Artificial Intelligence – Infrastructure Architect

Why should you attend?

Dive into the vast and intricate world of Artificial Intelligence infrastructure with a focus on enabling machines to emulate human intelligence. This course encompasses an array of topics, beginning with the core frameworks like TensorFlow and PyTorch that are instrumental in building Machine Learning (ML) and Deep Learning models. Understand how these frameworks apply to real-world applications such as image recognition, natural language processing (NLP), and time series analysis. The curriculum advances to cover SparkSQL, DataFrames, and Datasets, highlighting their importance in handling large-scale data efficiently. It emphasizes the shift from traditional RDDs to more advanced data abstractions for better performance and ease of use. Delving into big data ecosystems, learners will explore Data Lakes using Hadoop and Spark, gaining insights into data wrangling techniques essential for managing voluminous datasets. Automation of data pipelines is another critical component covered in this course, where students learn to orchestrate workflows using tools like Apache Airflow and Apache NiFi while ensuring data quality and lineage tracking. The program also addresses handling unstructured binary data with Hadoop's file system (HDFS) and MapReduce framework, providing a comprehensive understanding of different data types. Learners will also get acquainted with NoSQL databases through Cassandra for structured big data management, InfluxDB for time series data analysis, and Neo4J for graph-based data modeling. Finally, the course culminates with practical deployment strategies for ML models both on-premises and in the cloud along with versioning and logging practices. Real-world case studies on stock market predictions, image recognition, anomaly detection, text classification, and captioning offer hands-on experience to solidify learning outcomes.