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Google Cloud : GCP Big Data & Machine Learning Fundamentals

  • Gained a strong understanding of cloud-native architectures for large-scale data processing and machine learning

  • Learned to design data pipelines for batch and streaming analytics using Google Cloud’s managed services

  • Explored end-to-end machine learning workflows, including data ingestion, model training, deployment, and monitoring

  • Applied big data and AI tools to real-world business scenarios to support data-driven decision-making and automation

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Tools & Techniques:

  • BigQuery – SQL-based analytics and data warehousing

  • Cloud Storage – scalable storage for raw and processed data

  • Pub/Sub – real-time streaming data ingestion

  • Dataflow (Apache Beam) – unified stream and batch data processing

  • Dataproc / Bigtable – managed Hadoop, Spark, and NoSQL solutions

  • Vertex AI / AI Platform – model training, deployment, and MLOps

  • TensorFlow – foundational machine learning framework

  • ETL/ELT pipelines – data integration, transformation, and orchestration

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