Modern Analytics A Foundation To Sustained Ai Success

Modern Analytics: A Foundation to Sustained AI Success Microsoft Fabric eBook series volume 2 Modern Analytics: A Foundation to Sustained AI Success 2 Modern Analytics: A Foundation to Sustained AI Success

Data opportunities in the era of AI

Data science projects in Microsoft Fabric

Architecture of a data science project in Microsoft Fabric

Augment Copilot in Microsoft Fabric

Find more value in your data

Next steps Modern Analytics: A Foundation to Sustained AI Success 3 Data opportunities in the era of AI a valuable resource that drives innovation, efficiency and business growth. Commercial AI capabilities today AI is a vast field with multiple branches, each presenting a unique approach to data interpretation, learning mechanisms and decision-making processes. Understanding these diverse approaches of AI aids in comprehending its complexity and the multitude of ways it’s applied. By understanding these various branches, organisations can grasp how they can harness the AI landscape. • Machine learning: Machine learning is an essential subset of AI. It serves as the cornerstone of many AI systems by empowering computers to learn from data and make accurate predictions or deductions. Machine learning operates in a realm where copious amounts of data are generated daily through diverse sources such as text messages, emails, social media posts and numerous sensors embedded within an environment. Data scientists then employ this extensive dataset to train machine learning models capable of making predictions based on discerned patterns. The potential of AI is boundless, with profound implications for industries such as healthcare, smart infrastructure, entertainment, retail, banking, logistics, manufacturing and beyond. The significance of data cannot be emphasised enough with the ongoing transition into an era shaped by AI. Data is created and captured everywhere from devices big and small, applications and interactions. Leading organisations harness data to undergo digital transformations and gain a competitive advantage in their respective industries. The current state of AI has amplified these opportunities exponentially. AI’s ability to analyse and interpret data on an unprecedented scale allows organisations to derive deeper insights, make more informed decisions and create more personalised experiences for customers. For instance, an Australian AgTech company, The Yield, uses sensors, data and AI to provide farmers with insightful information about weather, soil and plant conditions. This knowledge helps them make informed decisions, thus promoting sustainable farming techniques. AI is truly revolutionising how organisations understand and utilise data, turning it into

yield-is-using-data-and-ai-to-help-feed-the-world/ Modern Analytics: A Foundation to Sustained AI Success 4 Machine learning employs several techniques to analyse data and predict outcomes. These techniques include: •Supervised learning: Handles datasets with labels or a predefined structure, where data serves as a teacher to train the machine, enhancing its predictive abilities. •Unsupervised learning: Processes datasets without any labels or structure. It uncovers hidden patterns and relationships by grouping similar data into clusters. •Reinforcement learning: Involves a computer program that interacts with its environment to learn behaviours, with the learning process based on a system of rewards and penalties. • Deep learning: Deep learning is a more advanced branch of machine learning. It deploys artificial neural networks inspired by the structure of the human brain. These networks pose nested questions, with answers leading to related inquiries. Deep learning necessitates extensive training on large datasets, making it particularly suitable for applications such as image recognition. • Generative AI: Generative AI utilises neural networks to scrutinise data, recognise patterns and generate novel outputs such as text, images or code. Microsoft uses generative AI in various applications, including Azure OpenAI Service, GitHub Copilot and Bing Image Creator. • ChatGPT: ChatGPT, built by OpenAI, is a refined model from the GPT-3.5 series. It employs a transformer-based neural network architecture. Trained on an extensive text dataset and refined through conversational interactions, ChatGPT generates responses by considering the context of the conversation and analysing user inputs. ChatGPT is available in preview in Azure OpenAI Service where developers can integrate custom AI-powered experiences directly into their own applications, all backed by the unique supercomputing and enterprise capabilities of Azure.