While machine learning models offer solutions to a multitude of business challenges, selecting the appropriate one tailored to a specific business use case can be quite challenging. The biggest challenges in machine learning adoption, including scaling up (43%), versioning ML models (41%), and securing senior buy-in (Statista, 2021). It is crucial to adhere to best machine learning practices throughout the entire ML lifecycle to ensure that the model is well-prepared for efficient production.
In light of this, we have compiled a comprehensive post outlining best practices to maximize the potential of machine learning in key aspects, such as: Objectives & Metrics, Infrastructure, Data, Models, and Code.
Objective & Metric Best Practices
The initial and fundamental step in designing an ML model is to define the business objective. Unfortunately, many ML projects commence without clear and well-defined objectives, setting them on a path towards failure. ML models necessitate precise objectives, parameters, and metrics to thrive. In some instances, organizations may lack a specific and clearly defined goal for their ML models. While they may seek insights from the available data, a vague objective is insufficient for the development of a successful ML model.
It is essential to establish a clear and measurable objective and identify the metrics for assessing success. Failing to do so can lead to significant time wastage or the pursuit of unattainable goals. Below are key best practices to consider when formulating the objectives for your machine learning solutions:
1. Assess the Necessity of the ML Model
Before embarking on the development of an ML model, it's essential to determine whether it is truly profitable. While many organizations aspire to embrace the ML trend, not every problem can be effectively solved with machine learning. Evaluating the specific use case is crucial. Small-scale businesses, in particular, need to exercise caution, as ML models consume resources that may strain their budgets. Identifying areas of difficulty and ensuring the availability of relevant data are foundational steps for developing a profitable ML model and enhancing organizational efficiency.
2. Gather Data for Your Chosen Objective
While use cases are important, data availability is a pivotal factor in the successful implementation of an ML model. For an organization's initial foray into ML, it's advisable to select objectives supported by extensive data sets.
3. Set Up Clear Metrics
Start by defining the use cases that necessitate the creation of the ML model. Develop technical and business metrics aligned with these use cases. A well-defined objective and corresponding metrics are key to maximizing the performance of the ML model. Thoroughly examine the existing processes aimed at achieving business goals. Identifying the challenges in the current process is a crucial step in determining where automation can be applied. Recognizing deep learning techniques that can address these challenges is essential.
Infrastructure Best Practices
Before dedicating time and resources to the construction of an ML model, it is imperative to verify the presence of an adequate infrastructure to support the required model. The development, training, and implementation of a machine learning solution are profoundly influenced by the available infrastructure. A recommended best practice is to construct an encapsulated ML model that operates autonomously. Comprehensive testing and sanity checks on models are indispensable prerequisites for deployment.
Here are some infrastructure best practices to consider when formulating your machine learning solutions:
1. Optimal Infrastructure Components
Machine learning infrastructure encompasses diverse components, related processes, and proposed solutions for ML models. Integrating machine learning into business operations entails the evolution of the infrastructure with AI technology. It is advisable for businesses to refrain from investing in the entire on-premises infrastructure before embarking on ML model development. Taking into consideration various factors, including containers, orchestration tools, hybrid environments, multi-cloud setups, and agile architecture, they should be gradually implemented to ensure enhanced scalability.
2. Cloud-based vs. On-premise Infrastructure
When enterprises embark on machine learning architecture, it is advisable to initially leverage cloud infrastructure. Cloud infrastructure offers cost-effectiveness, low maintenance, and seamless scalability. Industry giants provide robust support for cloud-based infrastructure, and customizable ML platforms with comprehensive features are readily available. Cloud-based infrastructure entails lower setup costs and benefits from strong support from ML-specific service providers, accommodating scalability with a range of computing clusters.
On the other hand, on-premise infrastructure may feature readily available learning servers such as Lambda Labs and Nvidia Workstations. Building deep learning workstations from scratch is also an option. However, in-house infrastructure requires a substantial initial investment. It offers enhanced security benefits, particularly when deploying multiple ML models for enterprise-level automation. Ideally, ML models should utilize a combination of cloud-based and on-premise infrastructure, tailored to different requirements.
3. Ensure Scalability in Infrastructure
Your ML model's infrastructure should align with your business practices and future objectives. It's essential that the infrastructure supports distinct training and serving models. This division allows ongoing model testing with advanced features without disrupting the deployed serving model. Implementing a microservices architecture is pivotal for achieving encapsulated models.
Data Best Practices
For the successful development of ML models, thorough data processing is paramount. Data defines the system's objectives and holds a pivotal role in training ML algorithms. Both model performance and evaluation hinge on the use of suitable data.
Here are some general guidelines to consider when preparing your data:
1. Data Quantity
Building ML models necessitates a substantial volume of data. Raw data may be unrefined, it's essential to extract actionable insights from it before proceeding with ML model development. Initiate the data collection process within your organization's existing systems, which provides the data metrics required for building the ML model. In cases of limited data availability, transfer learning can be employed to gather additional data. Once raw data is available, apply feature engineering to preprocess the data. Transforming raw inputs into features is vital for the ML model design phase.
2. Data Processing
Data processing begins with data collection and preparation. Feature engineering is applied during data preprocessing to establish correlations between essential features and the available data. Data wrangling metrics are employed during interactive data analysis. Exploratory data analysis leverages data visualization for understanding data, conducting sanity checks, and validating the data. As the data processing workflow matures, data engineers incorporate continuous data ingestion and appropriate data transformations for multiple data analytics entities. Data validation is an imperative step at each iteration of the ML or data pipeline for model training. The detection of data drift necessitates ML model retraining, while data anomalies require the suspension of pipeline execution until they are resolved.
3. Data Preparation
Understanding and implementing data science best practices significantly contribute to data preparation for use in machine learning solutions. Datasets should be categorized based on features and thoroughly documented to ensure their usability throughout the ML lifecycle.
Model Best Practices
When the data and infrastructure are ready, it is time to choose the perfect ML model. Multiple teams work with various technologies, some of which may or may not overlap. You need to select an ML model that can support the existing technologies. Data science experts may lack programming expertise and could be using outdated technology stacks. On the other hand, software engineers may be employing the latest and experimental technologies to achieve the best results. The ML model must support older models while accommodating newer technologies. The selected technology stacks must be cloud-ready, even though in-house servers are currently in use.
The following are the most important best practices for model selection:
1. Build a Robust Model
Within the ML model pipeline, the validation, testing, and monitoring of ML models play a critical role. Ideally, model validation should be completed before transitioning to production. Robustness metrics should be established as vital benchmarks for model validation. Model selection should be based on these robustness metrics. If the chosen model cannot be enhanced to meet benchmark standards, it should be abandoned in favor of a different ML model. The definition and creation of practical test cases are essential for continuous ML model training.
2. Develop and Document Model Training Metrics
Constructing incremental models with checkpoints enhances the resilience of your machine learning framework. Data science encompasses a multitude of metrics, which can be bewildering. Prioritize performance metrics over fancy ones. Continuous training is imperative for ML models, and each iteration should utilize serving model data. Initially, production data is valuable. Utilizing serving model data for training ML models streamlines real-time deployment.
3. Fine-Tune the Serving ML Model
Serving models necessitate ongoing monitoring to detect errors in their early stages. This task requires human intervention as it involves identifying and allowing acceptable incidents. Regular monitoring must be scheduled during the serving phase of the ML model to ensure it behaves as expected. Integrating the user feedback loop into model maintenance is crucial for developing a robust incident response plan.
4. Monitor and Optimize Model Training Strategy
Achieving success in model production demands extensive training. Continuous training and integration guarantee that the ML model is effective in solving business problems. Initial training batches may exhibit accuracy fluctuations, but subsequent batches that leverage service model data will yield greater accuracy. For optimizing the training strategy, it's essential to ensure that all object instances are complete and consistent.
Code Best Practices
Developing MLOps entails an extensive amount of code writing across various programming languages. The written code must perform efficiently throughout different phases of the ML pipeline. Collaboration between data scientists and software engineers is vital for reading, writing, and executing ML model code. Unit tests for the codebase will validate individual features, and employing continuous integration will facilitate pipeline testing, ensuring that coding modifications do not disrupt the model.
Explore the following best practices for crafting machine learning code:
1. Adhere to Naming Conventions
Naming conventions are sometimes overlooked by developers eager to get their code up and running. Given that ML models require ongoing coding adjustments, altering anything in one place can have ripple effects. Employing consistent naming conventions aids the entire development team in comprehending and identifying multiple variables and their roles in model development.
2. Ensure High Code Quality
Code quality checks are imperative to ensure that the written code fulfills its intended purpose without introducing errors or bugs into the existing system. Well-written code should be easy to comprehend, maintain, and expand according to the ML model's requirements. A uniform coding style across the ML pipeline aids in detecting and eliminating bugs before the production phase. Standardized coding practices make it simple to identify dead code and duplicate code. Continuous experimentation with various code combinations is necessary for enhancing the ML model. It's important to maintain a robust code tracking system to correlate experiments and their outcomes.
3. Write Code Ready for Production
Although the ML model demands intricate coding, it's essential to craft production-ready code to enhance the model's competence. Reproducible code with version control simplifies deployment and testing. Adapting the pipeline framework is pivotal for creating modular code that supports continuous integration. The best ML model code adheres to a standardized structure and coding style convention. Every aspect of the code should be meticulously documented using appropriate documentation tools. A systematic coding approach should encompass the storage of training code, model parameters, datasets, hardware specifications, and environmental information to facilitate easy identification of code versions.
4. Container Deployment for Seamless Integration
A thorough comprehension of the functioning model is vital for the seamless integration of the ML model into company operations. Once the prototype is finished, deploying the model should be swift. An optimal approach involves leveraging containerization platforms to create distinct services within isolated containers. These container instances can be deployed on-demand and trained using real-time data. For simplified debugging, it's advisable to limit each container to one application. Containerization makes ML models reproducible and scalable across diverse environments, facilitating the streamlined initiation of model production and individualized training without disrupting existing operations.
5. Implement Automation Wherever Feasible
ML models necessitate ongoing testing and integration, especially when introducing new features or incorporating fresh data. Employing multiple unit tests with various test cases is crucial to ensuring the proper functionality of the machine learning application. Automated testing significantly reduces the manual effort required for coding. Automation of integration testing ensures that a single change is reflected consistently throughout the ML model code.
6. Explore Low-Code/No-Code Platforms
Low-code and no-code machine learning platforms streamline the coding process, allowing data scientists to introduce new features without affecting development engineers. While these platforms offer flexibility and rapid deployment, the degree of customization achieved is generally lower than what can be achieved through handwritten code. As ML models become more complex, development engineers typically play a more prominent role in crafting machine learning code.
In conclusion, adopting best practices in the diverse domains of Machine Learning is essential for success. These practices encompass setting clear objectives and metrics, establishing robust infrastructure, maintaining high-quality data, optimizing models, and adhering to sound coding principles. By following these guidelines, organizations can drive efficiency, reliability, and innovation in Machine Learning.