What is a managed machine learning platform?

A managed machine learning platform is a cloud-based service that simplifies and streamlines the process of developing, deploying, and managing machine learning models. It is designed to provide a user-friendly interface and abstract away the complexities of underlying infrastructure, allowing data scientists and developers to focus on building and training models rather than managing the underlying infrastructure and software stack.

Code is only a small component of any machine learning solution. Usually companies have to use different tools and services to manage a machine learning solution end-to-end, including:

• Compute services to wrangle data and train machine learning models;
• Data management tools to clean, modify, track, and secure data;
• Software engineering tools to write and maintain code;

Dashboarding tools to interact with the solution and view results.

The goal of managed machine learning services is to centralize these components into a single packaged solution.

But not all managed machine learning services are fully comparable. Tools like AWS Sagemaker help you manage the complexity inherent in any machine learning solution, but still expect you to have engineers on your team who can build and understand the code. These tools focus more on the compute layer.

 Tools like Alteryx focus more on the presentation layer, and they try to hide the complexity, providing no-code user interfaces to integrate basic machine learning.

Dataiku, Alteryx, Amazon SageMaker, DataRobot, and Databricks are all popular platforms in the field of data science and machine learning, each with its strengths and use cases.

Dataiku: Dataiku is a collaborative data science platform that enables teams to work together on data projects. It provides tools for data preparation, visualization, machine learning, and deployment. Dataiku supports multiple programming languages, making it flexible for data scientists and engineers. It emphasizes usability and collaboration, making it suitable for organizations with diverse teams.

Alteryx: Alteryx is a self-service data analytics platform that allows users to perform data blending, data preparation, and advanced analytics without writing code. It offers a visual workflow interface, making it accessible to users with varying technical backgrounds. Alteryx is popular among business analysts and data professionals who want to derive insights from data without extensive programming knowledge.

Amazon SageMaker: Amazon SageMaker is part of Amazon Web Services (AWS) and is a cloud-based platform for building, training, and deploying machine learning models at scale. It offers pre-built algorithms, managed Jupyter notebooks, and infrastructure for distributed training. SageMaker is suitable for developers and data scientists who work within the AWS ecosystem and require scalable and cost-effective machine learning solutions.

DataRobot: DataRobot is an automated machine learning platform that aims to make machine learning accessible to users with various skill levels. It automates the end-to-end process of building and deploying machine learning models, including feature engineering, model selection, and hyperparameter tuning. DataRobot is useful for organizations that want to speed up their machine learning development process and for users with limited expertise in data science.

Databricks: Databricks is a unified analytics platform that integrates data engineering, data science, and business analytics. It is built on Apache Spark, making it suitable for processing large-scale data and running distributed computing tasks. Databricks provides collaborative workspaces, interactive notebooks, and tools for data exploration, making it valuable for data engineering and data science teams.

Dataiku vs. Alteryx

Dataiku and Alteryx are both managed machine learning platforms, but Dataiku focuses on the engineering aspects, while Alteryx focuses on analytics and presentation.

Dataiku provides Data Science Studio (DSS), a cross-platform desktop application that includes a notebook (similar to Jupyter Notebook) for engineers to write code and a workflow orchestration tool (similar to Apache Airflow) to manage data and tasks. While it provides some user interfaces, there’s still an emphasis on writing code. By contrast, Alteryx provides a better dashboarding experience but less flexibility: In Alteryx you use the UI to create no-code machine learning components.

• Use Dataiku if your team is technical, and you want your data scientists, engineers, and analysts to all use the same tool.
• Use Alteryx if your team is less technical and you want to do advanced analytics using prebuilt components.

Dataiku vs. Databricks

Both Dataiku and Databricks aim to allow data scientists, engineers, and analysts to use a unified platform, but Dataiku relies on its own custom software, while Databricks integrates existing tools. Databricks acts as the glue between Apache Spark, AWS or Azure, and MLFlow, and provides a centralized interface to connect these.

Dataiku is a higher-level tool, with integrations for machine learning libraries like Tensorflow and an AutoML interface that can do machine learning on data in a spreadsheet format.

• Use Dataiku if you’re comfortable managing your own infrastructure but want a platform to manage your machine learning pipelines and analytics.
• Use Databricks if you want a platform that manages your infrastructure for you and you’re comfortable with Apache Spark.

Dataiku vs. Datarobot

Datarobot and Dataiku both provide AutoML: a no-code machine learning platform where you can upload your data as spreadsheets, choose a target variable, and have the platform choose and optimize a machine learning model for you.

It’s important to note that this is Datarobot’s core focus, but it’s only one component of Dataiku, which also offers a full suite of data science tooling, including an IDE, a task orchestrator, and visualization tools.

• Use Datarobot if you have existing clean datasets and want to use predefined machine learning models to analyze your data, with no engineering skills required.
• Use Dataiku if you need something more flexible to help you design and build your own custom machine learning models.

Dataiku vs. Sagemaker

Dataiku focuses on providing coding and analytics tools for data scientists and engineers, while Sagemaker focuses on the underlying infrastructure: the servers that run and serve these models. Dataiku provides an integration to Sagemaker, but Sagemaker is also releasing tools that directly compete with Dataiku: Sagemaker Studio and Sagemaker Autopilot.

You can either use these platforms in combination, using Dataiku to build and manage your models and Sagemaker to train and serve them, or you can use Sagemaker for everything.

• Use Dataiku if you need a more mature platform with a focus on user interfaces and user experience, one that both your engineers and your analysts can use.
• Use Sagemaker if you have more engineers than analysts, you need more flexibility, and you don’t mind interfaces that are still being iterated on and lack polish.

Alteryx vs. Datarobot

Alteryx is a broader solution that provides analytics, data management, and dashboarding components as well as no-code machine learning. Datarobot has a narrower focus on no-code machine learning.

• Use Alteryx if your focus is on data and analytics, and you need a platform for your whole organization.
• Use Datarobot if you have an existing dataset, and you want to analyze it using predefined and curated machine learning models.

Alteryx vs. Knime

Alteryx and Knime are similar tools, and their capabilities largely overlap. Alteryx is more commercial, offering only a paid platform, while Knime also has a free, open-source option. Knime lacks some of Alteryx’s polish, but it offers more flexibility.

• Use Alteryx if you have more business analysts than engineers on your team and you need polished reports and dashboards.
• Use Knime if you’re on a budget and flexibility is more important to you than presentation.