Over the past couple of years, the machine learning community has grown a lot. One of the great things to come from it is the abundance of efficient machine learning tools that help us do more with less effort. As a machine learning engineer, you now have less work when building models or pipelines.
For example, if you want to build a deep learning model,you might need to define the workings of weights, biases, amount of nodes, amount of layers, activation function, optimizer, and the list goes on and on. With ML tools, you don’t need to do all of that yourself.
These tools let you play around with your data, tweak models, adjust performance, and much more, saving you and your team precious time.
In this article, we’ll analyze the best tools that can save time for your ML team and increase your productivity – along with some great tools for time tracking, communication, and a few in-between.
Let’s get into it.
Neptune—manage all your model building metadata in a single place
Neptune is a lightweight, powerful metadata store for MLOps. It gives you a centralized location to display your metadata, allowing you to easily track your machine learning experiments and results.
To build an optimized ML model, you run many trials with different hyperparameters and architectures. Tracking the performance of each experiment can become a bottleneck. This is where Neptune comes in.
It’s easy to integrate Neptune into your pipeline and follow up with a wide range of metrics. But it doesn’t stop there. Neptune stores trials, manages and analyzes results, creates visualizations, and makes it easy to share your findings with other team members and managers.
Neptune is flexible, so it can easily be integrated with other machine learning frameworks, like Scikit-learn, R, PyTorch, Ignite, HiPlot, XGBoost, and Skorch.
Plus, this tool scales up like it’s nothing. Tracking can support experimentation to the scale of millions of runs.
- Storage and analysis of large-scale data.
- Run as many experiments as you want, with checkpoints.
- Resume experiments from the last checkpoint.
- Tools for sharing insights with team members and project stakeholders.
- Integrates with your favorite machine learning libraries and tools.
- Performs exploratory data analysis on your data.
Want to learn more?
TensorFlow—build, train, and deploy models quickly and easily
TensorFlow is a powerful open-source library for creating and deploying your machine learning models. It comes with a comprehensive collection of tools and resources for building state-of-the-art ML-powered applications quickly and easily.
You can build, train, and deploy deep learning models on various environments, including the browser, on-device, on-premise, or cloud. The simple and flexible architecture can save you a lot of time during training and inferencing deep learning models.
TensorFlow is an end-to-end platform for applying deep learning to solve real-world problems. It’s an excellent choice for building a wide range of AI-powered systems, like text-to-speech, image captioning, object detection, language translation, predictive analysis, and much more.
- End-to-end open-source platform for a wide range of machine learning tasks.
- Performs efficient numerical computations with the flow of vectors in a computational graph.
- Cross-platform, runs on almost everything—GPUs and CPUs, including mobile devices and embedded platforms.
- Scales well, one of the reasons why companies like Samsung, Google, Apple, and NVIDIA actively use it.
Scikit-learn—wide collection of tools for building models
Scikit-learn is an open-source library with a wide collection of tools for building machine learning models and solving statistical modeling problems. This, largely Python-based, library is built on matplotlib, SciPy, and NumPy.
Scikit-learn holds most of the popular algorithms for supervised, unsupervised, and reinforcement learning problems. Some of them are logistic regression, linear regression, decision trees, support vector machines, principal component analysis, and random forest.
With Scikit-learn, training your dataset on any desired algorithm is as easy as importing the algorithm and calling the fit method. This will save you the stress of building the entire model from scratch.
- Robust library to work with various machine learning algorithms.
- Integrates well with other machine learning libraries, such as pandas for dataframes, numpy for array vectorization, and matplotlib for plotting.
- For regression, it supports Bayesian regression, Lasso, Ridge, SVR, and other algorithms.
- For clustering, it supports hierarchical clustering, k-means, DBSCAN, mean shift, spectral clustering, and other algorithms.
- For classification, it supports SVC, logistic classifier, random forest classifier, k-nearest neighbors, and other algorithms.
- Has the facility for feature selection, feature extraction, dimensionality reduction, and various preprocessing procedures.
How you can keep track of your Sklearn model training metadata.
Trello—manage, organize, visualize, and share projects seamlessly
Trello is a resourceful tool for managing, organizing, visualizing, and sharing projects with others. It’s not explicitly meant for machine learning, but it comes in handy for managing teamwork in ML engineering teams.
You create dashboards, called Trello Boards, for each project. The boards are populated with cards where everyone can comment and discuss details. You can add files, due dates, checklists, and labels on the cards to keep your team members up to speed with your work progress.
For a machine learning project, the Trello Board may contain cards that show the steps for data cleaning, data preprocessing, exploratory data analysis, model building, model training, and deployment. This way, you can very conveniently track the progress of all team members.
- Visually collaborate with members of your ML team.
- Easy, drag-and-drop editing capabilities.
- Progress meter checklist for allocated tasks.
- Deadline alerts and notifications.
- Supports file attachments.
- Robust backup and information retrieval mechanism.
- Easy-to-use API for integrating Trello with third-party applications.
- Access on multiple operating systems—Windows, Mac OS, iOS, and Android.
Jupyter Notebook—create and share documents interactively and easily
Jupyter Notebook is an open-source, web-based IDE for interactively creating and presenting data science and machine learning projects. You can use it to develop and share documents with explanatory text, visualizations, equations, and live code.
Jupyter Notebook lets you run chunks of code separately for quick results, without the direct dependence on previous codes. It’s easier to know exactly what’s going on under the hood as you code.
The layout for exploratory data analysis is impeccable. It’s also great for rendering visualizations, especially when used with other libraries like seaborn, matplotlib, or plotly.
Jupyter Notebook is undoubtedly a useful tool that you’re probably already using to enhance productivity in your machine learning projects.
- Easy to use with great color highlighting for reserved keywords, comments, strings, and other phrases.
- Convert a Jupyter Notebook document into other output formats, such as PDF, LaText, HTML, and presentation slides.
- Connect to several kernels to support programming in various languages, apart from Python, R, and Julia.
- Great file management system. Files sizes are small and can be retrieved easily.
- Auto-save feature. Your code won’t be lost in case of unexpected system failure.
- Great tool not only for programming, but also for presentation—with its Markdown, Raw NBConvert, and heading features.
Toggl—track your online and offline work hours with one click
Toggl is one of the best tools out there for time management. It’s a simple and powerful tool for tracking the amount of time you spend working on your projects.
With Toggl, time tracking is as easy as clicking a start button when you begin working, and a stop button when you’re finished. It’ll help you stay productive and manage time better.
When you’re doing nothing, perhaps you stepped away from your PC, Toggl automatically detects idle times and takes note of them. In a team, this is great for transparency – especially when working remotely. You and your team can see how long you actually work.
Another feature that makes Toggl a top choice is checking the web pages and programs you visited during work hours, and how long you spent there. This way, you get insights into how you spent your time online.
You don’t even need to be online all the time to use Toggl. It works accurately even when you’re offline.
- Track time across multiple platforms—browser extension, mobile app, desktop app, and web app—with a single click. All your tracked entries are synced automatically in real-time.
- Auto-track every website or application you visit.
- Works both online and offline.
- Supports calendar integration, showing you where your time goes.
- Supports seamless integrations with more than 100 third-party applications, including Trello, Todoist, and Asana.
- Tracking reminders.
- Powerful reporting capabilities for actionable insights. Filter and sort your data, and export via PDF or CSV.
- Schedule your favorite reports to be sent regularly to your email.
- Useful for project management. Toggl shows you how long you’ve spent on a project, estimated time to finish up, how well you allocated time for each activity, and so on.
- Useful for team management.
Sweetviz—perform in-depth EDA with a few lines of code
Sweetviz is an open-source library for performing Exploratory Data Analysis (EDA) and creating beautiful visualizations with just a few lines of code. It’s built on top of the pandas profiling library.
It’s a great tool to boost your productivity. It reduces drastically the time you spend manually cleaning data and creating every visualization because Sweetviz does all the hard work while you focus on your core activities. It has features like target analysis, comparison, feature analysis, correlation and output a fully self-contained HTML application.
Sweetviz analyzes your entire data and provides a detailed EDA report with visualizations. The generated report contains a lot of information for understanding the characteristics of your dataset.
You can also use it to check how a particular feature affects other features in the dataset, perform feature correlation in the dataset, and visualize features.
- Perform quick EDA and generate beautiful visualizations in a few lines of code.
- Analyze how a target variable relates to other features in the dataset.
- Characterize a dataset and find out about the distribution of values, data types used, missing information, and more.
- Compare and visualize two distinct datasets, such as training and test data.
- Create an easy-to-read, yet robust, summary of the characteristics of the target dataset.
- Automatically detect continuous, categorical, and textual data.
- Integrate with other machine learning tools, like Jupyter Notebook and Google Colab.
Slack—communicate with your team instantly and conveniently
Slack is an instant messaging platform for easy communication. It’s an excellent place for efficient communication and collaboration within your machine learning team.
You have different channels (chat rooms) for easy navigation. You can create private and public channels. Anybody can get into the public channels, private channels can only be accessed by team members added by the channel’s owner or admin.
You get notifications of new messages in your workspace, including when you’re logged out of a channel—though this feature can be changed in the notification settings.
You can get started with the free version, where you can store and search up to 10,000 most recent messages.
- Channels provide a centralized space for managing communication with other team members. Send messages to the whole team or directly to an individual.
- File sharing, voice calls, video calls, and reactions with emojis.
- Integrations with several third-party tools, such as Asana, Google Drive, Trello, and Zendesk.
- Great for file archiving. You can archive old conversations on a channel for future reference.
- Fantastic search engine. You can search for messages, archives, and even file names.
- Easy-to-use API to integrate Slack capabilities into different use cases.
- Access on various platforms, including mobile Android and iOS apps, web browsers, desktop clients for Windows and Mac OS, and Apple Watch.
Google Cloud AutoML—leverage Google’s robust technology to automatically train models with minimal effort
Google Cloud AutoML is a suite of powerful tools to get started with training high-quality machine learning models without extensive expertise. Leverage Google’s tech to train your models with minimal effort.
Google Cloud AutoML uses Google’s battle-tested, pre-trained services. It doesn’t begin from scratch when training models.
To help you create models that meet your needs, AutoML applies Google’s Neural Architecture Search technology (it looks for the right blend of extra network layers), as well as automatic deep transfer learning technology (it begins from an existing neural network that’s already been trained on other data).
Google AutoML removes the trouble of finding a suitable algorithm to use, the number of layers, nodes, learning rate, and other hyperparameter tunings.
With Google Cloud AutoML, you don’t worry about the time-intensive nature or extensive experience required to train today’s neural networks. You can use it to design neural networks for your specific business needs without mastering the intricacies.
- Train best-in-class machine learning models automatically, even with limited knowledge.
- Google’s sophisticated Neural Architecture Search technology and deep transfer learning technology performs machine learning tasks automatically.
- Useful for various machine learning tasks, including image classification, natural language classification, and language pair translation.
- Validate, fine-tune, and deploy your models at scale.
Keras—minimize the cognitive load when defining and training models
Keras is a robust, open-source API for building and evaluating deep neural networks. It’s designed to be modular, flexible, and simple—you can define and train models using a few lines of code.
Note, that Keras doesn’t work on its own. You need a backend (or engine) to power it. By default, the backend is TensorFlow, but it also supports other backends, like Theano and Microsoft Cognitive Toolkit.
Keras minimizes the cognitive load when developing machine learning models. The efficient API reduces the work you need to implement common use cases, so you can be more productive when working with models.
- Consistent and an easy-to-use API. Even beginners can build neural networks with Keras within a few minutes.
- Python-based interface for working with deep neural networks.
- Works well on both CPUs and GPUs.
- Flexible and great for research purposes.
- Expansive ecosystem covering each step of the machine learning workflow—from managing raw data, through training hyperparameters, all the way to deploying solutions.
- Widely adopted in the machine learning community. Backed by tech giants like Amazon, Microsoft, Google, Apple, and others.
Check how you can track your model training metadata with Neptune + TensorFlow / Keras integration.
Those are ten of the best tools you can use as a machine learning engineer to be more productive by yourself, or with your team.
We outlined the key features of each tool and how they can help you to take your machine learning development efforts to the next level. Hopefully this helped you pick the right tools for your case.
MLOps at GreenSteam: Shipping Machine Learning [Case Study]
7 mins read | Tymoteusz Wołodźko | Posted March 31, 2021
GreenSteam is a company that provides software solutions for the marine industry that help reduce fuel usage. Excess fuel usage is both costly and bad for the environment, and vessel operators are obliged to get more green by the International Marine Organization and reduce the CO2 emissions by 50 percent by 2050.
Even though we are not a big company (50 people including business, devs, domain experts, researchers, and data scientists), we have already built several machine learning products over the last 13 years that help some major shipping companies make informed performance optimization decisions.
In this blog post, I want to share our journey to building the MLOps stack. Specifically, how we:
- dealt with code dependencies
- approached testing ML models
- built automated training and evaluation pipelines
- deployed and served our models
- managed to keep human-in-the-loop in MLOps