Data Science & Data Engineering blogs
Thanks for reading. Here you will find a huge range of information in text, audio and video on topics such as Data Science, Data Engineering, Machine Learning Engineering, DataOps and much more. The show notes for “Data Science in Production” are also collated here.
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.
Read MoreFabric 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.
Read MoreExplore 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.
Read MoreAre 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.
Read MoreCustomer 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.
Read MoreMLOps 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.
Read MoreThe 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.
Read MoreWondering 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.
Read MoreDo you know what the 10 most commonly useful clustering algorithms are? If you wondering what they are, then this article is for you.
Read MoreThis article uses an easy and simple example to explain what clustering is and how it is being used in business to solve problems.
Read MoreUsing optimisation algorithms for scheduling in sport (or anything else).
Read MoreHow is AI used in finance? What are the top AI use cases that are transforming the finance industry? What benefits does it bring to the financial industry? Read how AI is transforming the finance industry.
Read MoreWhat is the explainable AI and why is important? How do we implement it? All these questions and more are answered in this article!
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