Why Having the Right Strategy of MLOps is Important?

Author(s): Sumit Singh

Originally published on Towards AI.

The last decades have seen incredible advancement in the field of AI and machine learning. What previously has been a research topic and was accessible to only a handful of scientists and researchers is now accessible to entry-level engineers and students.

But as technology grew and the industry-wide applications started rolling out, so did the complexities and challenges.

Building an ML model to do basic stuff like identifying a few household objects or human activities to say 70% confidence is a common thing, but to achieve 99% accuracy in identifying cancers or tumors in a medical image is a different thing.

What it takes to build a production-level AI

To solve any industrial use case, ML teams have to manage a scale, which means a high volume of data to be processed, the right algorithm selection, and a vast amount of iterations.

It involves a significantly bigger team with different expertise, and to manage the overall process, one needs to implement the right process and strategy.

That process is called MLOps.

What are the MLOps components?

Every team has its unique set of requirements, and based on their goals, the implementation might differ, but components of MLOps will largely remain the same.

MLOps can range from data pipeline to model output in some cases, while other projects may only require MLOps execution of the model deployment process. The majority of businesses use MLOps principles in the following areas:

• Exploratory data analysis (EDA)
• Data preparation and feature development
• Model development and refinement
• Model evaluation and governance
• Serving and model inference
• Model monitoring
• Model retraining that is automated

Let’s understand each component one by one-

1. Exploratory Data Analysis (EDA): EDA is a process to analyze and summarize a data set, typically with the goal of finding patterns or relationships between variables.

It involves using visualizations, statistical techniques, and other methods to explore the data in order to gain insight and better understand the data set.

2. Data preparation: It is the process of cleaning, transforming, and organizing raw data so that it can be used for analysis, modeling, and other tasks.

Let’s say you have a data set of customer records.

You might need to preprocess the data by normalizing the values, removing invalid or missing data, converting categorical data into numerical values, and scaling the data so that the values fall within a specific range.

Once the data is preprocessed, it can then be used for machine learning or other data analysis tasks.

3. Feature development: is the process of creating new variables from existing data, either through combining existing variables or by extracting new information from existing data.

To understand this, let’s say you have a data set containing customer records that include information about their age, gender, location, and purchase history.

You can create new features from this data by combining existing variables, such as creating a “total purchases” feature by adding up all the customers’ purchase histories, or by extracting new information from existing data, such as creating a “location density” feature by counting the number of customers in a given location.

These new features can then be used for machine learning or other data analysis tasks.

4. Model development and refinement: is the process of creating, testing, and refining a mathematical model to fit observed data.

This process typically involves selecting the appropriate model type, specifying the parameters and variables, and fitting the model to the observed data.

Once the model has been created, it can be used to make predictions and to explain relationships between variables.

One example of this would be a machine learning model trained on a data set to predict customer churn.

The model would be tested on a validation data set, and any errors or inconsistencies in the model’s performance would be identified and addressed by refining the model parameters or introducing new features to improve the model’s accuracy.

Once the model has been refined and is performing appropriately, it can be used for predictive analytics and other applications.

5. Model evaluation: It involves assessing the accuracy, reliability, and validity of the model, while model governance involves establishing policies and procedures to ensure that the model is used in an ethical and responsible manner.

Model evaluation and governance are important in ensuring that models are used responsibly and produce reliable results.

An example of model evaluation and governance is the use of a risk assessment process to evaluate the accuracy and reliability of a model before it is deployed for use in decision-making.

This process involves assessing the data used to train the model, assessing the performance of the model on a validation data set, and assessing whether the model is ethically sound and is being used in a responsible manner.

If any issues are identified, the model can be revised or adjusted to improve accuracy and reliability and to ensure that it is being used responsibly.

6. Serving and model inference: is the process of deploying a trained machine learning model and using it to make predictions and inferences about data.

This process typically involves deploying the model to a server, where it can be used to make predictions and inferences on new data.

Additionally, the process may involve collecting feedback from users to ensure that the model is providing accurate and useful predictions and inferences.

Let’s understand this with an example using a trained deep-learning model to classify images.

The model would be deployed to a server, where it could be used to make predictions on new images.

In order to ensure that the model’s performance is accurate, the model would be evaluated on a validation data set.

The model would also be monitored to ensure that it is performing correctly and efficiently, and feedback from users would be collected to ensure that the model is providing accurate and useful predictions.

7. Model monitoring: is the process of tracking the performance of a machine learning model over time.

It involves tracking metrics such as accuracy, precision, recall, and false positive rate to ensure that the model is performing as expected.

Additionally, model monitoring can also be used to detect shifts in the data that could affect the model’s accuracy, allowing for the model to be updated or improved if necessary.

8. Model retraining: is the process of updating a model to improve its accuracy and performance.

This process involves collecting new data, retraining the model on the new data, and then deploying the updated model.

Model retraining can be used to improve the accuracy of a model, as well as to adjust the model to changing conditions and new data.

This process can also be used to detect any potential drift or bias in the model’s performance, allowing for the model to be adjusted or updated as necessary.

MLOps is continuous, never-ending process

As we discussed above, it should be clear that ML development runs in a loop, and MLOps also runs in a continuous fashion.

Every component works in tandem and is equally important. Ignoring any component would result in an unwanted and unpredictable result.

It is common knowledge now that 80% of AI-ML initiatives result in failure.

Not setting up a right MLOps strategy is one major reason behind that.

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Why Having the Right Strategy of MLOps is Important? was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Published via Towards AI