Top End-to-End MLOps Platforms in 2024
In today's machine learning (ML) world, MLOps is key to getting ML models from the drawing board to real-world use. In this article, we're going to explore what MLOps is all about and its role in the ML lifecycle. We'll look at four end-to-end MLOps platforms that organizations commonly use to develop, test, and deploy models in production. Our journey covers three big names in the field: AWS, Google Cloud, and Databricks. We'll see what makes each of them unique in what they offer, how they operate, who they're designed for. Plus, we're throwing a new player into the mix - Qwak. As an emerging end-to-end MLOps platform, we'll see how it measures up against the big three.
By the end of this article, you'll have a clearer picture of which managed solution might fit your needs, their pros and cons, and how they stack up against popular open-source options. All this is to help you make a more informed choice in your ML journey.
MLOps, short for Machine Learning Operations, is a practice that intertwines the world of machine learning with the principles of software engineering. It's about making the process of developing, deploying, and maintaining ML models more efficient and effective. The concept emerged from the need to bridge the gap between experimental ML models and operational software, a challenge that became prominent as machine learning started being integrated into real-world applications. It's a response to the complexity that comes with bringing ML models into production, ensuring they're not just academic experiments but robust, scalable, and reliable solutions.
MLOps in the Machine Learning Lifecycle
The role of MLOps becomes clear when we look at the lifecycle of a machine learning model. This lifecycle isn't just about creating a model; it's about nurturing it from a concept to a fully functional tool. In this journey, MLOps acts as a guiding force, ensuring that each phase - from data preparation to model training, from validation to deployment, and ongoing maintenance - is conducted with precision and efficiency. It's about creating a synergy between these stages, so the transition from a data scientist's experiment to an operational model is as seamless as possible.
Solving Real-World Problems with MLOps
Why do we need MLOps? The answer lies in the challenges it addresses:
- Integration: It brings together disparate processes and tools into a unified workflow.
- Consistency: MLOps ensures that models perform reliably across various environments.
- Scalability: It tackles the complexities of scaling models to meet real-world demands.
- Collaboration: MLOps fosters a culture of collaboration among diverse teams such as data scientists, engineers, and business stakeholders. It also establishes standardization in practices and processes, ensuring that teams work in a harmonized manner, which is critical for maintaining the integrity and efficiency of ML projects.
- Compliance: It helps in maintaining the standards required in regulated industries.
From Machine Learning to MLOps
Understanding MLOps also means distinguishing it from traditional machine learning. While ML focuses on the creation and fine-tuning of algorithms, MLOps is about ensuring these algorithms can be consistently and effectively applied in real-world scenarios. It's an evolution from crafting a sophisticated tool to making sure the tool is practical, maintainable, and delivers value where it matters. MLOps isn’t just about the technology; it's about the process and the people, transforming machine learning from a science into a solution.
Understanding MLOps Tools vs MLOps Platforms
In MLOps, and software development at large, it's crucial to distinguish between tools (or solutions) and platforms, as they cater to distinct objectives.
MLOps tools typically focus on specialized functions or specific phases of the ML lifecycle. For instance, model experimentation might utilize open-source tools like MLflow or managed solutions such as Weights and Biases. This leads to a further distinction between open-source and managed tools:
- Open-source MLOps tools offer targeted capabilities, such as Airflow for job scheduling or Kubeflow for orchestration, serving as potential components of a self-constructed MLOps platform. However, these tools alone are often insufficient to address the entire ML lifecycle, necessitating a combination of several tools to create a comprehensive solution.
Building an end-to-end platform from individual MLOps tools—mixing open source and managed services—requires an integration layer that acts as “glue” to cohesively bind these individual tools into a singular, streamlined workflow. It's designed to be generic enough to accommodate a variety of tools and workflows, yet sufficiently robust to ensure forward compatibility and adaptability. By abstracting the complexities of individual tools, this integration layer empowers data scientists with a simplified, cohesive flow that enhances productivity without the need for deep expertise in each underlying technology. This approach is typically adopted by organizations with significant engineering resources but can result in extended development timelines.
Given that ML projects are notorious for accruing "technical debt," starting with a model that addresses a specific business challenge is comparatively straightforward. The complexity lies in scaling that model for production, ensuring it can handle evolving data, schemas, and requirements in a reliable, automated manner.
For those interested in assembling a toolkit of open-source MLOps utilities, our article on top MLOps open-source tools provides a curated list for building, training, deploying, and monitoring ML models.
- End-to-end ML platforms, such as SageMaker, Vertex AI, Databricks and Qwak, offer integrated suites of ML tools built atop AWS, GCP, and Azure infrastructure, respectively. These platforms aim to simplify operations for ML engineers and data scientists, providing a more cohesive and often more efficient solution than piecing together open-source tools. Though potentially costlier and with some limitations for custom ML workflows, these managed platforms typically reduce the overhead compared to developing a solution from scratch.
For a feature by feature analysis of SageMaker, Vertex AI, Databricks, and how Qwak measures up against them, please visit our comprehensive comparison guide.
The Challenge of ML Projects Success
With Gartner reporting a high failure rate (85%) for ML projects, the importance of selecting the right MLOps platform is overlooked. Success depends not just on the technology, but on how well it aligns with your organization's technical capabilities, operational workflows, and business goals.
User-Friendly and Maintainable Design
Rather than just simplicity, focus on whether the platform's architecture aligns with your existing tech stack and future scalability needs. Consider how the platform integrates with your data sources, handles large-scale data processing, and scales with increasing data volume and complexity. The platform should be flexible enough to accommodate evolving project requirements without becoming a bottleneck.
Beyond just looking at upfront costs, conduct a thorough cost-benefit analysis. This includes evaluating the total cost of ownership (TCO), which encompasses not only licensing fees but also costs related to integration, operation, maintenance, and potential scalability. Balance these costs against expected ROI, considering factors like improved efficiencies, potential revenue generation, and long-term strategic value.
Time to Market and Simplicity
Consider the platform's impact on your operational efficiency and time to market. A platform that requires extensive custom development or has a steep learning curve can delay project timelines. Assess the availability of pre-built models, tools for rapid prototyping, and automation features that can expedite development cycles. Additionally, consider the level of support and community around the platform, which can be crucial for problem-solving and innovation.
Ability to Replace
This reflects on how seamlessly a platform can be transitioned out or upgraded to a new solution without significant operational disruptions. Evaluate the platform’s design for modularity, support for open standards, and the ease of data and model migration. A platform that ensures straightforward replaceability safeguards against future technology lock-ins and supports sustained agility and innovation.
In choosing an MLOps platform, it's important to consider its usability, cost-effectiveness, and the balance between features and simplicity. A platform that aligns well with these aspects can significantly improve your ML project's chance of success.
MLOps Platform Comparison Criteria Overview
This analysis focuses primarily on the production aspects of Machine Learning, distinguishing between essential ("table stakes") and advanced ("best-in-breed") features within MLOps platforms. While the experimental phase of ML, including Managed Notebooks and Experiment Tracking, is crucial during model development, our emphasis will be on capabilities that simplify and accelerate the transition of models into production environments. For insights into the model experimentation aspect, particularly between Vertex AI and SageMaker, we recommend exploring external resources that provide detailed comparisons.
MLOps Capabilities Explored:
Data Processing Pipelines: We examine each platform's ability to process data into features, a capability often intertwined with or integral to the Feature Store. The analysis includes the tools offered for data processing and their usability.
Feature Store: As a relatively new concept in ML, the Feature Store's functionalities across platforms are also compared, including offline and online storage and feature transformation capabilities. The scope of the Feature Store varies, with some platforms treating it as a metadata repository, while others view it as a comprehensive system for managing the lifecycle of features from raw data to model consumption.
Model Training and Building: This section delves into the infrastructure provided for model training, the ease of setting up custom training jobs, and the availability of pre-built model frameworks (e.g., PyTorch, TensorFlow). Additionally, we assess the platforms' hyperparameter tuning, experiment tracking, and model registry features.
Model Deployment and Serving: We evaluate the options for deploying models (real-time, batch, streaming) across platforms, focusing on the ease of setup and unique attributes of each. Real-time deployment aspects such as A/B testing support and shadow deployments are also considered.
Inference Monitoring & Analytics: The platforms' capabilities for monitoring model endpoints, debugging performance, accessing performance charts, and logging runtime data are reviewed. Support for monitoring model performance, data quality, and drift alerting are key points of comparison.
Workflow Automation: The final area of comparison looks at how each platform facilitates the automation of ML workflows, including tools for orchestrating jobs and creating a seamless ML operational pipeline.
This comparison aims to provide a foundational understanding of the capabilities and distinctions among leading MLOps platforms, assisting users in making informed decisions based on their specific use cases and needs.
A Deep Dive into Leading ML(Ops) Platforms: SageMaker, Databricks, Vertex AI
1. AWS SageMaker - Amazon’s Machine Learning Platform
Amazon SageMaker, part of Amazon Web Services (AWS), offers a rich set of tools and services designed to make machine learning (ML) projects more manageable at a larger scale. It's more than just a platform; it's a whole ecosystem that integrates seamlessly with AWS's extensive suite of services.
At its core, Amazon SageMaker benefits greatly from AWS's powerful cloud infrastructure. This backbone provides strong support, but it also brings a level of complexity. Building a full-fledged ML lifecycle platform that is user-friendly for data science teams, often comprising experts in fields like physics and statistics, can be quite a challenge, especially for those who are not as familiar with computer science.
While SageMaker is packed with impressive features, it's not without its quirks. The user interface, including the SageMaker Studio IDE, can be a bit tricky to navigate, especially for those new to AWS services. Its simplicity in certain areas, like setting up notebook servers or hosting basic models, is relatively easy. However, when you try to customize or step outside the standard usage scenarios, things can get complicated. SageMaker aims to streamline processes, but this can sometimes lead to confusion if you encounter issues, as the underlying mechanics are not always visible.
In terms of engineering, SageMaker scores well, but some users find the overall experience lacking, especially in terms of the platform's performance and user interface. These aspects could benefit from improvements, particularly for tasks that demand a lot of time and effort.
If you're planning to use SageMaker locally, be prepared to work with Docker containers. This is key for running your training and inference operations before deploying them on the platform. Although SageMaker takes care of some aspects of infrastructure management, a solid understanding of AWS services and how they connect is essential. This includes setting up and managing resources like EC2 instances and IAM roles, navigating availability zones and VPCs, or integrating with other AWS services, which adds another layer of complexity.
When it comes to data preparation, AWS SageMaker Data Wrangler is a tool designed for simplifying the process of data manipulation. It assists in loading, querying, and analyzing data, which can then be stored separately for ingestion into ML pipelines. However, it's important to note that SageMaker Data Wrangler is primarily focused on data preparation and exploration, rather than large-scale data processing.
For large-scale data processing, AWS services like EMR (Elastic MapReduce) or AWS Glue are more commonly used. These services offer managed instances of Apache Spark, which is similar to what Databricks provides. The key advantage of using Apache Spark, whether through AWS EMR, AWS Glue, or Databricks, lies in its ability to process large datasets efficiently due to its distributed nature. Worth noting that while Glue is serverless, EMR requires some management on your side, for starting/shutting down clusters and scaling them.
In terms of building data processing pipelines for transforming data into features—either for direct training use or for storage in a Feature Store—SageMaker offers several options. These include running Apache Spark jobs, utilizing frameworks based processing such as scikit-learn, PyTorch, TensorFlow, etc., or creating custom processing logic in a Docker container. A processing job can be a component of a Model Build Pipeline, which integrates various steps from data processing to model deployment.
It's crucial to note that data is not automatically ingested into the SageMaker Feature Store. This ingestion is a distinct step that needs to be configured separately. The Feature Store itself serves as a central repository where features can be stored, retrieved, and shared across different machine learning models, providing consistency and reducing redundancy in feature engineering efforts.
The SageMaker Feature Store supports two primary methods of sourcing data: batch and streaming. Batch sourcing can pull data from various AWS services like S3, RDS, Redshift, etc. For streaming data, services such as Kinesis are used. This dual approach allows for flexibility in handling different types of data workflows.
The Feature Store provides a comprehensive Python SDK, enabling users to define its behavior and manipulate data. Additionally, Spark SQL transformations can be applied to process raw data, offering another layer of flexibility for data processing.
Regarding resource provisioning, the underlying resources for processing jobs in the Feature Store are automatically provisioned. This feature simplifies the management of infrastructure and scales according to the demands of the processing tasks.
Data in the Feature Store is organized into Feature Groups, which can be stored in both offline and online formats. For offline storage, the Feature Store uses Amazon S3, and the data is typically written in Parquet format, which is efficient for large-scale data storage. The online store's specific storage backend is not explicitly detailed in the public documentation, but it is optimized for low latency retrieval, making it suitable for real-time applications.
One notable limitation of the SageMaker Feature Store is the lack of an entity-based model for features. This means that features are not inherently synchronized between the offline and online stores, potentially leading to inconsistencies. Additionally, users are required to define a schema when creating feature groups. While this schema can be modified later, it adds an initial step that requires careful planning and consideration.
Model Training and Building
SageMaker employs the concept of a Training Job, a mechanism that supports various types of model training. This includes popular deep learning frameworks like TensorFlow and PyTorch, as well as the option to train custom models.
For custom models, you need to package your code into a Docker image, upload this image to AWS's Elastic Container Registry (ECR), and then specify this container for your training job. SageMaker automates the tracking of inputs and outputs for these containers, storing relevant metadata. This metadata can be visualized in SageMaker Studio, which provides insights into model performance and training metrics.
SageMaker also addresses more advanced training needs. It features the Training Compiler, which optimizes the training of deep learning models by efficiently utilizing underlying hardware. Additionally, SageMaker supports hyperparameter tuning with Bayesian Optimization, allowing for more effective and efficient model tuning.
For TensorFlow and PyTorch models, SageMaker offers distributed training capabilities. This feature is particularly useful for complex models that require training on large datasets, as it allows for training across multiple instances, including those with GPU support. Distributed training in SageMaker helps in scaling the training process both vertically (more powerful instances) and horizontally (more instances).
Regarding infrastructure, SageMaker offers a broad selection of instance types, including those equipped with GPUs, catering to diverse training needs.
The Model Registry in SageMaker is another notable feature. It acts more like a repository linking S3 artifacts with specific Docker containers (used for model training or inference). When deploying models, the Model Registry plays a crucial role in ensuring that only approved models and their respective artifacts are used, thus maintaining control and governance over the model deployment process.
Model Deployment and Serving
AWS SageMaker supports multiple inference options for models, including batch, serverless, and real-time. The setup process for these deployment types involves several steps, such as configuring the model with the necessary container image and artifacts, setting up endpoint configurations, and finally launching the actual endpoint. These steps can be executed through various methods, including the use of a Python SDK, providing flexibility in deployment workflows.
SageMaker's deployments are fully managed, and the platform supports autoscaling, allowing endpoints to automatically adjust capacity based on incoming traffic. Additionally, deployments in SageMaker are designed to be performed without causing downtime, ensuring continuous availability of services. Invoking a model for inference can be done through multiple interfaces, including SageMaker Studio, the Python SDK, and the AWS Command Line Interface (CLI).
Shadow testing is also available within SageMaker and it allows users to create model variants that receive a duplicated portion of the real traffic. This setup is useful for testing new models or model versions under actual operational conditions without impacting the primary inference workflow.
Model Variants are another feature that enables traffic splitting among different models or model versions. This capability is essential for A/B testing and gradual rollouts, where different percentages of the traffic are directed to different model variants.
SageMaker itself does not provide an out-of-the-box solution for triggering deployments automatically, as this is typically considered more related to software engineering than machine learning. Therefore, integration with external workflow automation tools is necessary for implementing such automated deployment processes.
Inference monitoring in AWS SageMaker begins with capturing inference data. This involves configuring SageMaker to store your model's prediction data in an Amazon S3 bucket. Once this data is captured, you can initiate a data quality monitoring job using either the SageMaker Python SDK or SageMaker Studio. This job, which can be scheduled to run periodically, compares your newly captured data against a predefined baseline, such as your training dataset.
For model monitoring, the approach varies based on the specific machine learning problem you are addressing. SageMaker allows you to select from various quality metrics, which are then compared against a baseline that you can set up within Amazon SageMaker Ground Truth, a machine learning data labeling service. These metrics provide insights into the performance and accuracy of your model.
To implement alerts for either data or model monitoring, you will need to use Amazon CloudWatch, AWS's monitoring service. CloudWatch enables you to set up alerts based on the metrics generated from your monitoring jobs. However, it's important to note that, as of now, SageMaker does not offer a built-in dashboard for these types of monitoring. Users need to rely on external tools or custom solutions for dashboarding capabilities.
In addition to the above, monitoring the performance metrics of your model's endpoints is crucial. This includes tracking error rates, throughput, latency, resource consumption, and model logs. While SageMaker automatically sends these metrics to CloudWatch, setting up alerts and configuring dashboards to visualize and monitor these metrics requires manual effort. Same applies to analyzing the captured inference data, you will need to set up a querying engine such as AWS Athena in order to access the S3 based Parquet files via SQL.
SageMaker Model Build Pipelines function as Directed Acyclic Graphs (DAGs), orchestrating a series of steps in a sequential and logical manner to create a comprehensive workflow.
Essentially, SageMaker Model Build Pipelines can be likened to a Continuous Integration (CI) system for machine learning. They enable the chaining of multiple machine learning steps, such as data preprocessing, feature engineering, model training, and hyperparameter tuning, to consistently produce a predictable and reliable model artifact. This systematic approach ensures that each iteration of the model is built with a standardized process, enhancing reproducibility and efficiency.
However, it's important to note the distinction between CI and Continuous Deployment (CD) in this context. While SageMaker Model Build Pipelines excel at automating the model building process (CI), deploying and testing a model in a production environment (CD) is a separate challenge. To achieve end-to-end model deployment and testing, you would still need to integrate with a traditional CD tool. This integration is essential for managing the deployment of the trained models to production environments, monitoring their performance, and facilitating continuous delivery and updates to the model based on real-world feedback and data.
SageMaker is particularly well-suited for teams already familiar with or invested in the AWS ecosystem. However, it’s worth mentioning its capabilities in the following areas:
- SageMaker Notebooks offer an efficient environment for developing and evaluating models nicely connected via the Python SDK to most of its ecosystem.
- SageMaker Studio: This tool provides a visual interface for ML teams to access machine learning capabilities and construct data and model pipelines.Though it may not be the most intuitive option available, it’s pretty powerful.
SageMaker is designed as a versatile machine learning platform, catering to a broad range of use cases. Recently, AWS introduced Bedrock, a new product aimed at addressing the gaps in SageMaker, particularly for generative AI and large language models (LLMs).
It's crucial to remember that all the features discussed in Amazon SageMaker operate within the broader AWS ecosystem. This integration involves configurations with AWS regions, IAM (Identity and Access Management) roles, and VPCs (Virtual Private Clouds). SageMaker, renowned for its comprehensive suite of tools, stands out as a robust platform for DevOps.
However, this “completeness” is a double-edged sword. While it offers a vast range of functionalities, it also introduces a significant learning curve, especially when it comes to understanding how to effectively integrate and utilize each component. SageMaker caters to diverse preferences and skill sets: whether you are a visually-oriented user who prefers the graphical interface of SageMaker Studio or a developer who is more comfortable scripting in Python and utilizing the SDK.
Initially, many users find themselves overwhelmed by the platform's complexity. This experience varies across different ML teams. Some appreciate the depth and flexibility offered by SageMaker's extensive feature set, viewing it as a comprehensive solution. Others, however, may find it overly intricate, leading them to seek an additional “platform” layer. This layer aims to streamline and simplify operations, creating more straightforward, “golden paths” for common tasks.
This variety in user experience underscores the importance of evaluating your team's expertise and requirements when considering SageMaker. It’s not just about the capabilities of the platform, but also about how well its complexity aligns with your team's ability to navigate and utilize it effectively.
2. Databricks - The Big Data Analytics Platform
Databricks has rightfully earned its place in Big Data analytics through its exceptional capability to process vast datasets. This is primarily facilitated by the Apache Spark engine, which stands at the core of Databricks' analytics and data processing prowess. A noteworthy feature of Databricks is its provision of managed open-source tools, such as MLflow for experiment tracking and model registry. These tools are invaluable for model development and experimentation, showcasing Databricks' strengths in handling large-scale data projects.
Despite its robust data engineering capabilities, Databricks is not inherently an ML-first platform, unlike some of the other platforms discussed in this article. It has, over time, integrated ML capabilities to cater to the growing demand for machine learning applications, positioning itself more as a data engineering platform with added ML functionalities.
A distinctive advantage of Databricks is its compatibility across multiple public cloud environments, including AWS, GCP, and Azure, with its inception being closely tied to Azure. This interoperability makes Databricks a versatile choice for organizations operating in multi-cloud environments.
Databricks has introduced capabilities such as Vector Search and an AI playground to stay current with emerging trends like generative AI, enhancing the platform's utility and offering users innovative tools to explore and integrate into their workflows.
Databricks excels in the domain of data processing by seamlessly integrating Apache Spark into its platform, thereby offering a robust managed Spark service. This integration is pivotal, as it enables users to utilize the expansive data processing capabilities of Spark without the complexities of managing the infrastructure. Databricks makes Spark readily accessible to its users through interactive notebooks that support Python, Scala, SQL, and R, allowing for versatile and familiar coding experiences.
A standout feature within this ecosystem is Delta Live Tables. By facilitating data transformation declarations in a mix of Python and SQL, Delta Live Tables significantly reduce the complexity typically associated with Apache Spark. This feature is adept at handling both batch and streaming data, offering flexibility in data processing tasks. Databricks not only simplifies the user experience but also enhances Spark’s capabilities by providing optimized and automatically updated versions of Spark. This ensures that users always have access to the latest features and performance improvements.
Databricks offers a robust feature store that supports both offline and online feature handling. Offline data is stored in Delta Tables, with the flexibility to push data to an online store for low-latency access. This is achieved either through Databricks Online Tables or via integration with third-party services like AWS DynamoDB. While setting up third-party services might require extra steps, it allows for customization based on project needs.
Online Tables are essentially read-only versions of Delta Tables optimized for quick access, scaling dynamically to meet demand. This setup suits varying traffic but be aware of potential latency during cold starts. Online Tables are still under preview as of February 2024 with limited capacity and data sizes.
Integration-wise, Databricks covers a broad spectrum. For batch processes, it mainly uses Delta tables, accommodating Spark, Pandas, and SQL. Streaming data is more flexible, with support for Kafka and Kinesis, showing Databricks' adaptability to different data sources.
Databricks simplifies working with historical data, offering automatic backfilling. It also enables on-demand feature computation, which calculates features as needed by models. This makes the data pipeline both efficient and adaptable to changing requirements. However, the specifics of feature serving monitoring could be clearer, indicating an area for potential enhancement in terms of visibility and control over feature performance.
Model Training and Building
Databricks integrates closely with MLflow, providing a managed environment that simplifies experiment tracking and model registry. This integration is key for comparing model performances and managing model artifacts efficiently. Databricks also leverages Spark’s MLlib for distributed model training, allowing for scalable machine learning workflows. For optimizing models, Databricks includes Hyperopt, a tool for hyperparameter tuning that works well with MLflow and Spark, supporting parallel tuning jobs.
The platform is compatible with popular ML frameworks such as scikit-learn, TensorFlow, and PyTorch, and it also supports custom Python code. This flexibility ensures that data scientists can work in a familiar environment, using "Jupyter-like" notebooks for interactive development and exploration. These notebooks can run on custom compute instances, providing the versatility needed for various ML tasks.
One of the strengths of Databricks in model training and building is the ability to automate model training jobs through Databricks Jobs. This feature allows for scheduling and automating workflows, making the model development process more efficient and scalable. The integration with MLflow means that all outputs from model training are automatically tracked and stored, streamlining the management of different model versions and their associated data.
Model Deployment and Serving
Databricks supports model deployment and serving through serverless endpoints and MLflow, streamlining the process of getting models into production. However, as of January 2024, Databricks has phased out CPU compute instances, focusing instead on GPU instances, which are still under review (beta phase). This shift underscores a commitment to supporting high-performance computing tasks but may require users to adapt their deployment strategies.
A unique aspect of Databricks’ deployment process is the need to bundle model dependencies with the model artifacts. This approach can lead to larger package sizes but ensures that all necessary components are included for model execution. For custom Python dependencies, users must store them in the Databricks File System (DBFS) and log them in MLflow, facilitating version control and consistency across model deployments.
Databricks offers customizable serving endpoints, allowing users to tailor the compute resources according to the model's requirements. The platform also supports zero-downtime deployments, a critical feature for maintaining service continuity. For models requiring custom inference logic, Databricks enables the use of a pyfunc, a Python function for initializing and running predictions, providing flexibility in handling various model types.
Integration with development tools is also supported, with Databricks offering a custom app for GitHub integration or the use of Databricks Repos. This feature simplifies code management and collaboration, making it easier to maintain and update models.
Despite these capabilities, Databricks notes a serving overhead of less than 50ms, which may not meet the requirements for applications demanding ultra-low latency. Additionally, for batch predictions, Databricks facilitates the use of notebooks or JARs as jobs, which can be run on demand or scheduled, though it currently lacks automated deployment capabilities.
Databricks offers monitoring capabilities for deployed models, focusing on endpoint health and performance. Users can access dashboards within the Databricks UI that display key metrics, helping to quickly identify any issues with model serving. Additionally, Databricks supports exporting these metrics to external monitoring solutions like Prometheus and Datadog, allowing for integration into broader system monitoring setups.
For deeper insights into model and data performance, Databricks automatically stores inference request data in Delta Tables. This feature enables detailed analysis and facilitates the identification of trends or anomalies over time. Users can set up notebook jobs to query this inference data, performing statistical analysis to detect model drift or data quality issues. This setup leverages baselines established from MLflow training data, offering a comprehensive view of model behavior in production environments.
However, setting up and managing these monitoring tasks requires a manual effort. Users need to create and maintain the monitoring jobs themselves, including writing the necessary queries and logic to analyze the data. While this approach offers flexibility and customization, it also places the responsibility on the user to ensure that monitoring is comprehensive and effective. Notifications, dashboard creation, and alert management need to be configured as part of the overall monitoring strategy, adding to the operational workload.
Databricks streamlines workflow automation through its Jobs feature, enabling scheduled and event-triggered execution of various tasks. These tasks can range from running Spark jobs, executing notebooks, to running custom Python scripts or data transformation pipelines using dbt. This flexibility allows for comprehensive automation of data processing, model training, and inference workflows.
Jobs in Databricks can be configured to run based on specific triggers, such as time schedules or the completion of preceding jobs, allowing for complex, multi-step workflows that can adapt to data availability or processing outcomes. Additionally, Jobs can be set up to run conditionally, for example, only executing if certain data quality metrics fall below predefined thresholds, ensuring that workflows are both efficient and intelligent in handling data and model dependencies.
Integration with external notification systems like Slack, PagerDuty, or custom webhooks is another key feature, enabling real-time alerts on job status, failures, or significant events. This ensures that teams can quickly respond to issues, maintain high levels of data processing and model performance, and keep stakeholders informed.
It's important to note that while Databricks Jobs provide a powerful mechanism for automating and orchestrating workflows, they do not cover all aspects of CI/CD for machine learning models. For complete automation, including model deployment and updates, users may need to integrate Databricks with external CI/CD tools or platforms, crafting a more comprehensive MLOps ecosystem.
Databricks stands out for its robust integration with Apache Spark and MLflow, making it a powerhouse for handling Spark jobs and ML pipelines. This strong foundation in Spark means that building comprehensive end-to-end ML pipelines can involve managing multiple components and potentially complex configurations. As such, a solid understanding of Spark is essential for teams looking to leverage Databricks effectively. This makes the platform particularly well-suited for organizations dealing with large volumes of data and those with significant data engineering requirements who can benefit from distributed processing capabilities.
Managed MLflow within Databricks enhances the appeal by offering streamlined experiment tracking and model management. However, the full potential of MLflow is best unlocked when used in conjunction with Databricks’ broader ecosystem. For companies already using Azure, Databricks' integration with this cloud platform adds another layer of convenience, making it an accessible choice for those already embedded in the Azure ecosystem.
Databricks’ notebook functionality is among its highlights, offering an interactive environment for data exploration and model development closely integrated with Spark. However, when it comes to model deployment and serving, the platform may not fully meet the needs or expectations of all users, indicating an area where Databricks could enhance its offerings. Overall, Databricks is best positioned for teams that require the scalability and power of Spark for complex data and ML tasks.
3. Vertex AI - Google’s Machine Learning Platform
Vertex AI, part of the Google Cloud Platform (GCP), benefits from deep integration with GCP's storage, compute infrastructure, and data sources. This integration facilitates a cohesive environment for ML development and deployment. However, feedback from the community suggests that Vertex AI experiences less stability compared to other platforms, attributed to Google's rapid feature expansion, which may affect the robustness of existing functionalities.
A notable strength of Vertex AI is its AutoML capabilities, highly praised by users for simplifying the creation of high-quality models without extensive ML expertise. While AutoML is a significant feature, our focus here is more on the MLOps capabilities of Vertex AI.
Vertex AI promotes ease of use through its Python SDK and client library, supported by a wealth of examples on GitHub from both Google and the community. Despite this, users have reported that Vertex AI's documentation can be confusing and lacks concrete programmatic examples for many operations, marking a potential area for improvement.
Like SageMaker, Vertex AI supports interaction through a Python SDK and offers a visual interface via the Google Cloud Console, catering to different preferences for managing ML projects. This dual approach aims to make ML operations accessible to a broader range of users, from those preferring code-based interaction to those who benefit from a graphical interface.
Vertex AI itself doesn't come with built-in data processing capabilities; instead, it relies on the broader Google Cloud ecosystem to fill this gap, presenting several options tailored to different use-cases:
Dataflow: This managed service handles both stream and batch data processing. Based on Apache Beam, Dataflow allows users to define high-level pipeline logic while Google manages the underlying serverless compute infrastructure. This arrangement enables developers to focus on pipeline design rather than operational overhead.
BigQuery: Google's flagship product for data storage and analytics, BigQuery facilitates SQL-based queries for transforming and analyzing structured data. It's a powerful tool for data warehousing needs, allowing users to run complex queries at scale.
Dataproc: As a managed Spark and Hadoop service, Dataproc offers a serverless environment for running more granular data transformations. After processing, data can be stored in BigQuery for further analysis or used as features in ML models.
These services collectively support a comprehensive data processing framework within the GCP ecosystem, enabling Vertex AI users to preprocess data effectively for ML models. While Vertex AI doesn't directly handle data processing, the integration with these Google Cloud services ensures that users have access to powerful tools for preparing their data for ML workflows.
The Feature Store in Vertex AI is rather straightforward, directly mapping BigQuery tables to what's known as Feature Groups. This simplicity means your data needs to be in BigQuery first, positioning the Feature Store more as a metadata layer on top of your data rather than a dynamic ingestion engine. It's a basic setup that gets the job done but might feel a bit minimalistic, especially if you're looking for advanced feature ingestion or real-time streaming capabilities.
Vertex's approach to feature storage is clear: BigQuery for offline, historical features and BigTable for online, low latency ones. Both are solid Google Cloud products designed for handling large datasets—a data warehouse and a NoSQL database, respectively. Yet, Vertex has been working on an even faster option for online features, which is currently under review. This could potentially address the speed concerns associated with using BigTable.
However, syncing data from offline to online stores seems to have its limitations, particularly around scheduling and manual triggering. This might not sit well with those needing a tight loop between data ingestion and availability in the online store, indicating a potential gap for use cases requiring up-to-the-minute data freshness in online features.
Model Training and Building
Vertex AI provides a flexible environment for model training, leveraging Google's infrastructure to support a range of ML frameworks. Users have the option to utilize pre-built container images for popular ML frameworks or create their own custom images. For those opting for pre-built containers, the platform allows uploading Python files or packages to Google Cloud Storage, which are then fetched at runtime. However, unlike some competitors, Vertex AI does not offer native support for directly pulling code from source control repositories like GitHub.
Dependencies in Vertex AI need to be specified as a list of strings within the Vertex SDK, presenting a less flexible approach compared to using a requirements.txt file or Conda environments. This could potentially complicate dependency management for more complex projects.
Vertex AI strongly encourages the use of Google Cloud Storage (GCS) and BigQuery for data sourcing, aligning with its ecosystem. While it's possible to integrate other data sources using Python connectors, managing authentication and secrets securely requires using Google's Secrets Manager.
For model experimentation and hyperparameter tuning, Vertex AI employs a Bayesian optimization framework, supporting both single-instance and distributed training jobs. Google also offers a Reduction Server to minimize gradients communication overhead between workers, aimed at speeding up training times for models, especially those trained on GPUs using TensorFlow or PyTorch.
Model registry in Vertex AI is facilitated through Google Cloud Storage, with the Python SDK enabling straightforward artifact storage. Experiment tracking and comparison are available through a user interface, although the platform limits automatic logging capabilities, such as logging dataframes or structured data, which might require manual efforts to fully leverage.
Model Deployment and Serving
Vertex AI streamlines the deployment and serving of machine learning models with options for both custom and pre-built container images. This flexibility allows users to deploy models according to their specific needs, whether by adhering to pre-defined artifact naming conventions for pre-built containers or by configuring custom containers to serve models via a bespoke application.
For testing model predictions, Vertex AI facilitates running the container locally, enabling a straightforward validation process before deployment. The platform supports both online and batch prediction scenarios:
- Online Predictions: Deploying models for online predictions involves creating an endpoint, deploying a model to that endpoint from a Docker image, and configuring the minimum and maximum replicas. Vertex AI automates scaling based on usage, though manual scaling policies can also be defined.
- Batch Predictions: Vertex supports batch serving with options for single and multi-replica configurations. Input formats include JSON, CSV, TensorFlow records, and BigQuery tables, with outputs directed to Google Cloud Storage or BigQuery, accommodating a range of data handling requirements.
Vertex AI enhances model serving with features like automatic serving runtime logs pushed to Google Cloud Logging and traffic splitting between models using the same endpoint. However, it lacks a built-in Shadow Deployment feature, potentially limiting more advanced deployment strategies.
Inference Monitoring & Analytics
Vertex AI provides tools for monitoring model performance post-deployment, essential for maintaining the reliability and accuracy of ML models in production. It automatically stores model endpoint request-response data in BigQuery, setting a fixed schema once a model is deployed. This fixed schema approach ensures consistency but limits flexibility for schema modifications post-deployment.
To facilitate model monitoring, Vertex AI enables users to set up monitoring jobs for endpoints, focusing on detecting skew and drift. For skew detection, users must provide their training dataset, either stored on Google Cloud Storage or BigQuery, allowing Vertex AI to compare incoming data against the training data to identify discrepancies.
Vertex AI's monitoring capabilities extend to feature distribution histograms and alert setup for notifications via email or other channels, enhancing oversight of model performance. Recently, Vertex introduced batch model monitoring, currently in pre-General Availability, broadening the scope of its monitoring features.
For endpoint-specific metrics, Vertex AI collects data on resource usage, predictions per second, error rates, and latency. These metrics are available directly on the endpoint's dashboard in Google Cloud Console, offering a quick overview of model health and performance.
For automating machine learning workflows, Vertex AI leverages Kubeflow Pipelines and TensorFlow Extended (TFX), catering to developers and platform engineers seeking sophisticated orchestration tools. Kubeflow, a mature and widely adopted open-source project, offers a robust framework for deploying, monitoring, and managing ML workflows across various environments.
Kubeflow Pipelines in Vertex AI allow for the creation of reusable end-to-end ML workflows, facilitating the automation of tasks from data preparation to model training and evaluation. This integration empowers users to build complex pipelines that can be easily scaled and replicated, enhancing productivity and ensuring consistency across projects.
TensorFlow Extended (TFX) further complements these capabilities by providing a comprehensive suite of libraries and components designed specifically for TensorFlow users. TFX supports advanced ML practices, such as continuous training and delivery, making it easier to iterate on models and deploy updates in production environments.
The combination of Kubeflow Pipelines and TFX in Vertex AI represents a powerful toolset for MLOps professionals. However, the complexity of these tools may require a steep learning curve, particularly for those new to ML workflow orchestration, underscoring the importance of having experienced developers and platform engineers to leverage these capabilities fully.
Vertex AI is optimally designed for organizations deeply embedded within the Google Cloud Platform ecosystem, leveraging GCP's comprehensive range of services from data storage to analytics. Its tight integration with GCP makes it an attractive choice for those already using Google Cloud services, offering a seamless experience for building, deploying, and managing machine learning models.
The platform is particularly appealing to teams prioritizing AutoML capabilities, given Vertex AI's strong performance in automating the model development process. This feature simplifies machine learning for users without deep technical expertise in model building, enabling a broader range of professionals to implement ML solutions effectively.
However, the feedback regarding stability and the rapid introduction of new features over refining existing ones suggests that Vertex AI may be best suited for teams that are adaptable and willing to navigate through a fast-evolving platform. This environment can provide cutting-edge tools and capabilities but may require a tolerance for changes and updates that could impact workflow stability.
Vertex AI's comprehensive toolset, including support for Kubeflow Pipelines and TensorFlow Extended (TFX), caters to experienced developers and platform engineers. These tools offer sophisticated capabilities for orchestrating complex ML workflows, making Vertex AI a strong match for teams looking to implement advanced MLOps practices.
4. Qwak - The End to End MLOps Platform
Qwak is a comprehensive MLOps platform designed to simplify the entire ML lifecycle, making it significantly easier for Data Science and ML engineering teams to navigate. By integrating essential production-grade requirements—such as performance monitoring, autoscaling, and data monitoring—right out of the box, Qwak positions itself as a user-friendly solution that minimizes the need for external setup or third-party tools. Its modular architecture encompasses model building, deployment, collaboration notebooks, feature store, and vector store, facilitating a cohesive and efficient environment for ML projects.
A standout feature of Qwak is its adaptability as either a SaaS product or a hybrid solution deployable within a user's AWS or GCP cloud accounts. This flexibility ensures that data and compute resources remain under the organization's control, addressing privacy and security concerns directly.
The platform's accessibility is further enhanced by the Qwak CLI and Python SDK, offering a dual approach to interaction. Users can perform most operations either through the command line, providing a seamless experience for those accustomed to script-based workflows, or via the Python SDK for those who prefer a programmatic approach.
Qwak's commitment to simplifying ML operations, combined with its focus on reducing setup complexity and providing a standardized suite of tools for model deployment, positions it as a valuable contender in the MLOps space. This approach not only accelerates the deployment process but also ensures that models are production-ready, catering to both novice and experienced ML teams seeking efficient, scalable solutions.
Qwak's approach to data processing allows for defining high-level transformations using SQL queries and UDFs, accommodating both batch and streaming data sources. This flexibility is crucial for teams working with diverse data types and sources. For batch data, Qwak supports ingestion from a variety of cloud storage sources, including CSV or Parquet files on Amazon S3, BigQuery, Postgres, Amazon Athena, MongoDB, Redshift and ClickHouse. Its integration with Kafka for streaming data underscores Qwak's commitment to providing comprehensive data processing capabilities.
A distinctive feature of Qwak is the seamless incorporation of data processing into Feature Sets within the Feature Store. This integration means that the data transformation logic is closely tied to the feature management process, simplifying the pipeline from raw data to usable features for machine learning models. The processing infrastructure, although abstracted away from the user, is powered by Spark and Spark Structured Streaming, ensuring robust and scalable data processing behind the scenes.
Scheduled processing jobs, configurable via cron expressions, offer the flexibility to automate data transformation tasks based on specific timing requirements. This capability is particularly beneficial for maintaining fresh data in feature stores or preparing data for scheduled training jobs, enhancing overall workflow efficiency.
Qwak's Feature Store simplifies the management of ML features by offering a streamlined interface for both offline and online feature handling. By automatically syncing transformed data into features, Qwak ensures that data scientists can easily access the most relevant and up-to-date features for their models.
For offline features, Qwak utilizes Iceberg files, an optimized format based on Parquet while the online store leverages an in-memory cache database for fast access to features, addressing the need for low-latency feature retrieval in real-time applications.
Qwak also includes dashboards for monitoring features' data distribution and the performance of serving endpoints for online features. These tools are essential for ensuring that features are correctly distributed and that the online store meets the latency requirements of production applications. For the offline store, Qwak offers the capability to interactively query data using SQL syntax.
Access to the Feature Store is facilitated through Qwak's Python SDK or via REST calls, allowing data scientists to seamlessly integrate feature retrieval into their model training and inference workflows.
Model Training and Building
In Qwak, model training and building are streamlined through an intuitive process, with support for both CPU and GPU instances. Users can define their models by inheriting from the QwakModel class, which outlines methods for training the model and making predictions. This approach simplifies the coding process, making it accessible even for those with limited experience in custom model development.
Qwak provides general container images for training but also supports the use of custom images, catering to a variety of training environments and requirements. The entire pipeline, from infrastructure provisioning to environment setup and artifact validation, is managed within Qwak's platform. This integrated management includes running unit and integration tests, described through Python code, further automating the model development lifecycle.
The platform automates the publication of the model container image to the registry once the training is complete, ensuring that the deployment process is as efficient as possible. Model code can be sourced from a local machine, a remote workspace, or directly from a GitHub repository, offering flexibility in how and where models are developed and stored. Models and data can be registered to Qwak’s default registry or integrate your own jFrog account.
While Qwak currently does not provide built-in hyperparameter tuning, however it doesn’t limit users in integrating other tools such as Optuna, for example. Additionally, Qwak plans to introduce distributed training tools in the near future.
Local testing of models is facilitated by a Python import that replicates the cloud environment, enabling rapid feedback loops during development.
Training job logs and resource consumption dashboards are available through the platform's UI and for model experimentation, Qwak features a comparison tool that allows users to evaluate different models based on parameters, metrics, and configuration settings.
The model registry is easily accessible via the Python SDK, offering logging and loading for model artifacts, Python DataFrames, parameters and metrics.
Model Deployment and Serving
Deploying and serving models in Qwak is designed to be straightforward, supporting zero-downtime deployments on both CPU and GPU instances. The platform allows for easy deployment with just a few clicks in the UI or a simple command via the Qwak CLI. This user-friendly approach significantly reduces the complexity typically associated with moving models from training to production.
Qwak enables multiple model builds to be deployed to the same endpoint, with features like traffic splitting, audience-based splitting, and shadow deployments. These capabilities allow for sophisticated deployment strategies, such as A/B testing and canary releases, without impacting live traffic. Real-time predictions can be made through REST API, Python, Java, or Go clients, catering to a wide range of application requirements.
For streaming data, Qwak offers seamless integration with Kafka, making streaming deployments as straightforward as batch and real-time deployments. This consistency across deployment types underscores Qwak’s commitment to flexibility and ease of use.
Batch predictions in Qwak are configurable for execution on one or more executors, with support for sourcing data from local Python DataFrames or S3-based CSV, JSON, or Parquet files. This versatility ensures that Qwak can accommodate a broad spectrum of data processing and prediction needs.
In essence, Qwak simplifies the deployment and serving process, enabling rapid, flexible, and reliable model deployment strategies that are easily manageable within its platform.
Inference Monitoring & Analytics
Qwak automatically logs inference requests and responses, laying the groundwork for comprehensive model and data monitoring. Users also have the option to manually log additional data within their prediction calls.
The platform provides alerting mechanisms for identifying data/concept drift and training-serving skew, utilizing KL divergence metrics. This allows for proactive monitoring of model performance and data quality, ensuring that any significant deviations are quickly identified and addressed. For binary classification models, Qwak enables visualization of key metrics such as F1 score, Accuracy, Recall, and Precision, offering a clear view of model effectiveness.
Endpoint monitoring in Qwak includes out-of-the-box charts for throughput, error rates, average latency, and resource utilization. Additionally, users can define layered latency measurements within their prediction calls, offering deeper insights into the performance of their models in production environments.
The logged predictions feature a query engine, enabling detailed investigation of inference requests. This capability is crucial for diagnosing issues and optimizing model performance post-deployment, ensuring that models continue to meet or exceed their expected performance criteria.
Qwak enhances the MLOps lifecycle with automated jobs for building and deploying models based on specified metrics, integrating alerting mechanisms for successful runs or failures via Slack. This automation extends to cron-scheduled jobs, enabling consistent and timely updates to models and data pipelines without manual intervention.
With the same principle Batch Predictions can be executed automatically at specific time intervals, with individual configurations than the original batch deployments.
Qwak’s integration with GitHub Actions enables automatic model building and deployment directly from GitHub repositories. This connection allows for actions like building a model from a new branch upon a pull request, supporting a CI/CD approach for ML models. This integration simplifies the process of updating models in response to code changes, accelerating the agility of ML operations.
Qwak operates on a pay-as-you-go pricing structure, where costs are calculated based on Qwak Processing Units (QPUs/hour), equating to $1.2 for compute resources (equivalent to 4vCPUs with 16GiB of RAM). These resources are billed by the minute, tailored to the specifics of the job, whether it's for training on CPU or GPU instances, or for model deployment and batch execution tasks.
For data storage, Qwak's pricing is $60 per TB of data stored for the Offline Feature Store and $70 per GB per month for the Online Store, catering to different data access needs and scales of operation. This clear-cut pricing model is designed to match the resource utilization of ML projects, ensuring users pay only for what they use.
Qwak is particularly suited for teams looking for an end-to-end MLOps solution that simplifies the transition from model development to deployment. Its strength lies in abstracting away the complexity of infrastructure management, making it an excellent choice for data science and ML engineering teams eager to deploy models without delving into the operational details.
The platform is designed to support both batch and real-time use cases, with a strong emphasis on providing a streamlined suite of MLOps tools. This makes Qwak ideal for teams aiming to deploy custom models on a production-level infrastructure quickly and efficiently.
Qwak’s approach to providing "golden paths" means it offers standardized workflows for data pipelines, model training, and deployment. This standardization is beneficial for data science teams looking for a straightforward path to bringing models to production, minimizing the need to manage docker files, container images, or container registries.
Given its ease of use, flexibility, and comprehensive feature set, Qwak serves well for both teams new to deploying ML models in production environments and experienced ML platform engineering teams seeking to empower their data science counterparts with robust MLOps tools.
This comparative analysis draws upon a variety of sources, including official platform documentation, user reviews, and discussions within community forums, to provide a nuanced perspective on the capabilities of each MLOps platform discussed. It aims to offer insights that blend objective assessments with a "subjective" understanding of user experiences and community sentiment.
It's important to recognize that this comparison serves as a foundational guideline rather than an exhaustive evaluation. Each platform's strengths and suitability can vary greatly depending on specific project requirements, team expertise, and operational contexts. Consequently, we encourage readers to explore these platforms firsthand, conducting thorough evaluations to determine which solution best aligns with their unique needs and objectives.
We are open to expanding this comparison to include additional managed platforms and would appreciate your input on any critical features or considerations that might have been overlooked in this review. Your feedback is invaluable in ensuring that our analysis remains relevant, comprehensive, and useful for the broader MLOps community.