AI Lesson 19 – AI Workflows | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

AI Workflows

Building an AI system is not just about writing code or training a model. In real-world projects, AI follows a structured workflow that connects data, models, evaluation, and deployment into a continuous process.

Understanding the AI workflow helps developers design reliable systems, avoid costly mistakes, and scale models from experiments to production.

What Is an AI Workflow?

An AI workflow is a step-by-step process that defines how an AI system is built, tested, and maintained. Each stage depends on the previous one, and skipping steps often leads to poor results.

Unlike traditional software workflows, AI workflows are iterative. Models improve over time as more data becomes available.

Real-World Connection

Consider an AI system that predicts whether an email is spam.

  • Emails are collected as data
  • A model learns patterns from labeled examples
  • The model is evaluated for accuracy
  • The system is deployed to filter incoming emails
  • New emails continuously improve the system

This entire cycle is an example of an AI workflow in action.

Core Stages of an AI Workflow

Most AI projects follow these major stages:

  • Data collection
  • Data preprocessing
  • Model training
  • Model evaluation
  • Deployment
  • Monitoring and improvement

Stage 1: Data Collection

Data is the foundation of any AI system. It can come from databases, sensors, user interactions, or third-party sources.

High-quality data leads to better models, while poor data leads to unreliable predictions.

Stage 2: Data Preprocessing

Raw data is rarely usable in its original form. Preprocessing prepares the data for training.

  • Removing missing or incorrect values
  • Normalizing numerical data
  • Encoding text or categorical data

This step ensures the model receives consistent and meaningful input.

Stage 3: Model Training

During training, the AI model learns patterns from the data. The model adjusts its internal parameters to reduce prediction errors.

Training often requires multiple experiments to find the best model configuration.

Stage 4: Model Evaluation

After training, the model is evaluated using unseen data. This step measures how well the model generalizes to new inputs.

Common evaluation metrics include accuracy, precision, recall, and error rate.

Stage 5: Deployment

Deployment makes the AI model available for real users. This may involve integrating the model into an application, API, or cloud service.

A model that performs well in testing may still fail in real-world conditions, making deployment a critical stage.

Stage 6: Monitoring and Improvement

Once deployed, AI systems must be monitored continuously.

  • Detect performance degradation
  • Identify data drift
  • Retrain models with new data

AI workflows are never truly finished — they evolve with time and usage.

Simple Workflow Representation

Below is a simplified example of how an AI workflow might be represented programmatically. 


def ai_workflow():
    collect_data()
    preprocess_data()
    train_model()
    evaluate_model()
    deploy_model()
    monitor_system()

This example shows the logical flow of an AI system, not an exact implementation.

Practice Questions

Practice 1: What is the foundation of every AI workflow?



Practice 2: Which stage measures how well a model performs on unseen data?



Practice 3: Which stage ensures the AI system continues to perform well after deployment?



Quick Quiz

Quiz 1: Which step prepares raw data for training?





Quiz 2: Making a model available to users is called?





Quiz 3: AI workflows are best described as?





Coming up next: Data for AI — understanding how data quality, structure, and labeling directly impact AI systems.

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