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.
Navigating the rapidly evolving AI development landscape poses a distinct challenge, especially when choosing the appropriate tools for developing Large Language Model (LLM) applications. This blog explores Prompt Flow, Semantic Kernel, and LangChain, leading technologies each offering unique features to enhance AI-driven applications.
Read MoreAt Advancing Analytics, we have a question we like to ask new hires: Are you a plate or a bowl? In a chat with colleagues someone made an offhand comment about Morpheus from the Matrix offering a plate or bowl instead of the red or blue pill from the movie. This comment made me want to try and generate this image using AI and was also an interesting example of prompt refinement.
Read MoreGenerative AI is accelerating a global workforce disruption, the likes of which we have not seen since the Industrial Revolution. But where do you start? How do you ensure you are using AI in legal, responsible, ethical ways? How do you decide you are ready to start employing Generative AI?
Read MoreHow Intel technology stack improved model training in Advancing Analytics' custom solution for the financial sector.
Read MoreRetrieval Augmented Generation (RAG) is a framework that provides a more accurate and reliable solution for generating content and interacting with users. RAG addresses the limitations of large language models (LLMs) and helps businesses struggling to generate relevant and accurate content while maintaining credibility and transparency in their interactions with customers.
Read MoreThis 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 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 MoreStruggling 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.
Read MoreWe are thrilled to announce our partnership with Dataiku, the leading platform for data science and machine learning. Dataiku's Everyday AI platform offers powerful tools, intuitive capabilities and allows for collaboration between code-first data scientists and non-coding colleagues. The platform can also be integrated with Microsoft Azure and Databricks, driving innovation in the field of data science and ML. With Dataiku, we are able to offer our customers access to the latest in data science and ML tools, enabling advanced and sophisticated solutions to meet their business needs.
Read MoreKnowing 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.
Read MoreLearn 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
Read MoreThis blog discusses the challenges faced by the manufacturing industry and how big data can be utilized to overcome them. The focus is on demand forecasting, and the blog includes a case study demonstrating the effectiveness of using big data for this purpose. The case study shows how data analysis can improve production optimization and supply chain management, leading to more efficient and accurate demand forecasting. The blog also highlights the importance of predictive analytics and the potential for big data to revolutionize the manufacturing industry. Overall, the blog emphasizes the value of big data in overcoming industry challenges and driving manufacturing efficiency.
Read MoreIn this blog, we will discuss the transformer architecture, blockwise training, and the various use cases of large language models such as ChatGPT. We'll explore how these models can be fine-tuned for tasks like language translation, text summarization, text generation, question answering, sentiment analysis, dialogue generation and language modeling. Whether you are a ML engineer or a language enthusiast, this blog post will provide a comprehensive understanding of the latest developments in NLP.
Read MoreAre 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.
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.
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