That Secret Sauce To Great ML Projects: Best Practices

The development of machine learning models has graduated from being a buzzword to a necessity. Businesses have found ways to use the vast historical data to devise methods that direct resources toward payoffs. The payoffs that include precise predictions and a more targeted audience are better achieved when backed by fine-tuned models trained on relevant trends.

Surely, ML may not always be the key to the sweet success that your business deserves, but it does pave the way to more thoughtful decisions. Take the case of how it has revolutionized the world of online dating. What started as a simple matching service has evolved into intelligent recommenders that take less time to bring the best matches. Or take the example of how all of the data collected by your fitness bands are getting used to build more intelligent monitoring systems. The possibilities are limitless, and organizations possessing diverse capabilities have dipped their feet into the choppy waters of ML development.

The said organizations could be established multinationals with rookie ML development teams or startups primarily focused on delivering ML services. Nonetheless, any firm that has established an ML development ecosystem will have to deal with the baggage that comes along: the data and its governance, the technology choices, implementation and integration standards, and the deployment challenges. 

The business metrics also need continuous alignment with the performance of the deployed model. These considerations require measures that minimize the risks early on since an issue may avalanche into more significant problems down the lifecycle.

We will now talk about some practices that can help contain risks associated with each of the five stages of ML development.

‍

‍

Laying the Groundwork

At this point, your ML project is just an idea. This is the stage where business objectives and subsequent requirements for the product are decided. So what practices can ensure a great basis for your upcoming ML development?

Listen to the pulse of current systems

It is an exciting stage to lay down the expectations for your ML-powered product, more so, how it will be the most cutting-edge solution to real-world problems. It is advisable to collect and analyze historical data to understand where the solutions are lacking and how ML is required to circumvent them. In addition, the analysis of current solutions may reveal further optimization prospects.

Check for data availability

Data is the fuel of ML modeling. Whether you are developing a new system altogether or introducing ML to a legacy setup, it is essential to formalize the sources and policies for data acquisition. This practice is also helpful if you are borrowing ML artifacts as well.

Specify the metrics

A process that specifies expected milestones and metrics for each stage will communicate clear instructions to the team members. Also, metric instrumentation strategies bring uniformity in understanding goals and allow for a mature evaluation of intermediate outputs.

The metrics also guide optimizations since the teams can measure the improvements caused by alterations, especially those aimed at developing a scalable solution.

‍

Data Preparation: It is All About the Data

The costs involved in building a housing project depend on the quality of raw materials and their availability. The logistics have to consider whether it's worth transporting heavy slabs of fancy stone compared to readily available ones. How much delay and transportation costs can the project afford, and if the planners foresee any possible shortage. The considerations for your ML project datasets should be in a similar vein.

Set data quantity considerations

The training and testing of your model will need decent-sized datasets where the choice of ML algorithms also drives the quantity considerations. If you plan on using deep learning in the future, then ensure the availability of larger datasets.

If you are using data from multiple sources, it is a good practice to employ data pipelines that speed up consumption in dynamic environments and raise alerts for contingencies. MLOps tools that allow for automated data pipelines can ensure that the model will always be fed relevant datasets, including those collected after the deployment.

Set data quality considerations

The acquired datasets will rarely be suitable for immediate use by your model. The two-fold strategy to explore the data and pre-process it will not only provide usable training data but also refine the data acquired from the users of your solution.

The exploration and presentation of data attributes can better justify the transformations it would need, apart from fostering a better understanding. Since the dimensionality of real-world datasets is vast, the documentation of feature engineering methods and outputs can be a great practice. The documentation will assist the team members in appreciating the significance of the features and encourage the re-use of existing knowledge.

Invest in data-specific infrastructure

After it has been cleaned and structured by the team, the versioning of data can be a great advantage for future use. While many versioning systems exist for software development, MLOps has introduced tools that allow storing data in central repositories and collaboration among teams. Such practices also provide access control and well-laid governance strategies for low-risk shared use.

Organizations have used data warehouses for a long time to store structured data. While the data is in a consumable form in the warehouses, investing in data lakes can benefit data scientists. The raw data stored in a lake environment allows quicker access and offers more malleability for ML applications.

‍

Model Development: A Model is Born and Reborn

Evaluate your technical stack options

Provide adequate resources to your data science team to evaluate several candidates before deciding on the best fit for your product. The teams should also consider the metrics like applicability to the business problem, long-term stability, future readiness, and support for different hosting platforms to back the decisions.

The teams should also be provided with the infrastructure to test the various stack options.

Consistency is the key

The ML algorithm that will drive your product might need enhancements during its lifecycle. Continuous Integration and Continuous Deployment (CI/CD) will ensure that the teams can focus on delivering more efficient outputs rather than putting efforts into error-free integration.

While powerful repository management systems exist to allow collaboration between different teams, add-ons for CI/CD pipelining can add that extra zing to the workflows. The automated documentation of repository activities that include model training, evaluation, monitoring, and evaluation reports at every pull request will ensure monitoring and quick recovery in case of any errors.

CI/CD provisions also allow the data science and engineering teams to be aware of environment-specific details of the project.

Re-visit as much as possible

It is advisable to start with a simple solution and improve it. The relevance of data attributes can change due to external events, and the user interface may require upheavals. If the complexity of the solution is contained initially, it would be easier to incorporate modifications.

One can never have enough tests

It is good to test the model before it is ready to face the world. The tests should cover all aspects of its functioning and the process. While integration tests are the best way to ensure that contributions from different components work together, system tests focus on the solution as a whole. The model itself should be tested for different environments, and its performance should be gauged through predefined metrics.

The number of tests involved in the workflow means that automation can again prove to be a savior. The changes to code or the end of a process in the workflow can trigger a test suite that ensures the sanity of the output. The team can then check the alerts raised by such automated tests.

‍

Model Deployment: It’s Alive!

The model deployment standardized by MLOps processes is smoother and has almost zero chances of upgrade downtime. So how can you harness the benefits of MLOps during the deployment stage?

Automation of integration and serving

After being tested for the production environment, a trained and tested model needs to be deployed as an API endpoint or a bulk process. The creation of automated workflows will help the teams to integrate quickly. Additionally, the triggers caused by individual actions will initiate further processes to prepare for deployment with minimal human error.

Use of containers and orchestration tools

A deployed model can be best served in a microservices architecture. Containerization can ensure smooth functioning to further support compatibility between different versions of the model and resource requirements. Different environments can be catered to by encapsulating ML artifacts and specific configurations.

The orchestration of the containers will again reduce the human efforts required to provide suitable infrastructure. There are widely accepted MLOps platforms that provide containerization and their orchestration functions.

Ensure mature rollouts

Several MLOps strategies ensure minimal downtime when the live model is upgraded. These include canary and blue-green deployments. The tools to automate the deployments can again reduce the risks associated with this stage.

The teams should also be provided with resources to further enhance the model performance through explainability reports.

‍

Model Monitoring: Where do You See Your Model in the Next Five Months (or Weeks or Days)?

The performance metrics should also be continually evaluated to check if they correspond with business objectives. Since a live model will work in a dynamic environment, it will receive datasets that may differ from the data it had been trained for. Such unforeseen disparities that result in drifts can degrade the model performance silently.

Provide infrastructure for monitoring

A model that is monitored regularly can be guarded against drifts. MLOps-based platforms that automate monitoring can also make it easier to handle logs and reports.

A comprehensive solution like the Censius AI Observability platform would not only alert your team against any potential performance issues but also document and plot the findings in a discernible format.

Encourage teams to consume feedback

The results of tests done in real-time and explainability reports should be used by the team as feedback to determine potential optimizations. Additionally, such documentation would also help the data engineers to evaluate the different variants and their impact on model performance.

‍

Wrapping Up

The standards for the development of ML solutions have yet to become the word in stone, but some practices have been identified to nudge your ML development towards more maturity. These practices are some achievable measures to prevent your team from getting caught off-guard and focus their energy on building more robust ML models.

Thank you for reading