When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves.

Although the issue of silos in IT and data management are well known, companies appear to be falling back into this trap by not distributing their artificial intelligence (AI) and machine learning (ML) capabilities across their business. New research from Qlik and IDC revealed that just 20 percent of businesses widely distribute these capabilities across the organization.

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.

Much has been written on the growth of machine learning and its impact on almost every industry. As businesses continue to evolve and digitally transform, it’s become an imperative for businesses to include AI and ML in their strategic plans in order to remain competitive. In Competing in the Age of AI, Harvard professors Marco Iansiti and Karim R. Lakhani illustrate how this can be confounding for CEOs, especially in the face of AI-powered competition.

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.

Neural Guard produces automated threat detection solutions powered by AI for the security screening market. With the expansion of global trends like urbanization, aviation, mass transportation, and global trade, the associated security and commercial challenges have become ever more crucial.

There seems to be universal acceptance that effective use of data can help maximize bottom line value. However, many businesses still aren’t successfully leveraging data to its full extent due to people, processes and technology roadblocks. Thankfully, there is a unifying approach to unlocking the immense opportunity for enterprises to more effectively leverage data to create new products, services and business models.