This article was originally written by Neetika Khandelwal and posted on the Neptune blog.

There is a broad catalog of tools you can use as helping hands to increase your understanding of machine learning models. They come in different categories:

  1. Interactive web app tools
  2. Data analysis tools
  3. Model explainability tools
  4. Model debugging tools
  5. Model performance debugging tools
  6. Experiment tracking tools
  7. Production monitoring tools

In this article, I’ll briefly tell you about as many tools as I can, to show you how rich the ML tools ecosystem is.

1. Interactive web app tools



Este artículo fue escrito originalmente por Nelson Ogbeide y publicado en el blog de Neptune.

El turismo ha experimentado un crecimiento masivo a lo largo de los años, ya que la gente busca pasar tiempo fuera de casa con el objetivo de entretenerse, relajarse y disfrutar. Al menos antes de los tiempos de COVID, el turismo ha sido un sector de rápido crecimiento que juega un papel importante en la economía global.

Según la Organización Mundial del Turismo de las Naciones Unidas, se estima que hubo 25 millones de llegadas internacionales en 1950. 68 años después, creció a alrededor de…

This article was originally written by MJ Bahmani and posted on the Neptune blog.

Using AutoML frameworks in the real world is becoming a regular thing for machine learning practitioners. People often ask: does automated machine learning (AutoML) replace data scientists?

Not really. If you’re eager to find out what AutoML is and how it works, join me in this article. I’m going to show you auto-sklearn, a state-of-the-art and open-source AutoML framework.

To do this, I had to do some research:

  • Read the first and second paper for auto-sklearn V1 and V2.
  • Took a deep dive into the auto-sklearn…


This article was originally written by Vladimir Lyashenko and posted on the Neptune blog.

Developing your model is an essential part of working on ML projects. And it’s usually a tough challenge.

Every data scientist has to face it, along with difficulties, like losing track of experiments. These difficulties are likely to be both annoying and unobvious, which will make you feel confused from time to time.

That’s why it’s good to streamline the process of managing your ML model, and luckily there are several tools for that. These tools can help with things like:

  • Experiment tracking
  • Model versioning
  • Measuring…


This article was originally written by Aigiomawu Ehiaghe and posted on the Neptune blog.

Processing can be used to improve the quality of your image, or to help you extract useful information from it. It’s useful in fields like medical imaging, and it can even be used to hide data inside an image.

In this article I’ll tell you about how Image Processing can be applied in Machine Learning, and what techniques you can use. First, let’s explore more real world examples of Image Processing.

Image processing in the real world

Medical imaging

In medicine, scientists study the inner structures and tissues of organisms in order to help…


This article was originally written by Asritha Bodepudi and posted on the Neptune blog.

Have you ever tried working with a large dataset of machine learning or deep learning algorithms on Jupyter Notebooks? Well, then you probably ran into the all — too familiar and annoying ‘memory-error.’


This article was originally written by Nelson Ogbeide and posted on the Neptune blog.

Tourism has enjoyed massive growth over the years, as people seek to spend time away from home in pursuit of recreation, relaxation, and pleasure. At least before COVID times, tourism has been a fast-growing sector that plays a big role in the global economy.

According to the United Nations World Tourism Organization, there were an estimated 25 million international arrivals in 1950. 68 years later, it grew to about 1.4 billion international arrivals, an approximately 56 fold increase.

According to Statista, travel, and tourism directly contributed…


This article was originally written by Gopal Singh and posted on the Neptune blog.

Machine learning is increasingly popular and the increasing number of companies are harnessing the power of this new technology. Yet the knowledge about the teams themselves is yet limited — what do they use? What do they like? Who are they?

Neptune has been built by data scientists for data scientist — no mumbo-jumbo in between. So when facing the challenge of missing knowledge we do the best we can do — gather the data. …

This article was originally written by Stephen Oni and posted on the Neptune blog.

Earlier this year (2020), I decided to move fully into the engineering part of machine learning from Data Science. I wanted to experience a more efficient and scalable way of deploying machine learning models, decouple my models from my app, and version them properly.

Conventionally, what I do mostly after training my model is to import the model in my flask app and then perform inference whenever the API endpoint for the model is being called. …


This article was originally written by Vidushi Meel and posted on the Neptune blog.

Jupyter notebooks are cool. They’re language-independent, great for collaboration, easy to customize, you can add extensions — the list goes on.

Issues begin when you need to track training hyperparameters, metrics, test results, or graphs. That’s when the chaos starts.

Then there are spreadsheets, which can quickly become unmanageable, especially in a team environment where you have multiple people who need to edit them at the same time.

Managing spreadsheets is painful, and not great for productivity. …

Patrycja Jenkner

Growth Specialist at @neptune_ai

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