Watson Studio

Time Series is one domain which has been using some form or other of predictive analysis since long before the birth of contemporary machine learning. Once upon a time, our ancestors tracked the location and movement of the moon and the stars to decide when to move from place to place, when to hunt, and when to sow the seeds in the expectation of rain. In doing so they had figured out cycles and seasonality in the flow of time — something we now call the cyclical and seasonal components of a time series.

As a knowledge worker, data scientist, or business analyst you are probably spending a big amount of your time refining and wrangling data before you can use it for further analytics and machine learning.

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software and hardware. They spend considerable time ideating. Yet despite all this effort and money, many of these companies are enjoying little to no meaningful benefit. This is primarily because they have spent all these resources on too much experimentation, projects with no clear business purpose, and activity that doesn’t align with organizational priorities.

Deep learning can be tedious work. Taking Long Short-Term Memory (LSTM) as an example, we have lots of hyperparameters, (learning rate, number of hidden units, batch size, and so on) waiting for us to find the best combination. Considering the size of a deep learning model, hyperparameter tuning usually takes long time.

Explainability is a hot topic. Data Science models are used to trigger recommendations such as “accept” or “refuse” a loan, and the need to answer “why this recommendation?” is arising. Customers impacted by recommendations are asking “why”, and so some companies are willing to answer, and in some geographies, regulations even prevent applications being used in some areas when explainability is not available.

A practical guide to developing machine learning apps.

These 10 videos will let you explore such basic functions of IBM Watson Studio such as getting started, intro to notebooks, collaboration, data storage and connection, and more.

A simple walkthrough of a Machine Learning exercise, creating, evaluating, and deploying a Machine Learning model without writing a single line of code.