let’s talk about your situation and your challenges. We’ll determine if our program can support your business's transformation through Machine Learning and help you achieve your goals.

Need help?
Email alessia@ownml.co


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