# ML Life Cycle

![](/files/-MZHk81YtLKkug0UJufN)

We have finally arrived to the point of grouping all the foregoing pieces to be able to successfully understand how the **Machine Learning Life Cycle** can be managed and applied towards the creation of a new ML product.

In the following lectures, we will review each of the life cycle stages:

{% content-ref url="/pages/-MK9Cg\_yjccOWr82g\_aa" %}
[Business Objectives](/productize-ml/productize-it/business-objectives.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MK9Fpt5CvYNFc5frkoG" %}
[Data Preparation](/productize-ml/productize-it/data-preparation.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MK9IGO6IRHea3R364I9" %}
[Model Development](/productize-ml/productize-it/model-development.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MK9IUqLzMh6Zgvb9vXO" %}
[Train, Evaluate, and Deploy](/productize-ml/productize-it/train-evaluate-deploy.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MK9FVeUuLDmCV4FgeP2" %}
[A/B Testing](/productize-ml/productize-it/a-b-testing.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MFpMsT541J3Sw6ItyIC" %}
[KPI Evaluation](/productize-ml/productize-it/kpi-evaluation.md)
{% endcontent-ref %}

## Some considerations

Before we start defining each stage, let's first take into account that depending on the product stage we are at, the ML life cycle will require certain adjustments.

As a reminder, we can split our product initial stages as:

* 🚀 **MVP (Minimum Viable Product):** the first launched version of your product to customers. It is developed with a minimum number of features to solve your problem in a simple way. It is generally used as a trial tool only.
* ❤️ **MLP (Minimum Lovable Product):** similar to an MVP, but with special consideration for its design and UI. It aims to solve the problem, but also delight users.
* 💰 **MMP (Minimum Marketable Product):** this is the version of your MVP (or MLP) which you will launch to market.

## Readings

{% embed url="<https://blogs.oracle.com/datascience/7-artificial-intelligence-trends-and-how-they-work-with-operational-machine-learning-v2>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.productizeml.com/productize-ml/productize-it/ml-lifecycle.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.