This blog discusses an AI-driven system that uses large language models (LLMs) to empower employees by identifying skill gaps and creating personalized learning paths. The system acts as a personal career coach, providing tailored feedback and growth opportunities.

Fabric wouldn’t be an end-to-end data analytics platform without data science, so in this blog we will explore that data science and machine learning capabilities of Microsoft Fabric and assess where the platform fits in the completive data science landscape.

Explore the world of AI with Azure OpenAI Service, offering secure access to cutting-edge language models like OpenAI GPT, Codex, and DALL-E within the Azure ecosystem. This article delves into the differences between OpenAI and Azure OpenAI, providing valuable insights to help you choose the ideal solution for your data protection and AI implementation needs.

Are you struggling to deploy your machine learning models in the cloud? With so many options available, it can be overwhelming to know where to start. In this blog, we'll explore how Azure's Managed Endpoints can simplify the deployment process and provide a user-friendly interface for deploying and managing machine learning models.

Struggling to choose a machine learning platform for building and deploying models? Check out our blog on 10 reasons why Azure Databricks for machine learning is a great choice.

Knowing your customers inside out is key for any business. But getting a full understanding of what they want and need can be challenging. That's where machine learning (ML) comes in. By using ML-driven customer 360 views, you can get a better understanding of your customer's buying habits, preferences, and needs. This can then be used to make your marketing more personal, and improve the customer experience while increasing sales through cross-selling and upselling.

Learn how to use a classification model on a Kaggle dataset to drive quantifiable business value. This guide covers defining the business problem, cleaning the data, building and tuning the model, and more

Are you having trouble getting value out of your company's unstructured data? Optical character recognition (OCR) could be the answer. OCR technology allows businesses to turn unstructured documents and images into structured data, enabling them to make informed decisions. This technology can be used in various industries, including healthcare, finance, legal, and retail, to streamline processes and gain valuable insights. Discover how OCR can transform your business now.

Customer retention is important for the success of a business and can be improved through the use of machine learning models to predict churn. FLAML is a tool that can help businesses easily build these models. By retaining customers and preventing them from switching to competitors, businesses can increase revenue, save costs, and improve brand loyalty.

MLOps aims to resolve the challenges of getting machine learning models and processes into production for operational use and one of the major challenges is how to manage features, data pipelines and ensure consistency between training and production. This is what Feature Stores were designed to do. This blog will introduce you to the basics of Feature Stores and how they solve one of the largest impediments to machine learning success.

The future of data is AI. However, most companies still face a challenge when it comes to productionising machine learning models. Last week, at the AI and Data summit, Databricks unveiled MLFlow 2.0, a new feature coming soon that features MLflow Pipelines to accelerate the deployment of machine learning models.

Wondering how to create the best marketing strategy to reach out to different customer groups? Do you know which groups of customers are most likely to buy your product? Clustering your customers into segments based on profiles, behaviours and buying patterns is the answer. This article provides a great way to jump-start your clustering project using pre-built code designed by Databricks.

Do you know what the 10 most commonly useful clustering algorithms are? If you wondering what they are, then this article is for you.

This article uses an easy and simple example to explain what clustering is and how it is being used in business to solve problems.

Automated Machine Learning, or AutoML, has gained a lot of popularity. Recently, cloud providers have introduced the capability of AutoML for computer vision tasks. In this article, we explore the concept of AutoML for vision and see how computer vision is revolutionising many industries.

10 ways AI & Machine learning can be used to drive revenue in Insurance & Brokers.

Do you know exactly how many of your customers are leaving? Do you know why your customers are leaving? Are you able to predict which customers will leave the service? This article will show you how to predict churn using Azure Synapse Analytics and Azure Machine Learning.

Feature stores are rapidly gaining popularity in the machine learning environment. Find out what feature stores are all about and the benefits they offer when implemented in a machine learning pipeline.