Some Tools To Keep Your Machine Learning Projects Running Efficiently

On debugging, testing and version control

Whatever its end goal, any software project must go through some common steps, from ideation to commissioning. For example, data science projects are software projects in general, and so they must go through the same development process. This development process includes steps such as idea and planning, design solution, implementation, testing, deploying and maintaining the software.

While these steps will vary depending on the actual project you are creating, most of the time you will get through them somehow. In today’s article I will cover the final steps of a data science project, specifically project testing and maintenance.

One of the most difficult types of projects to test and maintain is any project involving machine learning algorithms. In general, testing and debugging a software application usually takes much longer than the time used to develop the application.
Machine learning applications are often complex and rely on sophisticated mathematics and statistics. This makes testing and debugging such an application more difficult and time-consuming. Fortunately, available tools can help us test, debug, and maintain our machine learning projects in less time and with minimal effort.
In this article, I’ll focus on five tools that can help you test, debug, and protect your projects efficiently and seamlessly.

1. TensorWatch
Let’s start this list with TensorWatch, a simple and easy to use tool. TensorWarch is a visual debugging tool designed by Microsoft Research to assist data scientists in debugging machine learning, artificial intelligence, and deep learning applications. TensorWatch works perfectly with Jupyter notebooks, showing different analytics for your model training and performance in real time.
Although you can use predefined visualization and analysis in TensorWatch, this tool is very flexible and extensible. You can design and implement your own visualizations, dashboards, and tests. You can also 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, TensorWatch is a great option.

2. Deepkit
Next on the list is a tool that is often mentioned when it comes to 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 can be used by individuals, small teams and large companies alike.
Deepkit offers many options you can use to train, test, and debug your machine learning and AI applications to make it a breeze. These options monitor every step of your machine learning project, build the debug model both visually and analytically, and offer computational management that allows you to audit your model’s infrastructure and use it efficiently.

3. TensorFlow Debugger
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, you’ve probably heard of TensorFlow. TensorFlow includes many tools and options for developing powerful machine learning applications.
One of these tools is TensorFlow Debugger. Debugging is an important step in any machine learning application, but it is often a very difficult and time-consuming step. TensorFlow Debugger provides features to inspect the data flow in your application during runtime. It also offers the developer a chance to observe the intermediate tensors and step-by-step simulation of the graph.

4. Data Version Control (DVC)
Git and version control are not the easiest concepts to understand, especially for beginners. That’s why Data Version Control (DVC) is a great option to keep track of your version control. DVC is a tool for version checking machine learning models, datasets, and other files in your project. DVC helps you to watch all your files through different cloud storage or offline disks like Amazon or Google. DVC will monitor 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.

5. Manifold
Our next tool is Manifold, an open source tool developed and used by Uber for debugging machine learning models. Often, data scientists use metrics such as log loss, mean absolute error, and area under the curve when testing the performance of machine learning models. But in most cases, these metrics don’t give you the information you need to know if your model isn’t behaving as expected.
Manifold was developed to make iterating over the model more informative and is a visual model diagnostic and debugging tool for machine learning. It allows you to look beyond key performance metrics and provide possible reasons why a model is performing incorrectly or unexpectedly. It can also suggest candidate models with expected accuracy for your particular dataset, with justifications for each model.