MLOps & ML Experiment Tracking: Essential for Building Robust ML Solutions

What is MLOps, Key Components of MLOps

Machine Learning Operations (MLOps) include all the processes, practices, and techniques used to ensure the smooth development, deployment, and governance of ML models. In other words, MLOps manages the entire ML development and maintenance lifecycle to ensure the developed solution meets the desired goals.

Typically, ML development requires expert contributions of ML engineers, data scientists & engineers, software engineers, design architects, and sometimes other professionals like Cloud engineers, Security engineers, etc., if the solution is too complex. Inspired from DevOps, MLOps facilitates seamless coordination and functioning between the ML solution’s development and operations teams and addresses unique ML-related challenges.

So, MLOps bridges the gap between varied teams handling ML, Data, Software, Design, and Operations to facilitate effective collaboration, ensure seamless ML workflows, and enhance ML model performance. The purpose of MLOps is to ensure reliable and consistent development, testing, deployment, maintenance, and monitoring of ML models.

With the involvement of multiple teams or many professionals handling different tasks, things can get easily out of control if rigorous procedures for correct hands-off practices across team members and accuracy of operations are not implemented. So, another purpose of MLOps is to continuously improve the performance of the ML lifecycle by experimenting and coordinating.

Key Components of MLOps

Depending on the focus and expanse of your ML project, MLOps implementation can differ from merely ensuring model performance to handling the entire ML lifecycle. Here are the elements or features that MLOps implementation in a complex project would include:

Exploratory Data Analysis (EDA): This is an iterative process that starts with exploring the data and visualizing it to identify trends, outliers, and patterns.

Data Preparation and Feature Engineering: Data Preparation includes data ingestion, cleaning, processing, and storing in required formats to make it available for training ML models. Feature engineering involves transforming data into relevant features that are useful for model training. This stage ensures high-quality data availability to help the ML model deliver desired outcomes.

Model Development, Training, and Tuning: Develop your own ML model and train fully, or choose a pre-trained model and fine-tune it for your intended use cases. This stage includes using the right ML algorithms, training the model, adjusting hyperparameters for tuning, and evaluating its performance on the test dataset.

Model Review and Governance: Before deployment, the model undergoes many iterations, creating versions. Track model lineage, versions, transitions, and artifacts like weights, architecture, configuration files, etc. Also, validate model performance and quality standards while ensuring its fairness, interpretability, and security.

Model Deployment: This includes deploying your trained ML model into production. You must set up the required infrastructure (on-premise, Cloud, or hybrid), include dependencies, implement configurations and integrations, and use CI/CD pipelines to automate deployment, versioning, and rolling out of updates.

Model Inference and Serving: Model inference is using this deployed model to make predictions on new, unseen data. Model Serving means using an API to allow external apps or systems to send data and get predictions from the ML model. Before serving the model, it must be prepared to handle requests, manage load, and provide responses with minimal latency.

Model Monitoring: It includes monitoring the model’s performance by tracking metrics like accuracy, precision, recall, etc., detecting drift i.e. degradation in performance over time, and identifying bias and other issues like overfitting and underfitting.

Automated Model Retraining: ML models must be automatically retrained when their performance degrades or new data is available for further training, so triggers must be incorporated for such events. You must also evaluate the model’s performance post-retraining to ensure desired outcomes.

MLOps is an integral and critical part of ML development that cannot be ignored if you want to ensure the success of your ML solution. Using popular open-source tools like MLflow, Kubeflow, Metaflow, etc., can enhance the scalability and improve coordination in your MLOps process.

ML Experiment Tracking: What is it and Why it Matters

Every ML development project involves several experiments (iterations) because there’s no one-size-fits-all solution to ensure your ML model’s accuracy and performance. ML Experiment Tracking is a critical task in MLOps that includes collecting, storing, and analyzing all the important information relating to each of these experiments (called metadata). This information includes:

As many experiments are conducted applying different combinations of parameters, datasets, environments, etc., ML Experiment Tracking (also called Experiment Logging) provides the following benefits in the MLOps process:

Reproducibility and Comparability: Suppose you’re a part of the ML team and after completing five experiments you realize that the second one was giving more accurate results. How will you recreate it if you have not collected and stored its details? Also, without having the data to make comparisons, how will you even identify which experiment (model version) worked better?

Collaboration and Efficiency: With multiple team members conducting experiments, logging helps understand what has been already tried and if it worked or not. So, the team can avoid running similar experiments that will almost always provide the same results.

Debugging and Optimization: Understanding why an experiment failed is critical and provides opportunities for debugging. Also, experiments working well on specific metrics can be optimized to improve performance on other parameters.

Monitoring and Fine-tuning: By logging the model artifacts after every iteration, ML Experiment tracking provides valuable data for model monitoring and helps with model retraining efforts for fine-tuning.

So, ML Experiment Tracking brings a systematic, collaborative, and efficient approach to the process aimed at improving the ML model’s performance. Tools like Weights & Biases, Neptune.ai, etc., help with accurate logging and visualization of experiments, metrics, and model performance.

MLOps and ML Experiment Tracking: How They Work Together

Complimenting each other, MLOps and ML Experiment Tracking ensure you get a scalable and robust ML workflow. While ML Experiment Tracking documents the iterative process of experimentations and facilitates data-backed and timely decisions within the team, MLOps helps operationalize and maintain those decisions in the production environment. Together they provide the following business benefits:

Faster Time-to-market: Automation via MLOps helps teams to move quickly from development to production with rapid deployment and integration. ML Experiment Tracking speeds up the interactive cycle of experimentation, monitoring, and fine-tuning to ensure quicker delivery of optimized ML solutions.

Improved Model Performance and Overall Solution Quality: With robust MLOps pipelines, performance issues are identified and addressed early. ML Experiment Tracking helps the team to quickly adopt the best-performing configurations after learning about past interactions. This results in more optimization and greater alignment of the ML model with the business needs and goals.

Flexibility and Scalability: As your business grows, your ML-based system needs to scale seamlessly across environments, be it on-premise, Cloud, or hybrid infrastructure. You also need to adapt the solution to your new business data and evolving business needs. While MLOps automates deployment and management, ML Experiment Tracking enables fast experimentation and helps achieve better results with minimal updates.

Compliance and Governance: Complying with regulations is more critical for ML solutions built specifically for healthcare, finance, and legal industries. So, transparency, auditability, and traceability of ML models must be ensured, which can be achieved through MLOps. ML Experiment Tracking can help prove that your data complies with regulations by keeping accurate records of model artifacts, metrics, and performance.

Build Robust ML Solutions Using MLOps and ML Experiment Tracking

We cannot emphasize more on the importance of MLOps and ML Experiment Tracking in ensuring that your custom-built ML solution aligns accurately with your business needs and delivers precise outcomes while staying resilient, scalable, and compliant.

Our experience building many ML solutions across industries has taught us how to iteratively leverage the benefits of MLOps and ML Experiment Tracking in our ML projects. We ensure our clients not just get an ML-based solution customized for their niche tasks but a future-ready solution that provides real value and tangible results. You can also explore our comprehensive AI services and solutions , with capabilities in Machine Learning, Natural Language Processing, Vision AI, and Deep Learning. Please also go through our case studies showcasing our AI development proficiency.