![]() It’s a first step to make sure you connect what ML can do to your organization’s objectives, and towards assessing feasibility. The ML Canvas lets you lay down your vision for your ML system and communicate it with your team. This is where you’ll specify methods and metrics to evaluate the system after deployment, and to quantify value creation. The lower left and right blocks relate to the “predictive engine” and its constraints, in terms of latency and throughput for making predictions and updating models.įinally, the last part of the canvas is dedicated to measuring how well the system works, on the domain side (“ Live Evaluation and Monitoring”). The upper left and right blocks relate to domain integration: how predictions are used and how data is collected in the domain of application. The top of the canvas provides more of a background view and the bottom goes into the specifics of the system. Building models: When do we create/update models with new training data and how long do we have for that?.Features: Input representations to extract from raw data sources.Collecting data: How do we get new data to learn from (inputs AND outputs)?.Data sources: Which raw data sources can we use?.The part on the right-hand side is dedicated to Learning from data. Offline evaluation: Which methods and metrics can we use to evaluate the way predictions are going to be made and used, prior to deployment?.Making predictions: When do we make predictions on new inputs and how long do we have for that?.Decisions: How are predictions used to make decisions that provide the proposed value to the end user?.classification, regression…), what is the input, and what is the output to predict (along with possible values)? ![]() The part on the left-hand side is dedicated to Predictions, based on the models that we’ll learn from data. Then there’s the How, which can be split in two parts: learning and making predictions. You can think of it as the What+Why+Who: What are we trying to do, Why is it important, and Who is going to use the system / be impacted by it. It starts with a central block dedicated to the Value Proposition of the system where ML is going to be used. The Machine Learning Canvas allows to describe precisely this. How are we making sure that the whole thing “works” through time?.How are we using predictions powered by that learning.In the context of data and Artificial Intelligence, a canvas can be useful to describe the actual learning that takes place in intelligent systems: They are just visual charts to describe complex objects in a better way than a simple text document: each key component has its own block and blocks are arranged on the chart in a way that makes sense visually (based on their proximity). the Culture Creation Canvas and the Mobile stickiness canvas). It provides an overview of this complex object that a Business Model is, and facilitates collaboration.Ĭanvases have also been used for completely different purposes, with different layouts/structures (e.g. CanvasesĬanvases are very popular in the startup community, starting with the hugely popular Lean Canvas, which is itself derived from the Business Model Canvas. One way to make collaboration easier is to use a canvas. data science, engineering, product, business) need to team up to build something innovative that creates value. This is a general problem in any endeavor where people of different backgrounds (e.g. And when they do work on the right problems, it’s a challenge to align everyone’s activities. It’s not uncommon to realize that time is being spent by engineers and scientists to solve the wrong problems and to build models that don’t get used. In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization’s objectives. which promotional offers to give to which customers, to make them stay). These predictions only become valuable when they are used to inform or to automate decisions (e.g. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the “churn” problem). At their core, they ingest data in a certain format, to build models that are able to predict the future.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |