Best MLOps Tools & Platforms 2022

Machine learning (ML) teaches computers to learn from data without being explicitly programmed. Unfortunately, the rapid expansion and application of ML have made it difficult for organizations to keep up, as they struggle with issues such as labeling data, managing infrastructure, deploying models, and monitoring performance.

This is where MLOps comes in. MLOps is the practice of optimizing the continuous delivery of ML models, and it brings a host of benefits to organizations.

Below we explore the definition of MLOps, its benefits, and how it compares to AIOps. We also look at some of the top MLOps tools and platforms.

What Is MLOps?

MLOps combines machine learning and DevOps to automate, track, pipeline, monitor, and package machine learning models. It began as a set of best practices but slowly morphed into an independent ML lifecycle management approach. As a result, it applies to the entire lifecycle, from integrating data and model building to the deployment of models in a production environment.

MLOps is a special type of ModelOps, according to Gartner. However, MLOps is concerned with operationalizing machine learning models, whereas ModelOps focuses on all sorts of AI models.

Benefits of MLOps

The main benefits of MLOps are:

• Faster time to market: By automating deploying and monitoring models, MLOps enables organizations to release new models more quickly.
• Improved accuracy and efficiency: MLOps helps improve models’ accuracy by tracking and managing the entire model lifecycle. It also enables organizations to identify and fix errors more quickly.
• Greater scalability: MLOps makes it easier to scale up or down the number of machines used for training and inference.
• Enhanced collaboration: MLOps enables different teams (data scientists, engineers, and DevOps) to work together more effectively.

MLOps vs. AIOps: What are the Differences?

AIOps is a newer term coined in response to the growing complexity of IT operations. It refers to the application of artificial intelligence (AI) to IT operations, and it offers several benefits over traditional monitoring tools.

So, what are the key differences between MLOps and AIOps?

• Scope: MLOps is focused specifically on machine learning, whereas AIOps is broader and covers all aspects of IT operations.
• Automation: MLOps is largely automated, whereas AIOps relies on human intervention to make decisions.
• Data processing: MLOps uses pre-processed data for training models, whereas AIOps processes data in real time.
• Decision-making: MLOps relies on historical data to make decisions, whereas AIOps can use real-time data.
• Human intervention: MLOps requires less human intervention than AIOps.

Types of MLOps Tools

MLOps tools are divided into four major categories dealing with:

1. Data management
2. Modeling
3. Operationalization
4. End-to-end MLOps platforms

Data management

• Data Labeling: Large quantities of data, such as text, images, or sound recordings, are labeled using data labeling tools (also known as data annotation, tagging, or classification software). Labeled information is fed into supervised ML algorithms to generate new, unclassified data predictions.
• Data Versioning: Data versioning ensures that different versions of data are managed and tracked effectively. This is important for training and testing models as well as for deploying models into production.

Modeling

• Feature Engineering: Feature engineering is the process of transforming raw data into a form that is more suitable for machine learning algorithms. This can involve, for example, extracting features from data, creating dummy variables, or transforming categorical data into numerical features.
• Experiment Tracking: Experiment tracking enables you to keep track of all the steps involved in a machine learning experiment, from data preparation to model selection to final deployment. This helps to ensure that experiments are reproducible and the same results are obtained every time.
• Hyperparameter Optimization: Hyperparameter optimization is the process of finding the best combination of hyperparameters for an ML algorithm. This is done by running multiple experiments with different combinations of hyperparameters and measuring the performance of each model.

Operationalization

• Model Deployment/Serving: Model deployment puts an ML model into production. This involves packaging the model and its dependencies into a format that can be run on a production system.
• Model Monitoring: Model monitoring is tracking the performance of an ML model in production. This includes measuring accuracy, latency, and throughput and identifying any problems.

End-to-end MLOps platforms

Some tools go through the machine learning lifecycle from end to end. These tools are known as end-to-end MLOps platforms. They provide a single platform for data management, modeling, and operationalization. In addition, they automate the entire machine learning process, from data preparation to model selection to final deployment.

Also read: Top Observability Tools & Platforms

Best MLOps Tools & Platforms

Below are five of the best MLOps tools and platforms.

SuperAnnotate: Best for data labeling & versioning

Superannotate is used for creating high-quality training data for computer vision and natural language processing. The tool enables ML teams to generate highly precise datasets and effective ML pipelines three to five times faster with sophisticated tooling, QA (quality assurance), ML, automation, data curation, strong SDK (software development kit), offline access, and integrated annotation services.

In essence, it provides ML teams with a unified annotation environment that offers integrated software and service experiences that result in higher-quality data and faster data pipelines.

Key Features

• Pixel-accurate annotations: A smart segmentation tool allows you to separate images into numerous segments in a matter of seconds and create clear-cut annotations.
• Semantic and instance segmentation: Superannotate offers an efficient way to annotate Label, Class, and Instance data.
• Annotation templates: Annotation templates save time and improve annotation consistency.
• Vector Editor: The Vector Editor is an advanced tool that enables you to easily create, edit, and manage image and video annotations.
• Team communication: You can communicate with team members directly in the annotation interface to speed up the annotation process.

Pros

• Easy to learn and user-friendly
• Well-organized workflow
• Fast compared to its peers
• Enterprise-ready platform with advanced security and privacy features
• Discounts as your data volume grows

Cons

• Some advanced features such as advanced hyperparameter tuning and data augmentation are still in development.

Pricing

Superannotate has two pricing tiers, Pro and Enterprise. However, actual pricing is only available by contacting the sales team.

Iguazio: Best for feature engineering

Iguazio helps you build, deploy, and manage applications at scale.

New feature creation based on batch processing necessitates a tremendous amount of effort for ML teams. These features must be utilized during both the training and inference phases.

Real-time applications are more difficult to build than batch ones. This is because real-time pipelines must execute complex algorithms in real-time.

With the growing demand for real-time applications such as recommendation engines, predictive maintenance, and fraud detection, ML teams are under a lot of pressure to develop operational solutions to the problems of real-time feature engineering in a simple and reproducible manner.

Iguazio overcomes these issues by providing a single logic for generating real-time and offline features for training and serving. In addition, the tool comes with a rapid event processing mechanism to calculate features in real time.

Key Features

• Simple API to create complex features: Allows your data science staff to construct sophisticated features with a basic API (application programming interface) and minimize effort duplication and engineering resources waste. You can easily produce sliding windows aggregations, enrich streaming events, solve complex equations, and work on live-streaming events with an abstract API.
• Feature Store: Iguazio’s Feature Store provides a fast and reliable way to use any feature immediately. All features are stored and managed in the Iguazio integrated feature store.
• Ready for production: Remove the need to translate code and break down the silos between data engineers and scientists by automatically converting Python features into scalable, low-latency production-ready functions.
• Real-time graph: To easily make sense of multi-step dependencies, the tool comes with a real-time graph with built-in libraries for common operations with only a few lines of code.

Pros

• Real-time feature engineering for machine learning
• It eliminates the need for data scientists to learn how to code for production deployment
• Simplifies the data science process
• Highly scalable and flexible

Cons

• Iguazio has poor documentation compared to its peers.

Pricing

Iguazio offers a 14-day free trial but doesn’t publish any other pricing information on its website.

Neptune.AI: Best for experiment tracking

Neptune.AI is a tool that enables you to keep track of all your experiments and their results in one place. You can use it to monitor the performance of your models and get alerted when something goes wrong. With Neptune, you can log, store, query, display, categorize, and compare all of your model metadata in one place.

Key Features

• Full model building and experimentation control: Neptune.AI offers a single platform to manage all the stages of your machine learning models, from data exploration to final deployment. You can use it to keep track of all the different versions of your models and how they perform over time.
• Single dashboard for better ML engineering and research: You can use Neptune.AI’s dashboard to get an overview of all your experiments and their results. This will help you quickly identify which models are working and which ones need more adjustments. You can also use the dashboard to compare different versions of your models. Results, dashboards, and logs can all be shared with a single link.
• Metadata bookkeeping: Neptune.AI tracks all the important metadata associated with your models, such as the data they were trained on, the parameters used, and the results they produced. This information is stored in a searchable database, making it easy to find and reuse later. This frees up your time to focus on machine learning.
• Efficient use of computing resources: Neptune.AI allows you to identify under-performing models and save computing resources quickly. You can also reproduce results, making your models more compliant and easier to debug. In addition, you can see what each team is working on and avoid duplicating expensive training runs.
• Reproducible, compliant, and traceable models: Neptune.AI produces machine-readable logs that make it easy to track the lineage of your models. This helps you know who trained a model, on what data, and with what settings. This information is essential for regulatory compliance.
• Integrations: Neptune.AI integrates with over 25 different tools, making it easy to get started. You can use the integrations to pipe your data directly into Neptune.AI or to output your results in a variety of formats. In addition, you can use it with popular data science frameworks such as TensorFlow, PyTorch, and scikit-learn.

Pros

• Keeps track of all the important details about your experiments
• Tracks numerous experiments on a single platform
• Helps you to identify under-performing models quickly
• Saves computing resources
• Integrates with numerous data science tools
• Fast and reliable

Cons

• The user interface needs some improvement.

Pricing

Neptune.AI offers four pricing tiers as follows:

• Individual: Free for one member and includes a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged.
• Team: Costs $49 per month with a 14-day free trial. This plan allows unlimited members and has a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged. This plan also comes with email and chat support.
• Scale: With this tier, you have the option of SaaS (software as a service) or hosting on your infrastructure (annual billing). Pricing starts at $499 per month and includes unlimited members, custom metadata storage, custom monitoring hours quota, service accounts for CI workflows, single sign-on (SSO), onboarding support, and a service-level agreement (SLA).
• Enterprise: This plan is hosted on your infrastructure. Pricing starts at $1,499 per month (billed annually) and includes unlimited members, Lightweight Directory Access Protocol (LDAP) or SSO, an SLA, installation support, and team onboarding.

Kubeflow: Best for model deployment/serving

Kubeflow is an open-source platform for deploying and serving ML models. Google created it as the machine learning toolkit for Kubernetes, and it is currently maintained by the Kubeflow community.

Key Features

• Easy model deployment: Kubeflow makes it easy to deploy your models in various formats, including Jupyter notebooks, Docker images, and TensorFlow models. You can deploy them on your local machine, in a cloud provider, or on a Kubernetes cluster.
• Seamless integration with Kubernetes: Kubeflow integrates with Kubernetes to provide an end-to-end ML solution. You can use Kubernetes to manage your resources, deploy your models, and track your training jobs.
• Flexible architecture: Kubeflow is designed to be flexible and scalable. You can use it with various programming languages, data processing frameworks, and cloud providers such as AWS, Azure, Google Cloud, Canonical, IBM cloud, and many more.

Pros

• Easy to install and use
• Supports a variety of programming languages
• Integrates well with Kubernetes at the back end
• Flexible and scalable architecture
• Follows the best practices of MLOps and containerization
• Easy to automate a workflow once it is properly defined
• Good Python SDK to design pipeline
• Displays all logs

Cons

• An initial steep learning curve
• Poor documentation

Pricing

Open-source

Databricks Lakehouse: Best end-to-end MLOPs platform

Databricks is a company that offers a platform for data analytics, machine learning, and artificial intelligence. It was founded in 2013 by the creators of Apache Spark. And over 5,000 businesses in more than 100 countries—including Nationwide, Comcast, Condé Nast, H&M, and more than 40% of the Fortune 500—use Databricks for data engineering, machine learning, and analytics.

Databricks Machine Learning, built on an open lake house design, empowers ML teams to prepare and process data while speeding up cross-team collaboration and standardizing the full ML lifecycle from exploration to production.

Key Features

• Collaborative notebooks: Databricks notebooks allow data scientists to share code, results, and insights in a single place. They can be used for data exploration, pre-processing, feature engineering, model building, validation and tuning, and deployment.
• Machine learning runtime: The Databricks runtime is a managed environment for running ML jobs. It provides a reproducible, scalable, and secure environment for training and deploying models.
• Feature Store: The Feature Store is a repository of features used to build ML models. It contains a wide variety of features, including text data, images, time series, and SQL tables. In addition, you can use the Feature Store to create custom features or use predefined features.
• AutoML: AutoML is a feature of the Databricks runtime that automates building ML models. It uses a combination of techniques, including automated feature extraction, model selection, and hyperparameter tuning to build optimized models for performance.
• Managed MLflow: MLflow is an open-source platform for managing the ML lifecycle. It provides a common interface for tracking data, models, and runs as well as APIs and toolkits for deploying and monitoring models.
• Model Registry: The Model Registry is a repository of machine learning models. You can use it to store and share models, track versions, and compare models.
• Repos: Allows engineers to follow Git workflows in Databricks. This enables engineers to take advantage of automated CI/CD (continuous integration and continuous delivery) workflows and code portability.
• Explainable AI: Databricks uses Explainable AI to help detect any biases in the model. This ensures your ML models are understandable, trustworthy, and transparent.

Pros

• A unified approach simplifies the data stack and eliminates the data silos that usually separate and complicate data science, business intelligence, data engineering, analytics, and machine learning. 
• Databricks is built on open source and open standards, which maximizes flexibility.
• The platform integrates well with a variety of services.
• Good community support.
• Frequent release of new features.
• User-friendly user interface.

Cons

• Some improvements are needed in the documentation, for example, using MLflow within existing codebases.

Pricing

Databricks offers a 14-day full trial if using your own cloud. There is also the option of a lightweight trial hosted by Databricks.

Pricing is based on compute usage and varies based on your cloud service provider and Geographic region.

Getting Started with MLOPS

MLOps is the future of machine learning, and it brings a host of benefits to organizations looking to deliver high-quality models continuously. It also offers many other benefits to organizations, including improved collaboration between data scientists and developers, faster time-to-market for new models, and increased model accuracy. If you’re looking to get started with MLOps, the tools above are a good place to start.

Also read: Best Machine Learning Software in 2022

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