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 ๐๐งต
cc: @abacusai
Let us understand how in this thread ๐๐งต
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 ๐
- Data
- ML Model
- Code
Let's look at each of these components in detail ๐
@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
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
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
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