Machine learning (ML) is being incorporated into virtually all aspects of enterprise IT. ML speeds up data analytics, facilitates real-time data processing and decision making, and greatly enhances modeling. Microsoft Azure ML and Databricks both offer top-rated ML tools. But which is best for your company?
As usual, there are similarities and differences. In many cases, the choice boils down to the specific ML needs of the environment.
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Azure ML vs. Databricks: Key Features
Azure Machine Learning is designed to help data scientists and developers quickly build, deploy, and manage models via machine learning operations (MLOps), open-source interoperability, and integrated tools. It streamlines the deployment and management of thousands of models in 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 the detection of data drift, and it can trigger retraining to improve model performance. Azure ML also has features 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 Apache Spark’s distributed computing framework to make management of infrastructure easier. It uses a batch in-stream data processing engine for distribution across multiple nodes.
Databricks positions itself as a data lake more than a pure ML system, but it incorporates heavy duty ML capabilities. The emphasis is on use cases such as streaming, ETL, and data science-based analytics/ML. It can be used to handle raw unprocessed 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 back-end services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.
The latest version has added advanced data warehousing and data governance capabilities, Databricks Marketplace and Data Cleanrooms for collaborative data sharing, data engineering optimizations to automatically execute batch and streaming data pipelines, automatic cost optimization for ETL (extract, transform, load) operations, and ML life cycle improvements.
For those needing robust ELT, data science, and machine learning features within a data lake/data warehouse framework, Databricks is the winner. For those just wanting to add ML to existing applications, Azure ML wins.
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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 create accurate and automated ML models quickly for tabular, text, and image. 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 best for those used to Apache and open-source tools. It takes a data science approach using open-source and machine libraries, which may be challenging for some users. It can run Python, Spark Scholar, SQL, NC SQL, and other platforms, and it comes packaged with its own user interface as well as ways to connect to endpoints such as JDBC connectors. Some users, though, report that it can appear complex and is not user friendly, as it is aimed at a technical market and needs more manual input for cluster resizing clusters or configuration updates. There may be a steep learning curve for some.
There is a version that runs on Azure, but this does not seem like the ideal combination. Garter Peer Reviews score Databricks way ahead of Azure-Databricks in terms of data access and manipulation, optimization, performance, scalability, data preparation, ease of deployment, and support. In most cases, it is probably best to pick one or the other and not try to cobble them both together.
Azure ML wins in terms of overall ease of use.
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Azure ML vs. Databricks: Security
Azure ML offers data protection, access control, authentication, network security, and threat protection to identify unusual access locations, SQL injection attacks, and authentication attacks.
Further security 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 plenty of other security features. Both platforms do a good job of security, so there is no clear winner in this category. For Microsoft shops, Azure wins. Beyond that, it is a tie.
Azure ML vs. Databricks: Integration
Microsoft does a good job tying its various ecosystems together. Azure ML, Azure Synapse, and the rest of the Azure offerings are well integrated. That applies as well to Windows and other Microsoft offerings, including Power BI for analytics. It even does a decent job integrating Apache tools, although not as well as Databricks, which is built solidly on an Apache bedrock.
In comparison, Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. Databricks also supports any format of data including unstructured data, which gives it an edge in that area over Azure ML.
More recently, Databricks added open-source connectors for Go, Node.js, and Python to make it simpler to access from other applications. A Databricks SQL query federation feature offers the ability to query remote data sources including PostgreSQL, MySQL, AWS Redshift, and others without the need to first extract and load the data from the source systems.
Azure ML is the obvious winner here for Microsoft and Azure shops. Outside of that sphere, Databricks wins.
Azure ML vs. Databricks: Pricing
There is a great deal of difference in how these tools are priced. But speaking very generally, Databricks is priced at around $99 a month. There is also a free version. As storage is not included in its pricing, Databricks may work out cheaper for some users and not for others. It all depends on the way the storage is used and the frequency of use. Compute pricing for Databricks is also tiered and charged per unit of processing. That said, some users complain about how expensive it can be.
Azure ML is a little complex on pricing, too. There are different parameters included that add to cost beyond a general pay per use model. But in general, it looks like it is cheaper than Databricks overall.
Azure ML wins on price, although it isn’t possible to do a full comparison. Users are advised 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 turn out cheaper, but for most, Azure ML will probably come out ahead.
Choosing Between Azure ML and Databricks
Azure ML and Databricks are both excellent ML tools. Each has pros and cons, but it all comes down to usage patterns, data volumes, workloads, and data strategies.
Azure ML is more suited for those who want to build models and crunch lots of data through an ML engine. It is also good for developers who want to build ML features into applications.
Databricks does similar things, but has ML as one component in a bigger data lake suite that includes streaming, data warehousing, and ELT. As such, it should be viewed more as a broad data platform with wider scope than Azure ML. Users store data in managed object storage of their choice. The focus, then, is on the data lake and data processing.
Databricks wins for a technical audience. Azure ML can work well for that same audience but is also designed for a less tech-savvy user base. Databricks isn’t as easy to use, is said to have a steep learning curve, and may require more maintenance. But, it can address a wider set of data workloads and languages.
The choice largely comes down to user preference and needs. Those familiar with Apache Spark will tend to gravitate toward Databricks. Those comfortable with Azure and Microsoft tools will be well suited to use Azure ML.
But, there may be occasional cases where Azure ML doesn’t provide all of the functions data scientists need, even if they are operating on Azure/Windows. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and other languages makes it attractive to developers in those camps.
Azure wins for those that just need to augment existing infrastructure and applications with ML functionality. Databricks wins for those favoring open-source technologies and who are looking for a broader data lake/data warehouse and data management platform.
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