TensorFlow Data Science Model Development Training Course

Course overview

Data science teams often reach a point where exploratory notebooks and baseline models are no longer enough. They need repeatable TensorFlow workflows that can ingest high-volume data, detect data quality issues, train reliable models, explain performance trade-offs, and package models for deployment. This course addresses the gap between knowing machine learning concepts and building TensorFlow solutions that can be reviewed, reproduced, monitored, and improved by a delivery team.

Participants develop production-oriented model development skills using TensorFlow and Keras. They prepare structured and unstructured datasets with tf.data pipelines, build neural network architectures, apply feature engineering and preprocessing layers, tune hyperparameters, handle class imbalance, evaluate models with appropriate metrics, and diagnose errors with TensorBoard. The course also covers validation with TensorFlow Data Validation, model versioning practices, SavedModel export, inference design, and performance optimisation for training and serving.

Teaching combines instructor demonstrations with guided coding labs built around a realistic business prediction case. Participants progressively create an end-to-end TensorFlow project: a documented data pipeline, validated training dataset, trained and evaluated model, TensorBoard experiment evidence, exported model artefact, and a deployment-ready model development plan. This deliverable gives both the participant and their manager a concrete basis for applying the methods to an organisational use case.

The course is suited to data scientists, machine learning engineers, analysts moving into model development, and technical leads responsible for establishing consistent deep learning practices. It is particularly valuable for teams standardising on TensorFlow or replacing ad hoc model-building workflows with governed, testable pipelines.

Course objectives

By the end of this course, participants will be able to:

• Build reproducible TensorFlow input pipelines with tf.data for batching, shuffling, caching, prefetching, and dataset splits
• Create Keras models using Sequential, Functional API, custom layers, callbacks, and reusable preprocessing components
• Validate training data with TensorFlow Data Validation and document schema expectations, anomalies, and remediation actions
• Train classification and regression models with appropriate loss functions, optimisers, regularisation, and class-weighting methods
• Evaluate model quality using confusion matrices, precision-recall curves, ROC-AUC, calibration checks, residual analysis, and business thresholds
• Diagnose training behaviour through TensorBoard metrics, histograms, embeddings, profiling traces, and experiment comparisons
• Optimise model performance through hyperparameter search, learning-rate scheduling, early stopping, transfer learning, and distributed training patterns
• Package a versioned TensorFlow SavedModel with model documentation, inference inputs, evaluation evidence, and deployment recommendations

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need to turn experimental machine learning notebooks into reproducible TensorFlow model pipelines
• Machine Learning Engineers — who build, tune, package, and operationalise TensorFlow models for production use
• Senior Data Analysts — who are moving from statistical modelling and dashboards into supervised machine learning development
• AI Engineers — who need practical Keras and TensorFlow techniques for developing deep learning solutions
• Data Science Team Leads — who must establish consistent training, evaluation, and model handover practices across projects
• Software Engineers — who integrate trained models into applications and need to understand TensorFlow model artefacts and inference requirements

Requirements and prerequisites

Participants should be comfortable writing and debugging Python functions, working in Jupyter notebooks or a similar development environment, and manipulating tabular data with pandas or NumPy. They should understand core supervised learning concepts including training and test splits, overfitting, features, labels, classification, regression, and basic evaluation metrics. Prior exposure to a machine learning library such as scikit-learn is helpful. Participants do not need prior TensorFlow, Keras, deep learning, cloud platform, or MLOps experience. This is not a beginner Python or introductory statistics course; those new to coding or machine learning should prepare before attending.

Training methodology

Each day combines short instructor-led technical briefings with live TensorFlow coding, individual lab work, and facilitated review of results. Participants work with a business prediction dataset and progressively extend one project rather than completing disconnected exercises. Labs require learners to inspect data, write tf.data pipelines, train Keras models, interpret TensorBoard evidence, and justify modelling decisions against measurable criteria. Small-group case discussions address leakage, fairness, deployment constraints, and model-risk trade-offs. The final day includes an application-planning workshop that maps the completed project patterns to participants' own data, platforms, and delivery controls.

Course outline

Day 1: TensorFlow model development foundations

• TensorFlow ecosystem architecture and execution modes
• TensorFlow tensors, shapes, dtypes, and broadcasting
• Eager execution and tf.function graph compilation
• Keras model development workflow
• Supervised learning problem framing and target definition
• Train, validation, and test split design
• Reproducibility with random seeds, environments, and configuration files

Workshop: Set up a reproducible TensorFlow project and build a baseline Keras classifier for a business prediction dataset.

Day 2: Data ingestion and tf.data pipelines

• Reading CSV, TFRecord, image, and text data sources
• Dataset construction with tf.data.Dataset
• Mapping, filtering, batching, shuffling, and repeating transformations
• Caching and prefetching for input-pipeline performance
• Data partitioning and leakage prevention
• Handling missing values and malformed records
• Pipeline testing with representative data samples

Workshop: Create and benchmark a tf.data input pipeline that produces clean, batched training and validation datasets.

Day 3: Feature engineering and data validation

• Numeric scaling, clipping, binning, and normalisation
• Categorical encoding with Keras preprocessing layers
• Text vectorisation and sequence preparation
• Feature crosses and embedding-ready categorical features
• TensorFlow Data Validation statistics generation
• Schema definition and anomaly detection
• Training-serving skew and data-drift considerations

Workshop: Define a TensorFlow Data Validation schema and implement reusable Keras preprocessing layers for the project dataset.

Day 4: Keras architecture design

• Sequential API for straightforward neural networks
• Functional API for multi-input and branching models
• Dense layers, activation functions, and parameter initialisation
• Embedding layers for high-cardinality categorical variables
• Dropout, batch normalisation, and weight regularisation
• Custom layers and custom model components
• Model summaries, shape inspection, and architecture review

Workshop: Develop and compare two Keras architectures, including a multi-input model with numerical and categorical features.

Day 5: Training strategies and optimisation

• Loss-function selection for regression and classification
• Optimisers including SGD, Adam, and AdamW
• Learning-rate schedules and warm-up strategies
• Early stopping and model checkpoint callbacks
• Class weights, focal loss, and resampling for imbalance
• Cross-validation constraints for deep learning workflows
• Experiment configuration and run comparison discipline

Workshop: Train an imbalanced classification model using callbacks and class-weighting, then compare optimisation strategies.

Day 6: Model evaluation and decision quality

• Confusion matrices and threshold-based classification decisions
• Precision, recall, F1 score, ROC-AUC, and PR-AUC
• Probability calibration and reliability assessment
• Regression residuals, MAE, RMSE, and error segmentation
• Baseline comparison and statistical evaluation discipline
• Error analysis by cohort, feature range, and data quality segment
• Translating model metrics into business acceptance criteria

Workshop: Produce an evaluation report that recommends an operating threshold based on error costs and stakeholder requirements.

Day 7: TensorBoard diagnostics and explainability

• TensorBoard scalar dashboards for loss and metrics
• Histogram and distribution analysis for weights and activations
• Embedding Projector for representation inspection
• TensorBoard Profiler for input and training bottlenecks
• Comparing experiment runs and identifying divergence
• Gradient inspection and vanishing-gradient diagnosis
• Feature attribution and local explanation approaches

Workshop: Use TensorBoard to diagnose an underperforming training run and document the corrective actions taken.

Day 8: Advanced TensorFlow modelling patterns

• Transfer learning with pretrained TensorFlow models
• Fine-tuning strategies and frozen-layer management
• Convolutional neural networks for image classification
• Recurrent and attention-based sequence modelling concepts
• Custom training loops with GradientTape
• Distributed training with tf.distribute strategies
• GPU utilisation and memory-management considerations

Workshop: Fine-tune a pretrained TensorFlow model and compare its learning curve and accuracy with a model trained from scratch.

Day 9: Model packaging and deployment readiness

• SavedModel format and TensorFlow model signatures
• Inference input contracts and preprocessing consistency
• Model versioning and artefact naming conventions
• Batch, real-time, and edge inference patterns
• Latency, throughput, memory, and cost trade-offs
• Model documentation with intended use and limitations
• Monitoring requirements for drift, performance, and failures

Workshop: Export a versioned SavedModel with an inference signature, model card, and deployment-readiness checklist.

Day 10: End-to-end TensorFlow delivery workshop

• Reviewing the complete TensorFlow development lifecycle
• Code organisation and reusable pipeline components
• Peer review of data, modelling, and evaluation decisions
• Model-risk review for bias, leakage, and misuse
• Deployment handover requirements for engineering teams
• Prioritising improvements from TensorBoard and evaluation evidence
• Application planning for organisational use cases

Workshop: Present a completed TensorFlow model development pack and create a 90-day implementation plan for an identified workplace use case.

Tools & standards covered

TensorFlow, Keras, TensorBoard, TensorFlow Data Validation

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions