If you follow the technology trends regularly, you might be familiar with what the buzzword Machine Learning has been for quite some time now. Whether you are developing a machine learning system or running the model in production, there is always a huge amount of data processing involved. And it is almost tempting to think of machine learning as a magic black box where the data goes in and predictions come out. But apparently, there is no magic in there. It’s just a game of algorithms, and models created by processing the data.
Data + Machine Learning Algorithms = ML Model
But how does that all unfold?
The workflow is quite simple –
Your data contains patterns.
You apply an ML algorithm which finds the patterns and generates a model.
The model will recognize these patterns when presented with new data.
A Machine Learning pipeline aids in exercising proper control over any ML model. A better-organized pipeline gives a flexible implementation of the model. It’s like having an exploded view of a car’s engine where you can pick the faulty pieces and replace them – in our case, replacing a chunk of code.
Data scientists define a pipeline for data as it flows through their Machine Learning solution. This pipeline consists of a sequence of components which are a compilation of computations. Data is sent through these components and it is then manipulated with the help of computations. The machine learning pipeline enables an iteration to improve scores of machine learning algorithms and make the model more scalable. It runs from ingesting and cleaning data, through feature engineering to model selection, and deploying the trained model while serving predictions.
The pipeline, unlike its name, is not just a one-way flow instead it is cyclical and iterative as every step is repeated to finally achieve a successful algorithm.
The key stages of a Machine Learning Pipeline are described below:
Problem Definition: The business problem for which a solution is required is defined in this stage.
Data Ingestion/Data collection: Identifying and gathering the data you want to work with, is the base of the Data Ingestion phase. The incoming data is funneled into a data store. The major point here is that data is persisted without undertaking any transformation whatsoever. This allows you to have an immutable record of the original dataset.
Data Preparation: This phase can be best described in three steps- Exploration, transformation, and feature engineering. Since the data that is ingested is raw and unstructured, it is rarely in a suitable form to be processed. It usually has missing values or duplicates records or unnormalized data or other correcting flaws, for instance, different representations of the same values in a column. Hence, it needs to be transformed to prepare it for the next step.
Data Segregation: Subsets of data are split to train the model. These subsets are then tested and further validated on how they perform against new data.
The segregation can be done by a number of methods, like –
- Use a custom ratio to split the data into two subsets in such an order that it appears in the source while making sure that there is no overlapping. For example, the first 70% of data is used for training and rest 30% for testing.
- Use a custom ratio to split data into two subsets via a random seed. For example, select a random 70% of the source data for training and the remaining complement for testing.
- Use a custom injected strategy to split when explicit control over the separation is required.
Model Training: Use the training subset of data to let the ML algorithm recognize the patterns in it.
Model training should be implemented keeping error tolerance in mind. Also, data checkpoints and failover on training partitions should be enabled. For example, each partition can be retrained if the previous attempt fails due to some transient issue like a timeout.
Candidate Model Evaluation: Assess the performance of the model using test and validation subsets of data in order to understand how accurate the prediction is. The predictive performance of a model is evaluated by comparing predictions on the evaluation dataset with true values using a number of metrics. And the “best” model in the evaluation subset is then selected to make predictions on future instances.
Model Deployment: The model deployment phase is not the end, it is just the beginning!
The best model chosen is deployed for predictions. More than one model can be deployed at a time to enable a safe transition between old and new models. While deploying a new model, services need to keep serving prediction requests.
Model Scoring: It is a process where the ML model is applied with a behavior dataset in order to uncover practical insights. These insights help solve a business problem. A.k.a. Model Serving.
Performance Monitoring: The model is continuously monitored to observe how it behaves in the real world and is calibrated accordingly. New data is collected to incrementally improve it. It is a continuous process as a shift in prediction might result in restructuring the entire design of the model. Providing accurate predictions to drive the business forward is what defines the benefits of Machine Learning!
After putting it all together, there you have a production-ready Machine Learning system.
Final thoughts –
The amount of data that any business captures and stores are overwhelming. However, it is not the volume of data but what businesses do with it that really matters. Today’s businesses are starting to realize how powerful big data is, and that it is definitely more valuable when paired with automation. Supported by massive computational power, machine learning is now helping businesses to analyze and use their data more effectively than before.
