Data Engineering for AI Projects Training Course

Introduction

The success of any Artificial Intelligence (AI) project, from sophisticated machine learning models to intelligent automation solutions, hinges critically on the quality, accessibility, and reliability of the underlying data. While data scientists focus on model development, it is the often-unsung role of data engineers that lays the essential foundation, ensuring that data is collected, stored, processed, and delivered in a manner fit for AI consumption. In today's data-intensive landscape, organizations are grappling with massive volumes of diverse data, often residing in disparate systems. Without robust data engineering practices, AI projects are plagued by issues like data silos, poor data quality, slow model training times, and ultimately, models that perform suboptimally or fail in production. Many organizations struggle with this foundational aspect, facing challenges in building scalable data pipelines, managing data governance, ensuring data security, and integrating complex data sources for AI workloads. Conversely, a strong data engineering capability empowers data scientists to focus on innovation, accelerates model development and deployment, enhances the accuracy and reliability of AI systems, and unlocks the true potential of data for AI-driven insights. Ignoring the critical importance of data engineering means jeopardizing the success and scalability of all AI initiatives. Our intensive 5-day "Data Engineering for AI Projects" training course is meticulously designed to equip data engineers, aspiring data engineers, data architects, data scientists, DevOps engineers, and IT professionals with the essential knowledge and practical skills required to design, build, and maintain robust, scalable, and efficient data pipelines specifically tailored to the demanding requirements of AI and Machine Learning projects.

This comprehensive program will delve into the core concepts of data engineering for AI, explore various data storage solutions, data processing frameworks, data governance strategies, and MLOps principles. Participants will gain hands-on experience with practical applications, covering data ingestion, transformation, orchestration, and monitoring in real-world scenarios, leveraging tools and technologies commonly used in the industry. By the end of this course, you will be proficient in conceptualizing, planning, and executing the data engineering efforts necessary to support and accelerate your organization's AI initiatives.

Duration

5 Days

Target Audience

The "Data Engineering for AI Projects" training course is crucial for a broad range of technical professionals who are responsible for managing, processing, and providing data for Artificial Intelligence and Machine Learning workloads. This includes:

  • Data Engineers: Seeking to specialize in data pipelines for AI/ML.
  • Aspiring Data Engineers: Looking to enter the field with a focus on AI.
  • Data Architects: Designing data ecosystems that support AI initiatives.
  • Data Scientists: To better understand data pipelines and collaborate effectively with data engineers.
  • Machine Learning Engineers: Focusing on the operationalization of ML models (MLOps).
  • Big Data Developers: Transitioning to roles with an AI/ML focus.
  • DevOps Engineers: Responsible for infrastructure and automation for data and ML pipelines.
  • IT Managers and Cloud Architects: Overseeing data infrastructure for AI projects.
  • Business Intelligence Developers: Moving towards more advanced data processing for AI.
  • Anyone involved in building scalable and reliable data solutions to power AI applications.

Course Objectives

Upon successful completion of the "Data Engineering for AI Projects" training course, participants will be able to:

  • Understand the fundamental role of data engineering in the success of AI/ML projects.
  • Identify and apply various data storage solutions suitable for different AI data types and scales.
  • Design and build scalable and efficient data ingestion pipelines for diverse data sources.
  • Master data transformation techniques and processing frameworks for AI-ready data.
  • Implement data quality, governance, and security best practices for AI datasets.
  • Orchestrate and automate complex data pipelines to support continuous model training and deployment.
  • Understand MLOps principles and the collaboration between data engineers and ML engineers.
  • Evaluate and select appropriate tools and technologies for data engineering in an AI ecosystem.

 Course Modules

Module 1: Foundations of Data Engineering for AI

  • The crucial role of data in AI/ML model performance.
  • Understanding the AI/ML lifecycle and the data engineer's place within it.
  • Key challenges in data for AI: Volume, velocity, variety, veracity.
  • Defining data pipelines: ETL vs. ELT, batch vs. streaming.
  • Introduction to modern data architectures (Data Lake, Data Warehouse, Data Lakehouse).

Module 2: Data Storage Solutions for AI

  • Relational Databases: When and why to use them (e.g., PostgreSQL, MySQL).
  • NoSQL Databases: Scalability and flexibility for various data types (e.g., MongoDB, Cassandra).
  • Data Lakes: Storing raw, unstructured, and semi-structured data at scale (e.g., S3, ADLS, GCS).
  • Data Warehouses: Optimized for analytical queries and structured data (e.g., Snowflake, BigQuery, Redshift).
  • Choosing the right storage solution based on AI project requirements.

Module 3: Data Ingestion and Integration for AI

  • Strategies for ingesting data from various sources (APIs, databases, files, streaming).
  • Batch data ingestion tools and techniques (e.g., Apache Sqoop, cloud-native services).
  • Real-time data ingestion and streaming platforms (e.g., Apache Kafka, Amazon Kinesis, Google Pub/Sub).
  • Data connectors and integration patterns for building robust pipelines.
  • Handling schema evolution and data changes in ingestion.

Module 4: Data Processing and Transformation Frameworks

  • Batch Processing: Leveraging Apache Spark for large-scale data transformations.
  • Stream Processing: Introduction to Apache Flink or Spark Streaming for real-time data.
  • Data cleaning, standardization, and normalization techniques.
  • Feature engineering from a data engineering perspective: Preparing data for ML models.
  • Building scalable and fault-tolerant data transformation jobs.

Module 5: Data Quality, Governance, and Security

  • The importance of data quality in AI: Impact on model performance and trust.
  • Techniques for data profiling and quality checks.
  • Implementing data governance policies: Data ownership, definitions, access controls.
  • Data security best practices: Encryption, access management, auditing for AI datasets.
  • Ensuring data lineage and reproducibility for ML models.

Module 6: Orchestration and Automation of Data Pipelines

  • Introduction to data pipeline orchestration tools (e.g., Apache Airflow, Azure Data Factory, AWS Step Functions, Google Cloud Composer).
  • Scheduling and monitoring data workflows.
  • Automating data refresh and model retraining triggers.
  • Implementing CI/CD for data pipelines.
  • Error handling, logging, and alerting in production data systems.

Module 7: MLOps and Collaboration with ML Engineers/Data Scientists

  • Understanding MLOps principles: Bridging the gap between data engineering, ML, and operations.
  • Data versioning for ML models and experiments.
  • Feature stores: Centralizing and managing features for consistent use across models.
  • Serving data for model inference (online vs. batch).
  • Collaboration tools and best practices for data engineers and ML teams.

Module 8: Cloud-Native Data Engineering for AI & Future Trends

  • Overview of key data engineering services on major cloud platforms (AWS Glue, Azure Data Factory, Google Cloud Dataflow/Dataproc).
  • Serverless data processing architectures.
  • Cost optimization strategies for cloud data pipelines.
  • Emerging trends: Data Mesh, Data Fabric, Data Observability.
  • Developing an action plan for implementing robust data engineering practices for AI in your organization.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Data Engineering For Ai Projects Training Course in El Salvador
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