5 Tools to Maintain Your Machine Learning Projects Efficiently
By: Sara A. Metwalli
Tags: Data Science, Productivity, Debugging, Programming, Machine Learning

5 Tools to Maintain Your Machine Learning Projects Efficiently

Photo by Marvin Meyer on Unsplash

Regardless of its end goal, any software project must go through some common set of steps from ideation to deployment. For example, data science projects, in general, are software projects, and so they need to go through the same development process. This development process contains steps such as ideation and planning, design solution, implementation, testing the software, deploying it, and maintaining it.

Although these steps may vary depending on the actual project you’re building, you will go through these steps in some form in the majority of the time. Today’s article aims to discuss the last steps of a data science project, especially the project testing and maintenance.

One of the most challenging types of projects to test and maintain is any project containing machine learning algorithms. In general, testing and debugging a software application often tasks a very long time, often longer than the time used to develop the application in the first place.

Machine learning applications are often complex and depend on sophisticated math and statistics. That makes testing and debugging such an application even more challenging and time-consuming. Luckily, existing tools can help us test, debug, and maintain our machine learning projects in less time and with minimal effort.

This article will go through five tools that can help you test, debug, and maintain your projects efficiently and hassle-free.

№1: TensorWatch

Let’s kick off this list with a simple and easy-to-use tool, TensorWatch. TensorWarch is a visual debugging tool designed by Microsoft Research to aid data scientists in debugging machine learning, artificial intelligence, and deep learning applications. TensorWatch works perfectly with Jupyter notebooks showing different analyses for your model training and performance in real-time.

Although you can use the predefined visualization and analysis in TensorWatch, the tool is very flexible and extendable. You can design and implement your own visualizations, dashboards, and tests. Moreover, you can use TensorWatch to perform queries against your model during the training process. So, if you’re looking for a simple, lightweight tool to start debugging machine learning models with, TensorWatch is a great option.

№2: Deepkit

Next on the list is a tool I often mention whenever I talk about tools that make any data scientist's life easier: Deepkit. Deepkit is an open-source development tool designed for debugging and testing machine learning applications. Deepkit is an all-in-one cross-platform application that both individuals, small teams, or big corporations can use.

Deepkit offers many options that you can use to make training, testing, and debugging your machine learning and artificial intelligence applications a piece of cake. These options track every step of your machine learning experiment, model debugging both visually and analytically, and offer computational management that allows you to oversee the infrastructure of your model and utilize it efficiently.

№3: Data Version Control (DVC)

This tool is one of my absolute favorite tools for data science out there. One of the aspects I struggled with when I was learning software development is version control. Git and version control are not the easiest concepts to comprehend, especially for beginners. That’s why Data Version Control (DVC) is an amazing option to keep track of your version control.

DVC is a tool used to version-control machine learning models, data sets, and any other files in your project. DVC helps you track all your files over different cloud storage like Amazon or Google or even offline disc ones. DVC will track the evolution of your machine learning model to ensure reproducibility and allow you to switch between different experiments. It also offers support for deployment and continuous integration.

№4: Manifold

Our next tool is an open-source tool developed and used by Uber to debug machine learning models; that tool is Manifold. So often, when data scientists test the performance of their machine learning models, they use metrics such as log loss, mean absolute error, and area under the curve. But, in most cases, these metrics don’t give you the necessary information to understand when your model doesn’t behave as expected.

Manifold is developed to make the process of iteration over the model more informative, and Manifold is a visual model diagnostics and debugging tool for machine learning. It allows you to look beyond the basic performance metrics and even provide potential causes of why a model may be performing wrong or unexpected. Not just that, but it can also suggest candidate models with their expected accuracies for your specific dataset with justifications to each given model.

№5: TensorFlow Debugger

Last but not least is the debugger of the monster tool TensorFlow. TensorFlow is one of the most well-known Python machine learning libraries developed by Google in the data science community. Even if you’re new to the field, chances are, you heard of TensorFlow. TensorFlow contains many tools and options to develop potent machine learning applications.

One of these tools is the TensorFlow Debugger (tfdbg). Debugging is an essential step in any machine learning applications, but it’s often a very difficult and time-consuming step. TensorFlow Debugger provides features to inspect the flow of the data in your application during runtime. Moreover, it offers a chance for the developer to observe the intermediate tensors of the graph as well as its simulating stepping.

Final Thoughts

Debugging software is one the most tedious steps in any software life cycle. That step gets even more complex and time-consuming when you're dealing with an application, including machine learning. This is because machine learning applications often depend on advanced math and statistics to operate, not to mention the data used to train the model.

These fact makes debugging machine learning application more of a hassle. But, luckily for us, there are different tools that we can use to assist us test, debug and maintain machine learning applications.

This article went through tools to test, debug, visually analyze, and version control machine learning models. Yes, there are many tools that one can use when working on a data science project, but once you find your favorite tools, your workflow will become smooth and efficient.