What should a ML engineer care about when building AI models with Tensorflow and Keras? | by Jerel Velarde | Mar, 2024

What is TensorFlow?

TensorFlow is an open-source software library for numerical computation using data flow graphs. It was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. However, TensorFlow is flexible enough to be used across a multitude of scientific domains.

TensorFlow provides a comprehensive, flexible ecosystem of tools, libraries, and community resources that allows researchers to push the state-of-the-art in ML, and developers to easily build and deploy ML-powered applications. It offers multiple levels of abstraction so you can choose the right one for your needs. At its core, TensorFlow facilitates the development of algorithms that can process large amounts of data, making it a cornerstone technology for machine learning and deep learning.

What is Keras?

From Keras itself: Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch.

Keras is:

• Simple — but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.
• Flexible — Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you’ve already learned.
• Powerful — Keras provides industry-strength performance and scalability: it is used by organizations including NASA, YouTube, or Waymo.

When building AI models with TensorFlow and Keras, a machine learning engineer should consider a variety of factors to ensure the development of robust, efficient, and effective models. Here are some key considerations:

Data Preparation and Preprocessing:

Data Quality: Ensure data is clean, relevant, and diverse. Handle missing values, outliers, and duplicates as necessary.

Feature Engineering: Select, modify, or create new features to improve model performance.

Data Augmentation: Use techniques to increase the diversity of your training set, especially important in tasks like image recognition.

Normalization/Standardization: Scale data to improve model stability and convergence speed.

Model Architecture:

• Selection of Model Type: Choose the appropriate model architecture based on the problem (e.g., CNNs for image tasks, RNNs/LSTMs for sequential data).
• Layer Configuration: Carefully configure the layers and neurons to balance between underfitting and overfitting.
• Activation Functions: Choose suitable activation functions (e.g., ReLU, Sigmoid) for different layers based on the specific task.

Training Process:

• Optimization Algorithms: Select the right optimizer (e.g., Adam, SGD) that best fits the problem.
• Learning Rate: Determine a suitable learning rate, considering strategies like learning rate schedules or adaptive learning rates to improve training.
• Batch Size: Choose an appropriate batch size for efficient and effective training.
• Regularization Techniques: Apply techniques (e.g., dropout, L1/L2 regularization) to prevent overfitting.
• Callbacks: Utilize callbacks in Keras for actions like model checkpointing, early stopping, and learning rate adjustments.

Validation and Testing:

• Splitting Data: Properly split the dataset into training, validation, and testing sets to evaluate model performance.
• Cross-Validation: Use cross-validation techniques for more reliable evaluation of model performance.
• Performance Metrics: Choose appropriate metrics (accuracy, precision, recall, F1 score, etc.) for evaluating model performance based on the problem.

Experimentation and Iteration:

• Hyperparameter Tuning: Use techniques like grid search, random search, or Bayesian optimization to find the optimal model parameters.
• Model Comparison: Compare different models and configurations to find the best solution for the problem.

Deployment and Monitoring:

• Model Serialization: Understand how to save and load models for deployment.
• Scalability: Ensure the model can handle the expected load and latency requirements in production.
• Monitoring: Set up monitoring for model performance and data drift over time to maintain effectiveness.

Ethics and Fairness:

• Bias and Fairness: Be aware of and mitigate biases in data and model predictions to ensure fairness across different groups.
• Transparency and Explainability: Implement methods to explain model decisions, enhancing trust and transparency.

Environmental Impact:

• Efficiency: Optimize model size and training strategies to minimize environmental impact, considering the computational resources required.

Legal and Regulatory Compliance:

• Data Privacy: Adhere to data privacy laws (e.g., GDPR, CCPA) when handling personal information.
• Model Governance: Ensure compliance with industry standards and regulations regarding model development and deployment.

By carefully addressing these aspects, ML engineers can develop AI models that are not only effective and efficient but also ethical, transparent, and sustainable.