Mastering MLOps: A Guide for Startups to Improve ML Deployment 

The transition from ML pilots into full-fledged applications is a major challenge for startups. According to Google Cloud’s 2021 Practitioner’s Guide to MLOps, only half of the organizations that tried implementing ML have successfully moved beyond the pilot or proof of concept stage. Capgemini’s 2020 report states that 72% of companies that started AI pilots before 2019 haven’t deployed a single application into production.

Why do so many initiatives falter? AI and ML models don’t evolve into reliable versions because of a lack of quality training data. Managing the ML model lifecycle is often the barrier. And when the models do evolve, they can drift from the intended performance due to changes in the market or inability to scale.

Enter machine learning operations (MLOps) — a collaborative engineering methodology that unifies ML engineering with the continuous deployment and quality assurance practices of DevOps. MLOps aims to speed up the development and training of models, ensure the models are of sufficient quality and accuracy, and streamline deployment to production.

Alpacked has a wealth of expertise in DevOps, AI, and engineering. We know that proper ML data management, model training, and operational processes can propel your startup past its competitors. In this article, we’ll share tips to maximize the value you can gain from investing in MLOps.

Machine learning engineering extends beyond conventional software engineering into data science, business analytics, and extensive testing. This brings many additional complexities and challenges you need to address, including:

• Navigating through a maze of data. High-quality data is the lifeblood of any successful ML project. Startups must train their systems with enough relevant, accurate, and consistent data to produce a model without bias that makes accurate predictions and fair decisions.
• Data management bottlenecks. Lackluster data management systems can lead to inefficient data retrieval, processing, and storage. This results in tasks that waste time, hinder the ML training pipeline and, consequently, slow down deployments.
• Keeping up with ML model versions. Dynamic ML models constantly update based on new data, updated algorithms, and user interaction. Without proper versioning control, you can’t trace errors back to the source and compare the outcomes of updates. Algorithmia’s 2020 State of Enterprise Machine Learning survey found that versioning challenges introduce complexities that prevent teams from gaining value. 
• Scalability issues. Algorithmia’s survey mentions scaling as another critical challenge of ML adoption. As your startup grows, so do its datasets. A model that performs well on a small dataset may not scale properly when faced with multiple larger datasets.
• Ensuring reproducibility of results. Deploying machine learning models without ensuring reproducibility is like building a car from scratch without a blueprint. Reproducibility problems are often linked to explainability. Black-box models don’t allow you to see how they reach certain conclusions, which can result in unexpected biases. 
• Mastering team collaboration. ML management involves handoff difficulties between different teams. Startups often struggle to ensure seamless collaboration between data scientists (who work with the ML model), engineers (who understand the production environment), and operations teams (who manage the underlying infrastructure).

Navigating the complexities of ML deployment is a huge feat for startups. A proper MLOps infrastructure goes a long way toward conquering the challenges.

MLOps provides a set of standardized processes and technological capabilities for building, training, and organizing ML systems. It supports ML deployments similarly to how DevOps supports software engineering, but with an additional focus. 

When you deploy a traditional web application, you care mainly about performance, resilience, and load balancing. ML systems also require you to think carefully about the model, the training data, and its impact on performance.

MLOps practices can unlock many benefits for startups, like those described below.

Seamless model deployments

According to the Google Cloud report mentioned earlier, MLOps streamline operational and governance processes. It basically puts your ML deployments on a conveyor belt. It helps automate your project’s workflows, such as feature engineering, model training, evaluation, and deployment. The goal of standardizing and automating these workflows is to produce reliable and repeatable results. 

Faster time to market

Google Cloud highlights that efficient MLOps practices result in shorter development cycles, which means projects make it to market faster. Streamlining the ML lifecycle reduces the resources and time required to deploy and update models. This means that startups in competitive tech industries can upgrade their model’s capabilities much faster.

Improved accuracy and reliability

Collecting, or creating and maintaining high-quality data can help ensure that ML models are trained on unbiased data. High-quality data doesn’t over- or under-represent specific demographics and avoids stereotypes affecting models' quality. Additionally, MLOps solutions can detect and deal with data drift caused by underlying data or environment changes.

Easier onboarding

Standardized operations and clear governance can smooth the onboarding process for new members. This is particularly helpful for growing startups that need to scale ML deployments and extend their teams.

Greater transparency and auditability

MLOps platforms include features for logging model training and performance, which improves transparency within the ML lifecycle. This can be particularly important for startups in heavily regulated industries like healthcare and fintech.

Enhanced cross-team collaboration

Maintaining a common framework and ML pipeline across departments and teams helps bridge communication gaps and avoid misunderstandings during development. For example, your data scientist can package model versions in a way that engineers can easily understand, and quality assurance experts can troubleshoot them faster.

To summarize, a well-structured MLOps team allows startups to improve the performance, scalability, and reliability of ML systems. Meanwhile, automation frees up time for data scientists to focus on model refinement rather than dealing with operational issues. In turn, this dramatically increases the project’s ROI.

Let's get to the main point: how can startups build an efficient MLOps infrastructure to bolster their deployments?

The practices and tips outlined below can help startups build a system that can handle the complexities and demands of dynamic models.

Streamline data management

The data you feed into ML models impact the models’ accuracy and fairness. You should establish data collection, preprocessing, and storage standards to ensure that you use the highest quality data to train your ML models and include the following essential practices:

• Establish standardized data collection protocols to ensure consistency, fairness, and relevancy for accurate modeling.
• Implement data validation checks during data collection to detect errors early and reduce debugging time.
• Incorporate data preprocessing to make the data suitable for ML models. Normalizing data ensures that all your data use a common scale, while encoding converts it to numerical data understandable by ML models.
• Use a central unified repository for ML features and datasets to easily manage and retrieve data for specific tasks and applications. This makes it easier to log access attempts and data changes.
• Maintain scripts for creating and splitting datasets so you can easily recreate models in different environments for testing and development purposes.

By placing data management at the heart of your MLOps strategy, you can increase the likelihood of creating fair and accurate models that drive value for your business.

Enforce model governance

You should enforce rules for registering, validating, and approving models. Model governance can differ based on your industry, regulatory landscape, and ML use cases. However, some practices are uniform across most organizations:

• Define features to ensure employees in different departments consistently understand what each feature represents.
• Maintain metadata and annotation policies to help teams monitor data, code, and parameters so that teams working on the same tasks can collaborate.
• Apply quality assurance standards to ensure that ML models meet your standards (including sufficient accuracy, explainability, and security).
• Create checking, releasing, and reporting guidelines that control risk and support compliance with government regulations and data privacy standards.
• Implement change management procedures to ensure new data and algorithm updates don’t introduce risk or reduce the ML model’s performance.

As a cornerstone of an MLOps infrastructure, model governance promotes consistency, quality, and compliance throughout the model lifecycle.

Establish performance metrics

Incorporating MLOps metrics helps teams understand whether models operate as expected or drift from their optimal performance. Different models have different key performance indicators (KPI), but here are the most common ones:

• Throughput: Number of decisions (predictions) that a machine learning model can handle per unit of time

• Accuracy: Percentage of correct decisions made by the model (a widely used metric for binary classification problems)
• Latency: The length of time the model needs to respond to a request

• Resource utilization: How much CPU, GPU, and memory the system needs to complete tasks
• Error rates: How often a model fails to complete a task or returns an incorrect or invalid result (a metric typically used for regression problems)

Regularly reviewing these metrics helps you identify and resolve performance issues early on. They’re helpful not only for maintaining system performance but also for predicting cost. Additionally, using dashboard tools, like Grafana or Prometheus, helps various stakeholders visualize and understand MLOps performance.

It’s also a good idea to collect relevant business KPIs to measure the impact of the ML system on your business (for example, click-through rate and revenue uplift before and after deploying a model).

Implement version control

Robust version control for models, data, and configurations makes changes to model development traceable, accountable, and reproducible. It basically gives you a way to go back to any point in the development process and understand what happened.

Maintaining a traceable log of ML metadata (assets and artifacts produced during engineering) is also key to improving your past work. To achieve this, we recommend the following:

• Utilize version control tools like DVC (Data Version Control) to track datasets, revert changes, and reproduce workflows when training and deploying ML models.
• Use experiment tracking software such as MLflow or TensorBoard to compare metrics and hyperparameters of different model versions.

Automate CI/CD pipelines

Manual processes are harder to scale than automated ones. They’re also more prone to mistakes. In contrast, many MLOps platforms and tools, like Kubeflow and the MLflow mentioned above, let you define and automate repeatable steps and processes in your CI/CD pipeline to minimize the possibility of errors.

Here are some practices to adopt for a continuous ML pipeline:

• Integrate notebook environments with version control tools to allow data scientists and collaborators to write and automate modular, reusable, and testable source code.
• Implement automated checkups to reduce the time between model development, testing, and deployment into production.
• Set up automated alerts for model drift so you can respond quickly to degradations in accuracy and other performance metrics.

Think of automated CI/CD for MLOps this way: developers use automated CI/CD to merge code changes and automate delivery, whereas ML teams can use it to continuously integrate new data, retrain models, and deploy updates.

Develop a collaboration strategy

MLOps, like DevOps, relies on collaboration between teams with different areas of expertise — data scientists, engineers, and in the case of MLOps, the operations department. The goal is to foster a culture where these teams can effectively communicate and work together. Communication may take several different forms:

• Regular sync-ups between departments help align understanding of the objectives and current tasks.
• Comprehensive documentation empowers everyone to follow established processes and definitions.
• Collaboration tools like Trello or Jira make coordinating employees and outsourcing teams easier.

These practices contribute to a robust MLOps framework, improving your AI and ML capabilities so you can deploy models more cost-effectively.