The excitement around artificial intelligence and machine learning keeps growing. With so many opportunities to apply ML across industries, companies are racing to build models. However, the bulk of effort happens after initial model development. Building a sustainable, scalable ML pipeline requires MLOps.
MLOps brings DevOps-style automation and monitoring to machine learning. The key goal of MLOps is increasing efficiency, reducing errors, and managing models at scale.
In this comprehensive guide, we’ll explore the top MLOps platforms to help you deploy models to production.
What to Look for in an MLOps Platform
With so many tools claiming to enable MLOps, it can get overwhelming to choose the right platform. Here are key capabilities to evaluate:
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Model Registry – Central registry to store models and track metadata like versions, parameters, metrics etc.
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Experiment Tracking – Log factors, metrics, and results during model development and compare experiments.
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Model Monitoring – Monitor models in production, spot issues like data drift, and trigger alerts or re-training.
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Infrastructure Management – Platform should abstract infrastructure or natively integrate with cloud providers.
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Deployment and Serving – Serve models at scale via API endpoints and minimize latency.
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Collaboration – Enable teams to work together on models through features like shared notebooks.
Let‘s explore platforms that excel in these MLOps capabilities.
MLFlow
MLFlow is arguably the most popular open-source MLOps library. Created by Databricks, it‘s free to use and has four main components:
Tracking – Log metrics, parameters, code versions during runs
Projects – Standard format for packaging data science code
Models – Deploy machine learning models into diverse serving environments
Registry – Central model store for discovering, collaborating and governance
MLFlow components work well together but can also be used individually. For example, combine MLFlow Tracking with SageMaker for a full MLOps stack.
MLFlow also has ample integrations with data science tools like TensorFlow, PyTorch, and Spark. It works seamlessly with Google Cloud, AWS SageMaker, and Azure Machine Learning.
For small teams with open-source affinity, MLFlow delivers full MLOps capabilities without vendor lock-in.
Azure Machine Learning
Part of Microsoft‘s cloud ecosystem, Azure Machine Learning provides an end-to-end platform for the ML lifecycle.
It takes care of data preparation, model building, training pipelines, deployment, and monitoring. You can leverage Azure‘s global infrastructure to train models faster while reducing costs.
Azure ML offers options to build models through the visual interface or your own Python scripts. It integrates tightly with open-source libraries like PyTorch and TensorFlow.
For version control, Azure ML has first-class GitHub and Git support. Set up workflows to trigger model retraining upon check-ins to your Git repo.
Azure also enables easy model deployment to Kubernetes clusters and real-time data pipelines. Monitor models post-deployment and auto-scale resources based on load.
For companies invested in Microsoft‘s cloud, Azure ML is the obvious enterprise MLOps choice.
Vertex AI
Vertex AI is Google Cloud‘s unified managed ML platform, covering the full machine learning lifecycle.
It consolidates Google‘s existing ML products like AutoML Tables, Video Intelligence API, and Watson Language Translator.
Vertex AI is accessible to developers through SDKs and an API for coding models. It also has a no-code environment enabling business users to build without writing code.
For deployment, Vertex AI integrates nicely with serverless options like Cloud Run. And you get out-of-the-box monitoring and alerts to track model performance post-deployment.
Vertex AI brings Google Cloud‘s strong AI, storage, and compute capabilities together in one workflow. For GCP customers, it‘s a compelling offer.
Databricks
Databricks is a data-first company, focused on data warehouses, data lakes and ML on massive datasets.
As Databricks created MLFlow, it’s no surprise that MLFlow capabilities are deeply integrated into Databricks.
Within its visual workspace, Databricks allows rapid prototyping through notebooks in Python, R, SQL and Scala. It handles infrastructure provisioning and optimization in the background.
Databricks also simplifies deployment with a one-click way to expose models via API endpoints. The integrated model registry enables discovering models published by others in your organization.
Overall, Databricks excels at collaborative, reproducible model development leveraging its speed and scalability strengths.
SageMaker
AWS SageMaker targets end-to-end MLOps workflows fully within the AWS ecosystem.
It provides Jupyter notebooks with convenient access to S3 data sources and common machine learning packages.
SageMaker handles infrastructure management through integration with EC2, EBS, and S3 instances. It enables fast, distributed training without machine resource configuration.
Model deployment is also simplified with SageMaker hosting models behind secure endpoints. Take advantage of other AWS services like CloudWatch monitoring and integration with Lambda functions.
For AWS customers, SageMaker delivers convenience and optimized cost structure within AWS.
DataRobot
DataRobot captures the full machine learning lifecycle via dashboards tailored to different users.
It supports rapid prototyping and automation from data prep to optimal model selection to ongoing model monitoring. Thousands of modeling combinations can run in parallel to determine the best performers.
DataRobot requires no coding for model building but also supports custom Python scripts. It captures key model metadata like accuracy metrics, training times and other parameters to simplify model comparison.
Once the best model is selected, deployment to production happens via a single click. DataRobot Apps then enables building custom UIs with no coding for business users.
For larger enterprises seeking an end-to-end, code-light MLOps option, DataRobot is a strong contender.
Run:AI
Run:AI takes a unique approach among MLOps tools. Instead of managing models themselves, Run:AI focuses on GPU orchestration.
It sits between your MLOps platform and hardware infrastructure. Run:AI then schedules efficient processing of training workloads across all connected GPUs.
This helps optimize GPU utilization, whether on-premises servers, public cloud instances, or a hybrid setup. Infrastructure can scale up or down automatically based on load patterns.
Overall, Run:AI accelerates model training by removing GPU bottlenecks across distributed hardware. It’s cost-efficient and can plug into your existing MLOps stack.
H2O Driverless AI
H2O.ai pioneered the open source distributed machine learning space with H2O, Sparkling Water and now Driverless AI.
Driverless AI focuses on automatic feature engineering and model building to make data scientists more productive. It handles time-consuming data munging and hyperparameter tuning automatically through AutoML.
H2O also provides tools for model explainability, monitoring, and governance. These capabilities like LIME and Shapley force estimations build trust in model predictions.
The platform simplifies deployment to production through MOJOs and Python scoring pipelines. H2O also enables A/B testing models to confirm model efficacy.
For advanced users and data science teams, H2O Driverless AI accelerates machine learning experimentation through automation.
Key Takeaways
MLOps platforms provide structure, automation and best practices for organizations scaling up machine learning. They empower faster experimentation, simplified model management and monitoring workflows.
When evaluating options, consider your team’s size, skills, infrastructure, and whether you need an open-source tool. Also factor in integrations with your other data, analytics and DevOps tools.
With MLOps maturity still relatively low across companies, there’s ample room for growth in this space. We expect further consolidation and vertical specialization as the market evolves.
