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, comparable to continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets.
Because the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased. These tools help streamline your entire lifecycle of machine learning projects, from data preparation and model training to deployment and monitoring. On this comprehensive guide, we are going to explore among the top MLOps tools available, including Weights & Biases, Comet, and others, together with their features, use cases, and code examples.
What’s MLOps?
MLOps, or Machine Learning Operations, is a multidisciplinary field that mixes 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 speed up model development, improve deployment reliability, and maximize the worth derived from ML initiatives.
Constructing and Maintaining ML Pipelines
While constructing any machine learning-based services or products, training and evaluating the model on just a few real-world samples doesn’t necessarily mean the tip of your responsibilities. You’ll want to make that model available to the tip users, monitor it, and retrain it for higher performance if needed. A conventional machine learning (ML) pipeline is a set of assorted 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 liable for working on the primary 4 stages of the ML pipeline, while the last two stages fall under the responsibilities of the operations team. Since there’s a transparent delineation between the machine learning and operations teams for many organizations, effective collaboration and communication between the 2 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 strategy of deploying the ML models to production, together with maintaining and monitoring them. Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it may allow collaborations amongst data scientists, DevOps engineers, and IT teams.
The core responsibility of MLOps is to facilitate effective collaboration amongst ML and operation teams to reinforce the pace of model development and deployment with the assistance 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 sometimes called MLOps tools. After quite a lot of research, I even have curated a listing of assorted MLOps tools which can be used across some big tech giants like Netflix, Uber, DoorDash, LUSH, etc. We’re going to discuss all of them later in this text.
Varieties of MLOps Tools
MLOps tools play a pivotal role in every stage of the machine learning lifecycle. On this section, you will notice a transparent breakdown of the roles of a listing of MLOps tools in each stage of the ML lifecycle.
Pipeline Orchestration Tools
Pipeline orchestration by way of machine learning refers back to the strategy of managing and coordinating various tasks and components involved within the end-to-end ML workflow, from data preprocessing and model training to model deployment and monitoring.
MLOps software is de facto popular on this space because it provides features like workflow management, dependency management, parallelization, version control, and deployment automation, enabling organizations to streamline their ML workflows, improve collaboration amongst data scientists and engineers, and speed up the delivery of ML solutions.
Model Training Frameworks
This stage involves the strategy of creating and optimizing predictive models with labeled and unlabeled data. During training, the models learn the underlying patterns and relationships in the information, adjusting its parameters to reduce the difference between predicted and actual outcomes. You possibly can consider this stage as essentially the most code-intensive stage of your entire ML pipeline. That is the explanation why data scientists should be actively involved on this stage as they should check out different algorithms and parameter combos.
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 event team is finished training the model, they should make this model available for inference within the production environment where these models can generate predictions. This typically involves deploying the model to a serving infrastructure, organising APIs for communication, model versioning and management, automated scaling and cargo balancing, and ensuring scalability, reliability, and performance.
MLOps tools offer features comparable to 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 shouldn’t be a one-time process. Whenever you develop a model on a certain data distribution, you expect the model to make predictions for a similar data distribution in production as well. This shouldn’t be ideal because data distribution is liable to change in the actual world which ends up in degradation within the model’s predictive power, that is what you call data drift. There is simply one method to discover the information drift, by repeatedly monitoring your models in production.
Model monitoring and observability in machine learning include monitoring key metrics comparable to prediction accuracy, latency, throughput, and resource utilization, in addition to detecting anomalies, drift, and concept shifts in the information distribution. MLOps monitoring tools can automate the gathering of telemetry data, enable real-time evaluation and visualization of metrics, and trigger alerts and actions based on predefined thresholds or conditions.
Collaboration and Experiment Tracking Platforms
Suppose you’re working on developing an ML system together with a team of fellow data scientists. In the event you will not be using a mechanism that tracks what all models have been tried, who’s working on what a part of the pipeline, etc., it’ll be hard so that you can determine what all models have already been tried by you or others. There is also the case that two developers are working on developing the identical features which is de facto a waste of time and resources. And because you will not be tracking anything related to your project, you may most actually not use this data 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 comparable to 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 within the preprocessing phase. For some reason, in case you will not be in a position to train your model instantly, you must store this preprocessed data to avoid repeated work. The identical goes for the code, you’ll at all times need to proceed working on the code that you may have left in your previous session.
MLOps data storage and versioning tools offer features comparable to data versioning, artifact management, metadata tracking, and data lineage, allowing teams to trace changes, reproduce experiments, and ensure consistency and reproducibility across different iterations of ML models.
Compute and Infrastructure
Whenever you speak about training, deploying, and scaling the models, the whole lot comes all the way down to computing and infrastructure. Especially in the present time when large language models (LLMs) are making their way for several industry-based generative AI projects. You possibly can surely train a straightforward classifier on a system with 8 GB RAM and no GPU device, but it surely wouldn’t be prudent to coach an LLM model on the identical infrastructure.
Compute and infrastructure tools offer features comparable to 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 outstanding MLOps startups, several other tools can be found to support various points of the machine learning lifecycle. Listed here are just a few notable examples:
- MLflow: MLflow is an open-source platform that helps manage your 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 trace 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, goal 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’s Weights & Biases?
Weights & Biases (W&B) is a preferred 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 set 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 comparable to hyperparameters, model architecture, and dataset details. By logging these parameters, users can easily reproduce experiments and compare results, facilitating collaboration amongst 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 visualise experiment results, making it easy to investigate trends, compare models, and discover areas for improvement. These visualizations include customizable charts, confusion matrices, and histograms. The dashboard may 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 especially useful when experimenting with different architectures, hyperparameters, or preprocessing techniques. By maintaining a history of models, users can discover 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 comparable to 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’s Comet?
Comet is a cloud-based machine learning platform where developers can track, compare, analyze, and optimize experiments. It’s designed to be quick to put in and straightforward to make use of, allowing users to begin tracking their ML experiments with just just a few lines of code, without counting on any specific library.
Key Features of Comet
- Custom Visualizations: Comet allows users to create custom visualizations for his or her experiments and data. Moreover, users can leverage community-provided visualizations on panels, enhancing their ability to investigate and interpret results.
- Real-time Monitoring: Comet provides real-time statistics and graphs about ongoing experiments, enabling users to observe 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 which will arise throughout the training and evaluation process.
- Model Monitoring: Comet enables users to observe their models and receive notifications when issues or bugs occur, ensuring timely intervention and mitigation.
- Collaboration: Comet supports collaboration inside teams and with business stakeholders, enabling seamless knowledge sharing and effective communication.
- Framework Integration: Comet can easily integrate with popular ML frameworks comparable to TensorFlow, PyTorch, and others, making it a flexible tool for various projects and use cases.
Selecting the Right MLOps Tool
When choosing an MLOps tool on your project, it’s essential to contemplate aspects comparable to your team’s familiarity with specific frameworks, the project’s requirements, the complexity of the model(s), and the deployment environment. Some tools could also be higher fitted to specific use cases or integrate more seamlessly together with your existing infrastructure.
Moreover, it is vital to judge the tool’s documentation, community support, and the convenience of setup and integration. A well-documented tool with an energetic community can significantly speed up the educational curve and facilitate troubleshooting.
Best Practices for Effective MLOps
To maximise the advantages of MLOps tools and ensure successful model deployment and maintenance, it’s crucial to follow best practices. Listed here are some key considerations:
- Consistent Logging: Be sure 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 inside 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 on your machine learning models to make sure automated testing, deployment, and monitoring. This streamlines the deployment process and reduces the danger of errors.
Code Examples and Use Cases
To raised understand the sensible 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 may 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)
# Arrange 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 instance, we initialize a W&B run, train a ResNet-18 model on a picture classification task, and log the training loss at each step. We also save the trained model as an artifact using wandb.save(). W&B robotically tracks system metrics like GPU usage, and we will visualize the training progress, loss curves, and system metrics within the W&B dashboard.
Model Monitoring with Evidently
Evidently is a robust tool for monitoring machine learning models in production. Here’s an example of how you need to use it to observe 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 instance, we load reference and production data, in addition to a trained model. We create instances of DataDriftMonitor and PerformanceMonitor to observe data drift and model performance, respectively. We then run these monitors on the production data using ModelMonitor and generate an HTML report with the outcomes.
Deployment with BentoML
BentoML simplifies the strategy of deploying and serving machine learning models. Here’s an example of how you may 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 instance, 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 put it aside to disk. Finally, we load the saved model and begin the BentoML service, making it available for serving predictions.
Conclusion
Within the rapidly evolving field of machine learning, MLOps tools play a vital role in streamlining your 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 variety of features and capabilities to support various points 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. Because 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 total potential of artificial intelligence and machine learning technologies.
