Azure ML vs. Databricks: Comparing Machine Learning |  e-WEEK

Azure ML vs. Databricks: Comparing Machine Learning | e-WEEK

Machine learning (ML) is embedded in virtually every aspect of enterprise computing. ML speeds up data analysis, facilitates real-time data processing and decision-making, and dramatically improves modeling. Microsoft Azure ML and Databricks both offer top-notch ML tools. But what is best for your business?

As usual, there are similarities and differences. In many cases, the choice comes down to the specific ML needs of the environment.

See also: Best Machine Learning Platforms

Azure ML vs. Databricks: Key Features

Azure Machine Learning is designed to help data scientists and developers quickly build, deploy, and manage models through machine learning operations (MLOps), open source interoperability, and built-in tools. It streamlines the deployment and management of thousands of models across multiple environments for batch and real-time predictions.

Repeatable pipelines can be used to automate workflows for continuous integration and continuous delivery (CI/CD). Developers can use cross-workspace collaboration using registries. It also offers continuous monitoring of model performance metrics and data drift detection, and it can trigger recycling to improve model performance. Azure ML also has capabilities to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis.

Like Azure ML, Databricks is cloud-based. Its management layer is built around the distributed computing framework of Apache Spark to facilitate infrastructure management. It uses a batch streaming data processing engine for distribution across multiple nodes.

Databricks positions itself more as a data lake than a pure ML system, but it incorporates some very resilient ML features. The focus is on use cases such as streaming, ETL, and data science-based analytics/ML. It can be used to handle unprocessed raw data in large volumes.

Databricks is delivered as software as a service (SaaS) and can run on all major cloud platforms; there is even an Azure Databricks combo available. There is a data plane as well as a control plane for core services that provide instant computing. Its query engine is supposed to deliver high performance through a caching layer. Databricks provides storage by running on AWS S3, Azure Blob Storage, and Google Cloud Storage.

The latest release added advanced data warehousing and governance features, Databricks Marketplace and Data Cleanrooms for collaborative data sharing, data engineering optimizations to automatically run batch and continuous data pipelines, automatic cost optimization for ETL operations (extract, transform, load) and ML lifecycle improvements.

For those who need robust ELT, data science, and machine learning functionality in a data lake/data warehouse framework, Databricks is the winner. For those who just want to add ML to existing applications, Azure ML wins.

See also: Data Mining Techniques

Azure ML vs. Databricks: support and ease of use

Azure ML enables users to collaborate with Jupyter Notebooks using built-in support for open source frameworks and libraries. Users can quickly create accurate, automated ML models for tables, text, and images. And those familiar with SQL and Azure will find it particularly easy to use. But in general, the platform is designed to simplify ML processes.

Databricks, on the other hand, is great for those familiar with Apache and open source tools. It takes a data science approach using open-source and machine libraries, which may be difficult for some users. It can run Python, Spark Scholar, SQL, NC SQL, and other platforms, and it comes with its own user interface as well as ways to connect to endpoints like JDBC connectors. Some users, however, report that it may seem complex and not user-friendly, as it is aimed at a technical market and requires more manual input for cluster resizing or configuration updates. There can be a steep learning curve for some.

There is a version that works on Azure, but that doesn’t seem like the ideal combination. Garter peer reviews place Databricks well ahead of Azure-Databricks in data access and manipulation, optimization, performance, scalability, data readiness, ease of deployment, and support . In most cases, it’s probably best to choose one or the other and not try to reconcile them.

Azure ML wins in terms of overall ease of use.

See also: Top AI software

Azure ML vs. Databricks: Security

Azure ML provides data protection, access control, authentication, network security, and threat protection to identify unusual access locations, SQL injection attacks, and authentication attacks.

Other safety features include component isolation limits. Developers can use it in a managed and secure environment with cloud CPUs (central processing units), GPUs (graphics processing units), and supercomputing clusters while enjoying continuous monitoring with Azure Security Center.

Databricks provides role-based access control (RBAC), automatic encryption, and many other security features. Both platforms do a good job of security, so there’s no clear winner in this category. For Microsoft Stores, Azure wins. Beyond that, it’s a tie.

Azure ML vs. Databricks: Integration

Microsoft does a good job of linking its different ecosystems. Azure ML, Azure Synapse, and the rest of Azure offerings are well integrated. This also applies to Windows and other Microsoft offerings, including Power BI for analytics. It even does a decent job of integrating Apache tools, but not as well as Databricks, which is solidly built on an Apache bedrock.

In comparison, Databricks requires third-party tools and application programming interface (API) configurations to integrate data governance and lineage features. Databricks also supports all data formats, including unstructured data, giving it an edge in this area over Azure ML.

More recently, Databricks added open source connectors for Go, Node.js, and Python to simplify access from other applications. A Databricks SQL query federation feature provides the ability to query remote data sources, including PostgreSQL, MySQL, AWS Redshift, and others, without the need to first extract and load data from source systems.

Azure ML is the clear winner here for Microsoft and Azure Stores. Outside of this sphere, Databricks wins.

Azure ML vs. Databricks: Pricing

There is a big difference in the price of these tools. But very generally, Databricks costs around $99 per month. There is also a free version. Since storage is not included in its price, Databricks may be cheaper for some users and not for others. It all depends on how the storage is used and how often it is used. Compute pricing for Databricks is also tiered and billed per processing unit. That said, some users complain about its cost.

Azure ML is also a bit complex when it comes to pricing. There are various parameters included that add to the cost beyond a general pay-as-you-go model. But in general it seems to be cheaper than Databricks as a whole.

Azure ML wins on price, although a full comparison isn’t possible. Users are encouraged to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements. For some users, Databricks may be cheaper, but for most, Azure ML will likely come out on top.

Choosing between Azure ML and Databricks

Both Azure ML and Databricks are great ML tools. Each has its pros and cons, but it all depends on usage patterns, data volumes, workloads, and data strategies.

Azure ML is best suited for those who want to build models and analyze lots of data through an ML engine. It is also suitable for developers who want to integrate ML functionality into applications.

Databricks does similar things, but has ML as a component in a larger data lake suite that includes streaming, data warehousing, and ELT. As such, it should be considered more of an extended data platform with a broader scope than Azure ML. Users store data in the managed object storage of their choice. The focus is therefore on the data lake and data processing.

Databricks wins for a technical audience. Azure ML may work well for this same audience, but is also designed for a less tech-savvy user base. Databricks is not as easy to use, it is said to have a steep learning curve and may require more maintenance. However, it can handle a wider set of data and language workloads.

The choice largely depends on the preferences and needs of the user. Those familiar with Apache Spark will tend to look to Databricks. Those comfortable with Azure and Microsoft tools will be well suited to using Azure ML.

However, Azure ML may not provide all the functions that data scientists need, even if they are running on Azure/Windows. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and other languages ​​makes it appealing to developers in these camps.

Azure wins for those who just need to augment existing infrastructure and applications with ML functionality. Databricks wins for those who favor open source technologies and are looking for a larger data lake/data warehouse and data management platform.

See also: Main data mining tools

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