Azure AI/ML

Transform your business with artificial intelligence

As an infrastructure, data & AI, and digital & app innovation Microsoft Solutions Partner, Neal Analytics has been a key player in the artificial intelligence and machine learning space since 2011. We leverage our deep expertise in cloud, data engineering, and AI technologies to build custom solutions leveraging Artificial Intelligence, Machine Learning, Deep Learning, or any other technology required to solve unique business challenges.

With deep expertise in building AI-driven solutions for a wide variety of use cases, such as automatically discovering new customer segments, enabling more cost-effective maintenance by identifying failing equipment, and SKU assortment and promotion optimization, Neal Analytics can support virtually any organization’s AI-related goals and initiatives.

Neal Analytics leverages a multitude of AI and Machine Learning services, such as Azure Cognitive Services and Azure Machine Learning, to build and deploy custom-made models and algorithms to create quick, scalable, and cost-efficient solutions for unique business challenges.

Over the years, Neal has leverage Azure AI/ML technologies in the cloud and at the edge to help businesses solve real-world challenges, such as creating a more efficient supply chain through intelligent supply chain optimization, tearing down data silos to enable superior marketing personalization, automating inefficient or manual processes, and more.

AI ML DL Diagram

Neal leverages Azure Cognitive Services and Azure Machine Learning to build and deploy models and algorithms that help companies create intelligent supply chains, personalize customer experiences, automate inefficient processes, and more.

We use Azure AI/ML technologies to help businesses solve real-world problems from the cloud and at the edge.

Artificial intelligence, machine learning, and deep learning explained

Artificial intelligence

Artificial intelligence (AI) has become a common buzzword in the technology and IT industries in recent years. While AI solutions capable of learning via Machine Learning models are increasingly common, AI itself refers to a technology designed to mimic human intelligence or otherwise perform actions a human normally would. AI leverages data and algorithms to grant machines the ability to perform specific tasks, such as an interactive chatbot capable of conducting internet searches and return results based on user queries.

Businesses across virtually any industry, whether it be healthcare, technology, retail & CPG, energy, or anything in between, can take advantage of AI’s advanced computing and analytical capabilities to solve problems and improve performance.

Machine learning

Frequently used in conjunction with AI solutions to inform one another, Machine learning (ML) is a type of technology designed to make intelligent decisions based on previous inputs’ outcomes.

Machine Learning leverages algorithmic models trained by being fed data rather than programming each step individually. This enables ML models to learn and adapt to provide different results and outcomes based on different inputs. A well-trained model can identify minor variances between a wide range of inputs and act intelligently based on those variances.

Example use cases for ML include performing predictive maintenance, forecasting demand, identifying new customer segments, personalizing marketing and sales offerings for customers, and more.

blue neural network

Deep Learning (Deep Neural Networks)

Deep Learning (DL) is an evolution of machine learning techniques inspired by the architecture of human neural networks.

These artificial deep neural networks (DNN) leverage vast amounts of data and hidden layers (from a handful to hundreds, hence “deep”) to make connections and weigh inputs, allowing them to solve more complex problems than traditional statistically ML capabilities would allow. Deep neural networks are particularly adept at identifying unstructured data patterns, such as sound, images, and video.

Examples of use cases for DL include image analysis, voice recognition or synthesis, machine translation, recommendation engines, predictive analysis, and many more.

Autonomous Systems featured image

Autonomous Systems

Autonomous Systems are a subset of deep learning-powered AI solutions.

Autonomous Systems combine the power of Machine Teaching, Deep Reinforcement Learning (DRL), and simulations to solve real-world complex business process challenges across use cases and industries.

To build these solutions, it’s key to leverage platforms such as Microsoft Project Bonsai on Azure to integrate all these elements in a powerful, scalable, and flexible environment.

Learn more about Autonomous Systems