Machine Learning Operations (MLOps) is a set of practices and principles that aim to unify the processes of developing, deploying, and maintaining machine learning models in production environments. It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets.
As the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased. These tools help streamline the entire lifecycle of machine learning projects, from data preparation and model training to deployment and monitoring. In this comprehensive guide, we will explore some of the top MLOps tools available, including Weights & Biases, Comet, and others, along with their features, use cases, and code examples.
What is MLOps?
MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. By establishing standardized workflows, automating repetitive tasks, and implementing robust monitoring and governance mechanisms, MLOps enables organizations to accelerate model development, improve deployment reliability, and maximize the value derived from ML initiatives.
Building and Maintaining ML Pipelines
While building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.
A machine learning engineering team is responsible for working on the first four stages of the ML pipeline, while the last two stages fall under the responsibilities of the operations team. Since there is a clear delineation between the machine learning and operations teams for most organizations, effective collaboration and communication between the two teams are essential for the successful development, deployment, and maintenance of ML systems. This collaboration of ML and operations teams is what you call MLOps and focuses on streamlining the process of deploying the ML models to production, along with maintaining and monitoring them. Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams.
The core responsibility of MLOps is to facilitate effective collaboration among ML and operation teams to enhance the pace of model development and deployment with the help of continuous integration and development (CI/CD) practices complemented by monitoring, validation, and governance of ML models. Tools and software that facilitate automated CI/CD, easy development, deployment at scale, streamlining workflows, and enhancing collaboration are often referred to as MLOps tools. After a lot of research, I have curated a list of various MLOps tools that are used across some big tech giants like Netflix, Uber, DoorDash, LUSH, etc. We are going to discuss all of them later in this article.
Types of MLOps Tools
MLOps tools play a pivotal role in every stage of the machine learning lifecycle. In this section, you will see a clear breakdown of the roles of a list of MLOps tools in each stage of the ML lifecycle.
Pipeline Orchestration Tools
Pipeline orchestration in terms of machine learning refers to the process of managing and coordinating various tasks and components involved in the end-to-end ML workflow, from data preprocessing and model training to model deployment and monitoring.
MLOps software is really popular in this space as it provides features like workflow management, dependency management, parallelization, version control, and deployment automation, enabling organizations to streamline their ML workflows, improve collaboration among data scientists and engineers, and accelerate the delivery of ML solutions.
Model Training Frameworks
This stage involves the process of creating and optimizing predictive models with labeled and unlabeled data. During training, the models learn the underlying patterns and relationships in the data, adjusting its parameters to minimize the difference between predicted and actual outcomes. You can consider this stage as the most code-intensive stage of the entire ML pipeline. This is the reason why data scientists need to be actively involved in this stage as they need to try out different algorithms and parameter combinations.
Machine learning frameworks like scikit-learn are quite popular for training machine learning models while TensorFlow and PyTorch are popular for training deep learning models that comprise different neural networks.
Model Deployment and Serving Platforms
Once the development team is done training the model, they need to make this model available for inference in the production environment where these models can generate predictions. This typically involves deploying the model to a serving infrastructure, setting up APIs for communication, model versioning and management, automated scaling and load balancing, and ensuring scalability, reliability, and performance.
MLOps tools offer features such as containerization, orchestration, model versioning, A/B testing, and logging, enabling organizations to deploy and serve ML models efficiently and effectively.
Monitoring and Observability Tools
Developing and deploying the models is not a one-time process. When you develop a model on a certain data distribution, you expect the model to make predictions for the same data distribution in production as well. This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call data drift. There is only one way to identify the data drift, by continuously monitoring your models in production.
Model monitoring and observability in machine learning include monitoring key metrics such as prediction accuracy, latency, throughput, and resource utilization, as well as detecting anomalies, drift, and concept shifts in the data distribution. MLOps monitoring tools can automate the collection of telemetry data, enable real-time analysis and visualization of metrics, and trigger alerts and actions based on predefined thresholds or conditions.
Collaboration and Experiment Tracking Platforms
Suppose you are working on developing an ML system along with a team of fellow data scientists. If you are not using a mechanism that tracks what all models have been tried, who is working on what part of the pipeline, etc., it will be hard for you to determine what all models have already been tried by you or others. There could also be the case that two developers are working on developing the same features which is really a waste of time and resources. And since you are not tracking anything related to your project, you can most certainly not use this knowledge for other projects thereby limiting reproducibility.
Collaboration and experiment-tracking MLOps tools allow data scientists and engineers to collaborate effectively, share knowledge, and reproduce experiments for model development and optimization. These tools offer features such as experiment tracking, versioning, lineage tracking, and model registry, enabling teams to log experiments, track changes, and compare results across different iterations of ML models.
Data Storage and Versioning
While working on the ML pipelines, you make significant changes to the raw data in the preprocessing phase. For some reason, if you are not able to train your model right away, you want to store this preprocessed data to avoid repeated work. The same goes for the code, you will always want to continue working on the code that you have left in your previous session.
MLOps data storage and versioning tools offer features such as data versioning, artifact management, metadata tracking, and data lineage, allowing teams to track changes, reproduce experiments, and ensure consistency and reproducibility across different iterations of ML models.
Compute and Infrastructure
When you talk about training, deploying, and scaling the models, everything comes down to computing and infrastructure. Especially in the current time when large language models (LLMs) are making their way for several industry-based generative AI projects. You can surely train a simple classifier on a system with 8 GB RAM and no GPU device, but it would not be prudent to train an LLM model on the same infrastructure.
Compute and infrastructure tools offer features such as containerization, orchestration, auto-scaling, and resource management, enabling organizations to efficiently utilize cloud resources, on-premises infrastructure, or hybrid environments for ML workloads.
Best MLOps Tools & Platforms for 2024
While Weights & Biases and Comet are prominent MLOps startups, several other tools are available to support various aspects of the machine learning lifecycle. Here are a few notable examples:
- MLflow: MLflow is an open-source platform that helps manage the entire machine learning lifecycle, including experiment tracking, reproducibility, deployment, and a central model registry.
- Kubeflow: Kubeflow is an open-source platform designed to simplify the deployment of machine learning models on Kubernetes. It provides a comprehensive set of tools for data preparation, model training, model optimization, prediction serving, and model monitoring in production environments.
- BentoML: BentoML is a Python-first tool for deploying and maintaining machine learning models in production. It supports parallel inference, adaptive batching, and hardware acceleration, enabling efficient and scalable model serving.
- TensorBoard: Developed by the TensorFlow team, TensorBoard is an open-source visualization tool for machine learning experiments. It allows users to track metrics, visualize model graphs, project embeddings, and share experiment results.
- Evidently: Evidently AI is an open-source Python library for monitoring machine learning models during development, validation, and in production. It checks data and model quality, data drift, target drift, and regression and classification performance.
- Amazon SageMaker: Amazon Web Services SageMaker is a comprehensive MLOps solution that covers model training, experiment tracking, model deployment, monitoring, and more. It provides a collaborative environment for data science teams, enabling automation of ML workflows and continuous monitoring of models in production.
What is Weights & Biases?
Weights & Biases (W&B) is a popular machine learning experiment tracking and visualization platform that assists data scientists and ML practitioners in managing and analyzing their models with ease. It offers a suite of tools that support every step of the ML workflow, from project setup to model deployment.
Key Features of Weights & Biases
- Experiment Tracking and Logging: W&B allows users to log and track experiments, capturing essential information such as hyperparameters, model architecture, and dataset details. By logging these parameters, users can easily reproduce experiments and compare results, facilitating collaboration among team members.
import wandb
# Initialize W&B
wandb.init(project="my-project", entity="my-team")
# Log hyperparameters
config = wandb.config
config.learning_rate = 0.001
config.batch_size = 32
# Log metrics during training
wandb.log({"loss": 0.5, "accuracy": 0.92})
- Visualizations and Dashboards: W&B provides an interactive dashboard to visualize experiment results, making it easy to analyze trends, compare models, and identify areas for improvement. These visualizations include customizable charts, confusion matrices, and histograms. The dashboard can be shared with collaborators, enabling effective communication and knowledge sharing.
# Log confusion matrix
wandb.log({"confusion_matrix": wandb.plot.confusion_matrix(predictions, labels)})
# Log a custom chart
wandb.log({"chart": wandb.plot.line_series(x=[1, 2, 3], y=[[1, 2, 3], [4, 5, 6]])})
- Model Versioning and Comparison: With W&B, users can easily track and compare different versions of their models. This feature is particularly valuable when experimenting with different architectures, hyperparameters, or preprocessing techniques. By maintaining a history of models, users can identify the best-performing configurations and make data-driven decisions.
# Save model artifact
wandb.save("model.h5")
# Log multiple versions of a model
with wandb.init(project="my-project", entity="my-team"):
# Train and log model version 1
wandb.log({"accuracy": 0.85})
with wandb.init(project="my-project", entity="my-team"):
# Train and log model version 2
wandb.log({"accuracy": 0.92})
- Integration with Popular ML Frameworks: W&B seamlessly integrates with popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn. It provides lightweight integrations that require minimal code modifications, allowing users to leverage W&B’s features without disrupting their existing workflows.
import wandb
import tensorflow as tf
# Initialize W&B and log metrics during training
wandb.init(project="my-project", entity="my-team")
wandb.tensorflow.log(tf.summary.scalar('loss', loss))
What is Comet?
Comet is a cloud-based machine learning platform where developers can track, compare, analyze, and optimize experiments. It is designed to be quick to install and easy to use, allowing users to start tracking their ML experiments with just a few lines of code, without relying on any specific library.
Key Features of Comet
- Custom Visualizations: Comet allows users to create custom visualizations for their experiments and data. Additionally, users can leverage community-provided visualizations on panels, enhancing their ability to analyze and interpret results.
- Real-time Monitoring: Comet provides real-time statistics and graphs about ongoing experiments, enabling users to monitor the progress and performance of their models as they train.
- Experiment Comparison: With Comet, users can easily compare their experiments, including code, metrics, predictions, insights, and more. This feature facilitates the identification of the best-performing models and configurations.
- Debugging and Error Tracking: Comet allows users to debug model errors, environment-specific errors, and other issues that may arise during the training and evaluation process.
- Model Monitoring: Comet enables users to monitor their models and receive notifications when issues or bugs occur, ensuring timely intervention and mitigation.
- Collaboration: Comet supports collaboration within teams and with business stakeholders, enabling seamless knowledge sharing and effective communication.
- Framework Integration: Comet can easily integrate with popular ML frameworks such as TensorFlow, PyTorch, and others, making it a versatile tool for different projects and use cases.
Choosing the Right MLOps Tool
When selecting an MLOps tool for your project, it’s essential to consider factors such as your team’s familiarity with specific frameworks, the project’s requirements, the complexity of the model(s), and the deployment environment. Some tools may be better suited for specific use cases or integrate more seamlessly with your existing infrastructure.
Additionally, it’s important to evaluate the tool’s documentation, community support, and the ease of setup and integration. A well-documented tool with an active community can significantly accelerate the learning curve and facilitate troubleshooting.
Best Practices for Effective MLOps
To maximize the benefits of MLOps tools and ensure successful model deployment and maintenance, it’s crucial to follow best practices. Here are some key considerations:
- Consistent Logging: Ensure that all relevant hyperparameters, metrics, and artifacts are consistently logged across experiments. This promotes reproducibility and facilitates effective comparison between different runs.
- Collaboration and Sharing: Leverage the collaboration features of MLOps tools to share experiments, visualizations, and insights with team members. This fosters knowledge exchange and improves overall project outcomes.
- Documentation and Notes: Maintain comprehensive documentation and notes within the MLOps tool to capture experiment details, observations, and insights. This helps in understanding past experiments and facilitates future iterations.
- Continuous Integration and Deployment (CI/CD): Implement CI/CD pipelines for your machine learning models to ensure automated testing, deployment, and monitoring. This streamlines the deployment process and reduces the risk of errors.
Code Examples and Use Cases
To better understand the practical usage of MLOps tools, let’s explore some code examples and use cases.
Experiment Tracking with Weights & Biases
Weights & Biases provides seamless integration with popular machine learning frameworks like PyTorch and TensorFlow. Here’s an example of how you can log metrics and visualize them during model training with PyTorch:
import wandb
import torch
import torchvision
# Initialize W&B
wandb.init(project="image-classification", entity="my-team")
# Load data and model
train_loader = torch.utils.data.DataLoader(...)
model = torchvision.models.resnet18(pretrained=True)
# Set up training loop
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()
for epoch in range(10):
for inputs, labels in train_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Log metrics
wandb.log({"loss": loss.item()})
# Save model
torch.save(model.state_dict(), "model.pth")
wandb.save("model.pth")
In this example, we initialize a W&B run, train a ResNet-18 model on an image classification task, and log the training loss at each step. We also save the trained model as an artifact using wandb.save(). W&B automatically tracks system metrics like GPU usage, and we can visualize the training progress, loss curves, and system metrics in the W&B dashboard.
Model Monitoring with Evidently
Evidently is a powerful tool for monitoring machine learning models in production. Here’s an example of how you can use it to monitor data drift and model performance:
import evidently
import pandas as pd
from evidently.model_monitoring import ModelMonitor
from evidently.model_monitoring.monitors import DataDriftMonitor, PerformanceMonitor
# Load reference data
ref_data = pd.read_csv("reference_data.csv")
# Load production data
prod_data = pd.read_csv("production_data.csv")
# Load model
model = load_model("model.pkl")
# Create data and performance monitors
data_monitor = DataDriftMonitor(ref_data)
perf_monitor = PerformanceMonitor(ref_data, model)
# Monitor data and performance
model_monitor = ModelMonitor(data_monitor, perf_monitor)
model_monitor.run(prod_data)
# Generate HTML report
model_monitor.report.save_html("model_monitoring_report.html")
In this example, we load reference and production data, as well as a trained model. We create instances of DataDriftMonitor and PerformanceMonitor to monitor data drift and model performance, respectively. We then run these monitors on the production data using ModelMonitor and generate an HTML report with the results.
Deployment with BentoML
BentoML simplifies the process of deploying and serving machine learning models. Here’s an example of how you can package and deploy a scikit-learn model using BentoML:
import bentoml
from bentoml.io import NumpyNdarray
from sklearn.linear_model import LogisticRegression
# Train model
clf = LogisticRegression()
clf.fit(X_train, y_train)
# Define BentoML service
class LogisticRegressionService(bentoml.BentoService):
@bentoml.api(input=NumpyNdarray(), batch=True)
def predict(self, input_data):
return self.artifacts.clf.predict(input_data)
@bentoml.artifacts([LogisticRegression.artifacts])
def pack(self, artifacts):
artifacts.clf = clf
# Package and save model
svc = bentoml.Service("logistic_regression", runners=[LogisticRegressionService()])
svc.pack().save()
# Deploy model
svc = LogisticRegressionService.load()
svc.start()
In this example, we train a scikit-learn LogisticRegression model and define a BentoML service to serve predictions. We then package the model and its artifacts using bentoml.Service and save it to disk. Finally, we load the saved model and start the BentoML service, making it available for serving predictions.
Conclusion
In the rapidly evolving field of machine learning, MLOps tools play a crucial role in streamlining the entire lifecycle of machine learning projects, from experimentation and development to deployment and monitoring. Tools like Weights & Biases, Comet, MLflow, Kubeflow, BentoML, and Evidently offer a range of features and capabilities to support various aspects of the MLOps workflow.
By leveraging these tools, data science teams can enhance collaboration, reproducibility, and efficiency, while ensuring the deployment of reliable and performant machine learning models in production environments. As the adoption of machine learning continues to grow across industries, the importance of MLOps tools and practices will only increase, driving innovation and enabling organizations to harness the full potential of artificial intelligence and machine learning technologies.
