Mastering the Model Building Phase of MLOps: An Expert‘s Guide

As artificial intelligence (AI) and machine learning (ML) become increasingly integral to business operations, organizations are looking for ways to streamline the development and deployment of ML models. This is where Machine Learning Operations (MLOps) comes in.

MLOps is a set of practices and principles that aims to bring the robustness and reliability of DevOps to the field of machine learning. By adopting MLOps, organizations can accelerate the delivery of high-quality ML models while ensuring reproducibility, scalability, and governance.

The Rise of MLOps

The rapid growth of MLOps can be attributed to the realization that traditional model development practices are not well-suited for the scale and complexity of modern ML applications. A few key statistics highlight this trend:

• According to a recent survey by Algorithmia, 50% of organizations take between 8 and 90 days to deploy a single ML model into production[^1]
• The same survey found that 38% of organizations have not yet adopted MLOps practices and only 14% have a mature MLOps practice in place[^1]
• A report by Cognilytica predicts that the global MLOps market size will grow from $350 million in 2019 to $4 billion by 2025[^2]

These numbers underscore the fact that while many organizations are investing in ML, a majority are struggling to operationalize models and derive business value from their investments. MLOps aims to bridge this gap by providing a structured and automated approach to model development and deployment.

The MLOps Workflow

A typical MLOps workflow consists of three main phases:

1. Model Building
2. Model Deployment
3. Model Monitoring

In this post, we‘ll dive deep into the first phase – Model Building. We‘ll explore the steps involved, the challenges encountered, best practices to follow, and the tools available to streamline the process.

The Model Building Process

Model building is a complex and iterative process that involves several steps. While the exact breakdown may vary between organizations, a typical model building workflow includes the following stages:

1. Data Ingestion and Preparation
2. Model Design and Training
3. Model Evaluation and Validation
4. Model Packaging
5. Model Versioning and Registration

Let‘s examine each of these steps in detail.

1. Data Ingestion and Preparation

Data is the foundation of any ML model. The quality and relevance of the data directly impacts the performance of the resulting model. Therefore, the first step in model building is to identify, collect, and prepare the right data for training.

This involves tasks such as:

• Extracting data from various source systems (databases, data lakes, APIs, etc.)
• Cleaning and pre-processing data (handling missing values, outliers, inconsistencies)
• Transforming and normalizing data (scaling, encoding, feature engineering)
• Splitting data into training, validation and test sets
• Versioning and tracking data lineage

Tools like Apache Kafka, Apache NiFi, and Talend can be used for data ingestion, while libraries like Pandas, Dask, and Apache Spark are popular for data wrangling and preparation. For data versioning and lineage tracking, tools like DVC, Pachyderm and Delta Lake are commonly used.

A survey by Anaconda found that data scientists spend around 45% of their time on data preparation tasks[^3]. This highlights the need for automation and tooling to streamline this stage of the model building process.

2. Model Design and Training

With the data prepared, the next step is to design and train the ML model. This involves selecting an appropriate model architecture based on factors like the type of problem, size and structure of the data, latency requirements, and available compute resources.

Some common model types used in ML include:

• Linear models (Linear Regression, Logistic Regression, etc.)
• Tree-based models (Decision Trees, Random Forests, Gradient Boosted Trees)
• Deep learning models (Multi-Layer Perceptrons, Convolutional Neural Networks, Recurrent Neural Networks, Transformers)

The choice of model depends on the specific use case and data characteristics. For example:

• For tabular data with mostly linear relationships, linear models may suffice
• For complex non-linear relationships and unstructured data (text, images), deep learning is often preferred
• For tasks like ad ranking or fraud detection where fast inference is critical, tree-based models are commonly used

Once a model architecture is selected, the model is trained on the prepared data using an appropriate optimization algorithm (e.g. Stochastic Gradient Descent, Adam, etc.). Hyperparameter tuning is performed to find the optimal model configuration.

Popular ML frameworks for model training include Scikit-Learn, TensorFlow, PyTorch, XGBoost, and H2O. For hyperparameter tuning, libraries like Hyperopt, Optuna, and Ray Tune can be used.

To manage the compute resources for training, many organizations use containerization technologies like Docker and orchestration platforms like Kubernetes. This allows for efficient utilization and scaling of resources.

3. Model Evaluation and Validation

Having a trained model is only half the battle. Before a model can be deployed to production, it needs to be rigorously evaluated and validated. This involves assessing the model‘s performance on unseen data and testing for various quality attributes.

Some key considerations in model evaluation include:

• Choice of evaluation metric (accuracy, precision, recall, F1 score, RMSE, etc.)
• Performing cross-validation to get a robust estimate of model performance
• Analyzing model performance across different slices of data (by time period, customer segment, geography, etc.)
• Assessing model fairness and bias
• Explaining model predictions using techniques like SHAP and LIME
• Testing model robustness to input perturbations and distributional shifts

Tools like ML Metadata (MLMD) and Kubeflow Metadata can be used to track and visualize model evaluation results across different runs and experiments.

A study by Accenture found that 79% of organizations have not deployed an ML model due to quality and maintenance concerns[^4]. Rigorous model evaluation is critical to catching these issues early and ensuring only high-quality models make it to production.

4. Model Packaging

Once a model has been trained and validated, it needs to be packaged for deployment. This involves saving the model artifact in a format that can be easily consumed by the production system.

Common model packaging formats include:

• Serialized model objects (pickle, joblib, etc.)
• Containerized models (Docker images)
• Model serving APIs (TensorFlow Serving, MLflow, etc.)
• Compiled models (ONNX, PMML, etc.)

The choice of packaging format depends on factors like the programming language, framework, and deployment environment. For example, if the model is trained using Python and needs to be deployed on a Java-based system, using a language-agnostic format like ONNX or PMML may be preferred.

5. Model Versioning and Registration

The final step in the model building process is to version and register the packaged model in a central repository. This enables tracking of model lineage, reproducibility, and collaboration among team members.

A model registry typically stores the following information:

• Model version and metadata (training data version, hyperparameters, evaluation metrics, etc.)
• Model artifact (the packaged model file)
• Environment and dependencies (language, libraries, frameworks, etc.)
• Associated code and configuration files

Popular model registries include MLflow Model Registry, Azure ML Model Registry, and SageMaker Model Registry. These tools provide APIs and UIs for registering, discovering, and managing models.

Versioning models allows for easy rollback in case of issues and enables controlled experimentation and A/B testing of different model versions in production.

Challenges in Model Building

While the steps outlined above provide a framework for model building, the process is not without challenges. Some common challenges faced by organizations include:

• Data quality and availability – Poor quality or insufficient training data can lead to suboptimal models. Ensuring access to relevant and representative data is a key challenge.

• Experiment tracking and reproducibility – With multiple data scientists working on different experiments, keeping track of results and ensuring reproducibility can be difficult without proper tooling and processes.

• Computational resources – Training complex models can be computationally expensive. Managing and scaling compute resources efficiently is a challenge, especially for organizations with limited infrastructure.

• Model explainability and fairness – As ML models become more complex, explaining their predictions and ensuring they are fair and unbiased becomes harder. This is a major concern for regulated industries and applications with social impact.

• Skill gap – Building high-quality models requires a combination of data science, engineering, and domain expertise. Finding and nurturing talent with this diverse skill set is a challenge for many organizations.

Best Practices for Model Building

To overcome these challenges and build successful ML models, organizations should adopt the following best practices:

• Establish a data governance framework – Implement processes and tools for data discovery, quality control, access management, and lineage tracking. This ensures that data scientists have access to reliable and relevant data for model building.

• Use version control for data and models – Treat data and models like code and version control them using tools like DVC and Git. This enables reproducibility, collaboration, and easy rollback.

• Automate model training and evaluation – Use orchestration tools like Kubeflow Pipelines and Apache Airflow to automate the model training and evaluation process. This reduces manual errors and accelerates experimentation.

• Adopt explainable AI techniques – Use techniques like SHAP, LIME, and Shapley values to explain model predictions. This helps build trust in the model and enables debugging and improvement.

• Implement model governance processes – Establish clear policies and processes for model testing, validation, and approval before deployment. This ensures that only high-quality and safe models are pushed to production.

• Foster collaboration between data scientists and engineers – Encourage close collaboration and knowledge sharing between data scientists and software/ML engineers. This helps bridge the skill gap and ensures that models are built with production requirements in mind.

• Use managed MLOps platforms – Consider using managed MLOps platforms like Amazon SageMaker, Google AI Platform, or Microsoft Azure ML that provide end-to-end capabilities for model building, deployment, and monitoring. This can significantly reduce the operational overhead and time-to-market for ML projects.

The Future of Model Building

As MLOps matures and becomes more widely adopted, we can expect to see several trends and developments in the model building space. Some key areas to watch out for include:

• Automated Machine Learning (AutoML) – AutoML tools like Google AutoML, H2O Driverless AI, and Auto-Keras are becoming increasingly sophisticated. These tools can automate the entire model building process from feature engineering to hyperparameter tuning, making it easier for non-experts to build high-quality models.

• Pre-built models and transfer learning – With the rise of large pre-trained models like BERT, GPT-3, and Stable Diffusion, we can expect to see more organizations leveraging transfer learning to build domain-specific models faster and with less data.

• ML at the edge – As more and more ML models are deployed on edge devices like smartphones, drones, and IoT sensors, we can expect to see a greater focus on model optimization techniques like quantization, pruning, and federated learning to enable efficient inference on resource-constrained devices.

• Responsible AI – With the increasing use of ML in high-stakes applications like healthcare, finance, and criminal justice, there will be a greater emphasis on building models that are fair, transparent, and accountable. Tools and frameworks for assessing and mitigating bias, ensuring data privacy, and explaining model decisions will become more mainstream.

Conclusion

Model building is a critical phase in the ML lifecycle that can make or break the success of an ML project. By adopting a structured approach and following best practices, organizations can accelerate the development of high-quality models that drive real business impact.

However, model building is just one piece of the MLOps puzzle. To truly operationalize ML and drive long-term success, organizations need to also focus on streamlining the deployment and monitoring of models in production.

In the next post, we‘ll explore the challenges and best practices for deploying and operating ML models at scale. Stay tuned!

[^1]: Algorithmia. (2020). 2020 State of Enterprise Machine Learning. Retrieved from https://info.algorithmia.com/hubfs/2019/Whitepapers/The-State-of-Enterprise-ML-2020/Algorithmia_2020_State_of_Enterprise_ML.pdf

[^2]: Cognilytica. (2020). MLOps Market Report. Retrieved from https://www.cognilytica.com/2020/03/03/mlops-market-report-2020/

[^3]: Anaconda. (2020). State of Data Science 2020. Retrieved from https://www.anaconda.com/state-of-data-science-2020

[^4]: Accenture. (2018). AI: Built to Scale. Retrieved from https://www.accenture.com/_acnmedia/Thought-Leadership-Assets/PDF/Accenture-Built-to-Scale-PDF-Report.pdf