Time Series Forecasting is now a Beta feature in Modulos AutoML. This version also has a preview of the Solutions’ new REST API interfaces.
Modulus AutoML v.0.4.3 is out! You can now configure the resource optimization settings and use AutoML to tackle more Time Series use cases.
Modulus AutoML version 0.4.2 is out. With this version, we are adding Time Series forecasting as an alpha feature. This new Machine Learning workflow type allows you to tackle a range of new use cases, e.g. supply and demand forecasting and capacity planning. In addition to this major new feature, we display more workflow status details and are shipping several other refinements and fixes.
Modulos AutoML version 0.4.1. is out. With the latest AutoML version we give you more insight into the platform and the trained Solutions.
Our latest version of Modulos AutoML is out. With this version, we are giving you a completely redefined ML workflow creation process.
Our latest version of Modulos AutoML is out: v.0.3.5. With this version, we are adding major speed improvements to the assessor, which determines if a neural network has finished training. We are also preparing a new and improved workflow creation, which we are excited to share with our customers soon. We are including a sneak peek here.
The latest version of Modulos AutoML is out: v.0.3.4. With this version, we’re shipping some major new features to help you rapidly build better machine learning models to deploy to production while increasing the robustness of the platform.
We’re very happy to ship the latest version of AutoML: v.0.3.3. We now provide Jupyter notebooks in the Solution and support datetime data!
Release notes AutoML v.0.3.2: Neural architecture search, new data science modules and UI improvements
We’re very happy to ship the latest version of AutoML: v.0.3.2. With this version, we’re making the user interface more intuitive and we’re adding some exciting new data science features including a new AI approach we’re very excited about: neural architecture search!
Every activity in the day-to-day operations of a company produces a wealth of data. Analyzing these data and achieving data-driven insights is even more challenging. In this blog post, I focus on how a company can meet this need with ML by using our AutoML process and AutoML.