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ML projects progress in phases with specific
goals, tasks, and outcomes. A clear understanding of the ML development
phases helps to establish engineering
responsibilities, manage stakeholder expectations, and efficiently allocate
resources.
Successfully moving through the phases (often iteratively) is foundational
for designing, assembling, and building MLmodelsthat solve business problems
over the long term.
At a high level, implementing an ML solution consists of the following phases:
Ideation and planning
Experimentation
Pipeline building
Productionization
Ideation and planning
During the ideation and planning phase, you frame your problem in terms of an
ML solution and estimate the project's feasibility.
Goal: To determine if ML is the best solution to your problem.
Tasks: Analyze the business problem to understand your project's
constraints.
Outcome: A design doc outlining how to solve a problem with a
ML solution.
Experimentation
Experimentation is the core of machine learning. During this phase,
you verify that an ML solution is viable. Finding a solution is an
iterative process. It's not uncommon to try hundreds of experiments
before finding the right combination offeatures,hyperparameters,
and model architecture that solves the problem.
Goal: To build a model that solves the business problem.
Tasks: Experiment with features, hyperparameters, and model
architectures.
Outcome: A model with good enough quality to put into production.
Pipeline building and productionization
During thepipelinebuilding and
productionization phase, you build pipelines
for processing data, training a model, and servingpredictions. You then
deploy the model and pipelines into production with the necessary monitoring and
logging infrastructure.
Goal: To build and implement the infrastructure for scaling, monitoring,
and maintaining models in production.
Tasks: Build pipelines to automate many of the tasks for keeping
up-to-date models in production.
Outcomes: Validated ML pipelines.
End-to-end ML workflow
The following diagram illustrates the entire end-to-end ML workflow, listing
each phase and its tasks and outcomes:
Figure 1. The four main phases of an ML workflow.
Keep in mind
Multiple challenges exist at each phase.
Not realizing—and planning for—them may lead to missed
deadlines, frustrated engineers, and failed projects.
Check Your Understanding
You've just read about some ML technology that might benefit your
product. What should you do next?
Before spending time drafting a design doc or writing code, you
should first verify that ML is the right
solution to your problem.
Correct. Before spending time drafting a design doc or writing code,
you should first verify that ML is the
right solution to your problem.
Draft a design doc outlining the ML use case and the required
infrastructure to implement it.
Before drafting a design doc, you should first verify that ML is the
right solution to your problem.
Find code examples and begin experimenting to determine if the model
can make good predictions.
Before writing a line of code, you should first verify that ML is the
right solution to your problem.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eML solutions are developed in iterative phases: ideation and planning, experimentation, pipeline building, and productionization.\u003c/p\u003e\n"],["\u003cp\u003eEach phase has specific goals, tasks, and outcomes that contribute to the overall success of the project.\u003c/p\u003e\n"],["\u003cp\u003eIdentifying the right problem for an ML solution and ensuring its feasibility is crucial in the initial phase.\u003c/p\u003e\n"],["\u003cp\u003eRigorous experimentation is essential for building an effective ML model and may involve numerous iterations.\u003c/p\u003e\n"],["\u003cp\u003eProductionizing an ML solution requires robust pipelines for data processing, model training, serving predictions, and ongoing monitoring.\u003c/p\u003e\n"]]],[],null,["# ML development phases\n\nML projects progress in phases with specific\ngoals, tasks, and outcomes. A clear understanding of the ML development\nphases helps to establish engineering\nresponsibilities, manage stakeholder expectations, and efficiently allocate\nresources.\n\nSuccessfully moving through the phases (often iteratively) is foundational\nfor designing, assembling, and building ML\n[models](/machine-learning/glossary#model) that solve business problems\nover the long term.\n\nAt a high level, implementing an ML solution consists of the following phases:\n\n1. Ideation and planning\n2. Experimentation\n3. Pipeline building\n4. Productionization\n\nIdeation and planning\n---------------------\n\nDuring the ideation and planning phase, you frame your problem in terms of an\nML solution and estimate the project's feasibility.\n\n- **Goal**: To determine if ML is the best solution to your problem.\n- **Tasks**: Analyze the business problem to understand your project's constraints.\n- **Outcome**: A design doc outlining how to solve a problem with a ML solution.\n\n**Important:** During the ideation and planning stage, it can take a long time to understand the data and metrics required for a production-ready system. \n\nExperimentation\n---------------\n\nExperimentation is the core of machine learning. During this phase,\nyou verify that an ML solution is viable. Finding a solution is an\niterative process. It's not uncommon to try hundreds of experiments\nbefore finding the right combination of\n[features](/machine-learning/glossary#feature),\n[hyperparameters](/machine-learning/glossary#hyperparameter),\nand model architecture that solves the problem.\n\n- **Goal**: To build a model that solves the business problem.\n- **Tasks**: Experiment with features, hyperparameters, and model architectures.\n- **Outcome**: A model with good enough quality to put into production.\n\n**Important:** During the experimentation phase, new-to-ML practitioners often underestimate the challenges of designing and implementing the appropriate experimentation tooling and processes. \n\nPipeline building and productionization\n---------------------------------------\n\nDuring the [pipeline](/machine-learning/glossary#pipeline) building and\nproductionization phase, you build pipelines\nfor processing data, training a model, and serving\n[predictions](/machine-learning/glossary#prediction). You then\ndeploy the model and pipelines into production with the necessary monitoring and\nlogging infrastructure.\n\n- **Goal**: To build and implement the infrastructure for scaling, monitoring, and maintaining models in production.\n- **Tasks**: Build pipelines to automate many of the tasks for keeping up-to-date models in production.\n- **Outcomes**: Validated ML pipelines.\n\n**Important:** During the productionization phase, it's easy to underestimate the complexity of productionizing data pipelines and evaluating models, especially as features evolve. For instance, not only do you have to implement all the monitoring infrastructure required for a non-ML project, but also all the ML-specific monitoring. \n\nEnd-to-end ML workflow\n----------------------\n\nThe following diagram illustrates the entire end-to-end ML workflow, listing\neach phase and its tasks and outcomes:\n\n**Figure 1**. The four main phases of an ML workflow.\n\nKeep in mind\n------------\n\nMultiple challenges exist at each phase.\nNot realizing---and planning for---them may lead to missed\ndeadlines, frustrated engineers, and failed projects.\n\n### Check Your Understanding\n\nYou've just read about some ML technology that might benefit your product. What should you do next? \nBefore spending time drafting a design doc or writing code, you should first verify that ML is the right solution to your problem. \nCorrect. Before spending time drafting a design doc or writing code, you should first verify that ML is the right solution to your problem. \nDraft a design doc outlining the ML use case and the required infrastructure to implement it. \nBefore drafting a design doc, you should first verify that ML is the right solution to your problem. \nFind code examples and begin experimenting to determine if the model can make good predictions. \nBefore writing a line of code, you should first verify that ML is the right solution to your problem.\n| **Key Terms:**\n|\n| |-------------------------------------------------------|---------------------------------------------------------------|\n| | - [feature](/machine-learning/glossary#feature) | - [hyperparameter](/machine-learning/glossary#hyperparameter) |\n| | - [model](/machine-learning/glossary#model) | - [pipeline](/machine-learning/glossary#pipeline) |\n| | - [prediction](/machine-learning/glossary#prediction) |\n|"]]
