This is a comprehensive list of practices to be followed in order to avoid common pitfalls when working with machine learning. The objective is to give you an understanding of best practices for each area within the landscape of machine learning.
While machine learning models help solve various business challenges, choosing the right one based on the use case of a specific business is not easy. More than 43% of business organizations have reported that ML models are hard to produce and integrate. Best Machine learning practices have to be followed right from the first step of the ML lifecycle to ensure that the model has the ability for better production.
With that said, I've decided to put together a post covering the best practices for: Objective & Metric, Infrastructure, Data, Model, and Code Best Practices in an effort to help organizations to take full advantage of machine learning.
These Machine Learning Best Practices are a collection of ideas, suggestions, tips and tricks shared by practitioners in the industry. They are not written as a single document but instead are described on a per objective/metric, infrastructure, data/model and code basis. And will be updated frequently.
This is the Ultimate Guide to Machine Learning Best Practices in 2022.
So if you want to learn:
Then you are in the right place.
Let’s get started.
Objective & Metric Best Practices
Defining the business objective before beginning the ML model design is the first obvious step. However, many times, ML models are started without clearly defined goals. Such models are set for failure because the ML models need clearly defined goals, parameters, and metrics. Organizations may not be aware of setting specific objective goals for ML models. They may want to find insights based on the available data, but a vague goal is insufficient to develop a successful ML model.
You have to be clear about your objective and the metric you'll use to measure success. Otherwise, you'll waste a lot of time on the wrong thing or chase an impossible goal.
Here are some objective best practices to keep in mind when designing the objectives of your machine learning solutions:
1. Ensure The ML Model Is Necessary
While many organizations want to follow the ML trend, the machine learning model may not be profitable. Before investing time and resources into developing an ML model, you need to identify the problem and evaluable whether machine learning and MLOps will be helpful in the specific use case. Small scale businesses must be even more careful because ML models cost resources that may not be available. Identifying areas of difficulty and having relevant data to implement machine learning solutions is the first step to developing a successful model. It is the only way to improve the profitability of the organization.
2. Collect Data For The Chosen Objective
Even though use cases are identified, data availability is the crucial driving factor to determine the successful implementation of the ML model. The first ML model for an organization should be simple but choose objectives supported by a large amount of data.
3. Develop Simple & Scalable Metrics
First, begin with constructing use cases for which the ML model must be created. Technical and business metrics have to be developed based on the use cases. The ML model can perform better when there is a clear objective and metrics to measure those objectives. The current process to meet the business goal must be reviewed thoroughly. Understanding where the current process faces challenges is the key to automation. Identifying deep learning techniques that can solve the current challenges is crucial.
Infrastructure Best Practices
Before investing time and effort in building an ML model, you must ensure that the infrastructure is in place to support the necessary model. Building, training, and producing a machine learning solution depend greatly on the infrastructure available. The best practice is to create an encapsulated ML model that is self-sufficient. The infrastructure should not be dependent on the ML model. This allows the building of multiple features later on. Testing and sanity checks on models are required before deployment.
Here are some infrastructure best practices to keep in mind when designing your machine learning solutions:
4. Right Infrastructure Components
The ML infrastructure includes various components, associated processes, and proposed solutions for the ML models. The incorporation of machine learning in business practices entails the growth of the infrastructure with AI technology. Businesses should not spend on building the complete infrastructure before ML model development. Multiple aspects such as containers, orchestration tools, hybrid environments, multi-cloud environments, and agile architecture must be implemented stepwise, allowing maximum scalability.
5. Cloud-based vs. On-premise Infrastructure
When enterprises start with machine learning architecture, it is best to exploit cloud infrastructure initially. Cloud-based infrastructure is cost-effective, low-maintenance, and easily scalable. Some industry giants provide excellent support for cloud-based infrastructure. The cloud-based ML platforms with comprehensive features are already available for customization. Giants such as GCP, AWS, Microsoft Azure, etc., have ML-specific infrastructure elements ready to use. Cloud-based infrastructure has lower setup costs with better support from ML-specific providers. It also allows scalability with various-sized computing clusters.
On-premise infrastructure can incorporate readily available learning servers like Lambda Labs, Nvidia Workstations, etc. Deep learning workstations can be built from scratch. The in-house infrastructure model requires a large initial investment. However, on-premise systems offer more security advantages when multiple ML models are implemented for enterprise-level automation. Ideally, ML models must use a combination of cloud-based infrastructure and in-house infrastructure at varying levels.
6. Make The Infrastructure Scalable
The proper infrastructure for the ML model depends on business practices and future goals. Infrastructure should support separate training models and serving models. This enables you to continue testing your model with advanced features without affecting the deployed serving model. Microservices architecture is instrumental in achieving encapsulated models.
For developing successful ML models, exhaustive data processing is critical. The data determines the system's goal and plays a major role in training ML algorithms. The performance of the model and evaluation of the model can't be completed without appropriate data.
Here are some general guidelines for you to keep in mind when preparing your data:
7. Understand Data Quantity Significance
Building ML models is possible when there is a massive volume of data. Raw data is crude, but before proceeding with ML model building, you have to extract usable information from the data. Data gathering should begin with the existing system in the organization. This will give you the data metrics needed to build the ML model. When the data availability is minimal, you can use transfer learning to gather as much data as possible. Once raw data is available, you must deploy feature engineering to pre-process the data. Collected data must undergo necessary transformations to be valuable as training data. Raw inputs converted into features will be helpful in the design phase of the ML data modeling.
8. Data Processing Is Crucial
The first step in data processing is data collection and preparation. Feature engineering should be applied during data pre-processing to correlate essential features with available data. Data wrangling metrics must be used during the interactive data analysis phase. Exploratory data analysis exploits data visualization to understand data, perform sanity checks, and validate the data. When the data process matures, data engineers incorporate continuous data ingestions and appropriate data transformations to multiple data analytics entities. Data validation is required at every iteration of the ML pipeline or data pipeline for model training. When data drift is identified, the ML model requires retraining. If data anomalies are detected, the pipeline execution must be stopped until the anomalies are addressed.
9. Prepare Data For Use Throughout ML Lifecycle
Understanding and implementing data science best practices play a significant role in preparing the data for use in machine learning solutions. The datasets must be categorized based on features, and they must be documented for use throughout the ML lifecycle.
When data and infrastructure is ready, it is time to choose the perfect ML model. Multiple teams work with multiple technologies, which may or may not overlap. You need to select an ML model that can support existing technologies. Data science experts don't have programming expertise, and they may be using outdated technology stacks. On the other hand, software engineers may be using the latest and experimental technologies to achieve the best results. The ML model must support old models while making room for newer technologies. The selected technology stacks must be cloud-ready even though in-house servers are used currently.
The following are the most important model best practices:
10. Develop a Robust Model
In the ML model pipeline, validation, testing, and monitoring of ML models are crucial. Model validation should ideally be completed before the model goes through production. The robustness metric should become an important benchmark for model validation. Model selection should be made based on the robustness metrics. If the robustness of the chosen model can't be improved to meet benchmark standards, the model has to be dropped, and a different ML model must be picked. Defining and creating usable test cases is crucial for continuous ML model training.
11. Develop & Document Model Training Metrics
Building incremental models with checkpoints will make your machine learning framework resilient. Data science involves numerous metrics, which can be confusing. Performance metrics should always take precedence over fancy metrics. ML model requires continuous training, and with each iteration, serving model data should be used. Production data is helpful in the beginning stage. Using serving model data for training ML models will make the model easier to deploy in real-time.
12. Fine Tune The Serving ML Model
Serving models require continuous monitoring to catch errors in the early phase. This requires a human in the loop because acceptable incidents must be identified and allowed. Periodic monitoring must be scheduled in the serving phase of the ML model to ensure that the model behaves exactly in the way it is expected to behave. The user feedback loop must be integrated into the model maintenance to develop a strong incident response plan.
13. Monitor and Optimize Model Training Strategy
In order to achieve success with model production, extensive training is required. Continuous training and integration will ensure that the ML model is profitable to solve business problems. The model accuracy may fluctuate with the initial training batch, but subsequent batches that use service model data will provide greater accuracy. All the object instances must be complete and consistent for optimizing the training strategy.
Developing MLOps involves a massive amount of writing codes in multiple languages. The written code must execute effectively in different stages of the ML pipeline. Data scientists and software engineers must work together to read, write, and execute ML model codes. The codebase unit tests will test the individual features. Continuous integration will enable pipeline testing, which guarantees that changes in coding will not break the model.
Check out some of the best practices to follow when writing machine learning code.
14. Follow Naming Conventions
Naming conventions are often ignored by development engineers keen on making their code run. As ML models require continuous modifications in coding, changing anything anywhere results in changing everything everywhere. The naming conventions will help the entire development engineering team to understand and identify multiple variables and their roles in model development.
15. Ensure Optimal Code Quality
Code quality checks are mandatory to ensure that the written code does what it is supposed to do. The code shouldn't introduce errors or bugs in the existing system. The written code should be easy to read, maintain and extend depending on the ML model requirement. Throughout the ML pipeline, a Uniform coding style will help catch and eliminate bugs before the production stage. Dead code and duplicate code are easily identifiable when the engineers follow a standard coding style. Constant experimentation with different code combinations is unavoidable to improve the ML model. A proper code tracking system should be in place to correlate experiments and their results.
16. Write Production Ready Code
The ML model requires complex coding, but you should write production-ready code to make the model competent. Reproducible code with version control is easier to deploy and test. Pipeline framework adaptation is crucial to creating modular code that allows continuous integration. The best ML model code uses a standard structure and coding style convention. Every aspect of coding must be documented using appropriate documentation tools. The systematic coding approach should store training code, model parameters, data sets, hardware, and environment to identify code versions easily.
17. Deploy Models in Containers for Easier Integration
A clear understanding of the actual working model is crucial to integrating the ML model into company operations. Once the prototype is complete, there should be no delay in deploying the model. The best practice is to use containerization platforms to create multiple services in isolated containers. The instances of containers are deployed on-demand and trained using real-time data. Limit one application per container for easier debugging. Containerized approach makes the ML models reproducible and scalable across various environments. Engineering teams can easily start the production of models if the features are encapsulated. It also allows for individualized training without affecting the existing production.
18. Incorporate Automation Wherever Possible
The ML models require consistent testing and integration when new features are included, or new data becomes available. Multiple unit tests with varying test cases are essential to ensure that the machine learning application works as intended. Automated testing dramatically helps in reducing the manual labor required to complete the coding. Integration testing automation helps in ensuring that a single change is reflected all through the ML model code.
19. Low Code/ No-Code Platform
The low code and no code machine learning platforms reduce the amount of coding involved, enabling data scientists to introduce new features without affecting development engineers. While these platforms provide flexibility and quick deployment, the level of customization achieved is still low compared to handwritten code. As the complexity of ML models increases, development engineers become more involved in writing machine learning code.
We hope that this blog provides some good insights into machine learning best practices.
By following the best practices, you can create a scalable, customizable, and resilient ML model that requires minimal modification. Ideal ML models integrate with existing systems seamlessly. The ML model should always make room for improvement as the business requirements and data change continuously.
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