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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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AI Project 5 things You need to be Successful

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Suppose you have got an opportunity to create a project on AI. Try implementing these five before the start. These five are Learning, Programming Language, Knowledge representation, Problem Solving, and Hardware.

Ensure These 5 Things Done, if you want to be your AI Project Successful

1. Learning Process.

What is learning? - adding knowledge to the storehouse, and improving its performance. The success of an AI program depends on two things- the extent of wisdom it has and how frequently it acquires it. Learning agents consist of four main components. They are the:
  • The Learning element - is part of the agent responsible for improving its performance. 
  • The Performance element- is the part that chooses the actions to take. 
  • Critics, that tell the learning element of how the agent is doing. 
  • The Problem generator - suggests actions that could lead to new information experiences.

2. Programming Language.

  • LISP and Prolog are the primary languages used in AI programming.
  • LISP (List Processing): LISP is an AI programming language developed by John McCarthy in 1950. LISP is a symbolic processing language that represents information in lists and manipulates lists to derive information.
  • PROLOG (Programming in Logic): Prolog, which is developed by Alain Colmeraver and P. Roussel at Marseilles University in France in the early 1970s. 
  • Prolog uses the syntax of predicate logic to perform symbolic, logical computations.

Artificial Intelligence Project Know these Five Before Start
Artificial Intelligence Project Know these Five Before Start

3. Knowledge Representation.

The quality of the result depends on how much knowledge the system acquires. You should represent the current knowledge efficiently. Hence, knowledge representation is a vital component of the system. The best-known representations schemes are:
  • Associative Networks or Semantic Networks
  • Frames
  • Conceptual Dependencies and
  • Scripts

4. Problem Solving.

The objective of this particular area of research is how to implement the procedures on AI systems to solve problems as humans do.

The inference process should also be equally fit to obtain satisfactory results. Inference-process, you can be divided into brute and heuristic search procedures.

5. Hardware.

Most of the AI programs, implemented on Von Neumann machines. However, for AI programming, dedicated workstations have emerged - classified into one of the following four categories:
  • SISD, Single Instruction Single Data Machines
  • SIMD, Single Instruction Multiple Data Machines
  • MISD, Multiple Instruction Single Data Machines
  • MIMD, Multiple Instruction Multiple Data Machines

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