Few things in life are certain, least of all roadmaps. There is a saying I love, apart from the one above, which says “if you want to hear God laugh, tell him/her your plans”. Nowhere is that more true than in software development in a startup. There are grand ideas put forward, people often vie with one another, in short, life happens.

Although the title might sound like a collaboration of two music bands with really bad names, this blog is all about understanding how computer vision and machine learning can be used to improve safety and security in a harsh and dangerous environment of a construction site. The construction industry is one of the most dangerous industries according to the common stats from OSHA.

One of the most leading questions we often receive is, “How does ClearML Compare to..”. I am sure this is the same for any Open Source product. People always want to find the best. The sad truth is, of course, there usually is no “right answer”. What one person needs, another may not. I am sure that, whichever language you speak natively, there is some saying. In English it would be “one mans rubbish, is another mans gold”.

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

We have three big announcements to our community today, and I wanted to talk to you about them: One, Allegro Trains is changing its name, two, we’re adding a completely new way to use Trains, and three, we’re announcing a bunch of features that make Trains an even better product for you! Read all about it on our blog at Clear.ml, our new website for our open source suite of tools.

Deep learning has evolved in the past five years from an academic research domain, to being adopted, integrated and leveraged for new dimensions of productivity across multiple industries and use cases, such as medical imaging, surveillance, IoT, chatbots, robotic,s and many more. From NLP to computer vision, deep learning has been breaking the barriers of SOTA algorithms and providing results that were, otherwise, impossible to achieve.

The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess.

Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.

The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons for this difficulty is that the training procedure of machine learning models includes multiple hyperparameters that affect how the training process fits the model to the data.

So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.