Role of Pipelines in the Successful Machine Learning Project

You can imagine preparing an ML model as a sequence of consecutive steps. This is a basic framework that experts in our team use for machine learning tasks.

Data collection

It all starts with collecting the data. Our experts decide which data is required for the project, based on a clearly defined problem. We prefer to access as much information as possible here.

Data analysis and preparation

When the collection is done, the data is being transformed. We conduct the analysis and prepare it for the training. Key alterations include:

• Combining data collected from different sources

• Creating a single data set

• Formatting

• Cleaning it to detect possible abnormal values caused by errors

• Labeling

• Enhancing

We prefer to pick languages centered on data and tools for detecting data patterns. After the preparation, feature design begins. Our team creates data values for the model’s training and production, which include:

• Exploration of available data

• Finding attributes with the most predictive capability

• Coming up with a number of features

Model Selection

Now we can choose the algorithm. The mathematical algorithm for finding patterns in data is the core of any ML model. The algorithms can do this in different ways and some of them are better than others for particular goals like detecting sequences of written text or image recognition, etc. Different factors lead to the choice of the algorithm and the type of data for analysis. The factors may include:

• Business goals;

• Budget restrictions;

• The quality of data.

Model training

This is the heart of the entire process. To train an ML model, we need to feed an ML algorithm with suitable data. In fact, the term ML model refers to the method of the training process. Model training may differ depending on the available data and the problem to solve. Before using the resulting model, we need to test it, of course.

Model evaluation

This stage is a must to find out how well the model actually works, and we always go with it. We also check the accuracy of data for which we know the target answer. We use it later for evaluating predictive accuracy on the test dataset.

For supervised learning, we compute a summary metric that shows if and how precisely the predicted and actual values match by comparing the known target value and the ML model’s predictions.

Model tuning

When the evaluation is complete, it’s time to determine what aspects of the model can be improved for better outcomes. This process is called tuning the parameters. We can achieve higher accuracy by running through the training dataset multiple times, for example.

We can also tune the “learning rate”, which is the mark of how far we push the line during each step of training, based on information from the previous step. These values are extremely important for determining the accuracy and time expenses needed for the training to be completed.

Once we did everything we could to improve the model, we can move on to the final stage.

Deployment

So, we are ready to deploy the model and move it to production. Now the customer can use it to benefit from predictions based on the live data. When the model gets produced it is usually deployed and embedded in decision-making frameworks. Both offline and online predictions can be covered by the model. Additionally, you can deploy multiple models and safely switch between them. We can create multiple pipelines that can be planned in parallel to keep up with business demands. It is possible to implement because ML models are stateless.

How to measure your success?

Arguably the biggest challenge of ML modeling is actually knowing when the model is ready, and it’s time to stop the development phase. You might always have reasons to continue tuning and upgrading the model. That’s why we determine what exactly success means in each particular project at the very beginning, deciding on the necessary level of accuracy and the extent of errors. At the Evaluation stage, we determine the desired bar of quality. When we reach this bar, the project is going to be considered a success.

It could seem intimidating, building an efficient pipeline, but with a proper set of tools, the process will be easier. MLOps tools we use are helping us with:

• Developing solutions faster

• Making data ingestion, preparation, and storing much more simple

• Letting us validate our assumptions much easier, iterative, and organized

Our experts usually leverage some of the following tools, depending on the particular case:

MLFlow

To control the ML lifecycle, we use this open-source platform, which allows us to:

• Track experiments

• Reproduce

• Share

• Deploy models

MLFlow is a great fit for individual experts and teams of any size because it offers lightweight APIs compatible with any ML solutions that currently exist.

AWS SageMaker

This is a cost-effective fully managed service that allows us to build, train and deploy machine learning models. The module systems make it possible to easily use any combination of modules without the loss of transparency and control. AWS SageMaker spares us from administrative tasks.

Data labeling service SageMaker Ground Truth reduces data labeling expenses by almost 70% thanks to automatic data labeling.

Google Cloud Platform (GCP) AI Platform

This is a suite of cloud-based computing services that are built to cover a wide range of common needs. From hosting applications in containers to advanced machine learning and Artificial Intelligence solutions.

GCP helps us to combine TensorFlow Extended with Kuberflow Pipelines. TensorFlow is a library for numerical computation and large-scale machine learning, receiving the information in the form of “tensors” (multi-dimensional arrays of higher dimensions). The second one is an open-source platform for dealing with pipelines.

The most important benefits include:

• We can focus on different tasks while different stages run in sequence or in parallel without our attention;

• We are able to use existing resources efficiently, running separate steps on different compute targets;

• We can reuse pipeline templates;

• We are more productive thanks to eliminating a certain amount of manual work;

• We can develop projects quicker and better thanks to the modular structure of pipelines;

• We can work in different ML areas, making pipelines a great tool for more productive teamwork;

• We can use machine learning pipelines to prioritize high-potential customers and optimize marketing campaigns through automated predictive lead scoring.

Once you obtain an understanding of what is happening at every stage of your pipeline in machine learning, you get more transparency and the ability to make effective business decisions.

We at Intelliarts, a software engineering company, love to help companies solve challenges with data strategy design and implementation. If you have any questions related to ML pipelines in particular or other areas of data science – feel free to reach out.