𝗧𝗵𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗹𝗼𝗰𝗸𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗻𝗱 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀:
– Collect: Data from sensors, logs, and user input.
– Move/Store: Build infrastructure, pipelines, and reliable data flow.
– Explore/Transform: Clean, prep, and detect anomalies to make the data usable.
– Aggregate/Label: Add analytics, metrics, and labels to create training data.
– Learn/Optimize: Experiment, test, and train AI models.
𝗧𝗵𝗲 𝗹𝗮𝘆𝗲𝗿𝘀 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝗯𝗲𝗰𝗼𝗺𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁:
– Instrumentation and logging: Sensors, logs, and external data capture the raw inputs.
– Data flow and storage: Pipelines and infrastructure ensure smooth movement and reliable storage.
– Exploration and transformation: Data is cleaned, prepped, and anomalies are detected.
– Aggregation and labeling: Analytics, metrics, and labels create structured, usable datasets.
– Experimenting/AI/ML: Models are trained and optimized using the prepared data.
– AI insights and actions: Advanced AI generates predictions, insights, and decisions at the top.
𝗪𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻 𝗮𝗻𝗱 𝗸𝗲𝘆 𝗿𝗼𝗹𝗲𝘀:
– Data Infrastructure Engineers: Build the foundation — collect, move, and store data.
– Data Engineers: Prep and transform the data into usable formats.
– Data Analysts & Scientists: Aggregate, label, and generate insights.
– Machine Learning Engineers: Optimize and deploy AI models.
𝗧𝗵𝗲 𝗺𝗮𝗴𝗶𝗰 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗶𝗻 𝗵𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝗹𝗮𝘆𝗲𝗿𝘀 𝗮𝗻𝗱 𝗿𝗼𝗹𝗲𝘀 𝘄𝗼𝗿𝗸 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿. 𝗧𝗵𝗲 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝘆𝗼𝘂𝗿 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻, 𝘁𝗵𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗔𝗜.
