MLOps can also be thought of as the process of converting your machine learning models into useful solutions.

Let us understand how in this thread 👇🧵

cc: @abacusai

@abacusai MLOps can streamline the process of training, deploying, and managing machine learning models which will result in faster iteration and high-quality models.

@abacusai Any machine learning solution can be broken into three key components:

- Data
- ML Model
- Code

Let's look at each of these components in detail 👇

@abacusai Data Engineering

Most important and the initial step in any MLOps pipeline. It consists of the following steps:

1. Data Ingestion
2. Data Exploration and Validation
3. Data Wrangling (Cleaning)
4. Data Labeling
5. Data Splitting

@abacusai Model Engineering

It includes writing and executing machine learning algorithms to obtain an ML model. It consists of the following steps:

1. Model Training
2. Model Evaluation
3. Model Testing
4. Model Packaging

@abacusai To read more about it, please refer to the source article from where this thread is derived - ml-ops.org/content/end-to-end-ml-workflow

@abacusai If you want to deploy a machine learning solution for your usecase along with an end-to-end MLOps pipeline to keep it up-to-date.

Check out this amazing platform by @abacusai that lets you build scalable ML solutions out-of-the-box 👉 abacus.ai

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