Accelerate AI & ML projects using Databricks

Databricks and its role in Data Prep for AI solutions

Databricks is a buzzword in Data Science. It is so due to a lot of reasons. In order to work with massive amounts of data in petabytes or even more, Apache Spark is widely used. Apache Spark is an open-source, fast cluster computing system and a highly popular framework for big data analysis. This framework processes the data in parallel that helps to boost the performance. It is written in Scala, a high-level language, and supports APIs for Python, SQL, Java and R. Databricks is the implementation of Apache Spark on Azure. With fully managed Spark clusters, it is used to process large workloads of data and helps in data engineering, data exploring and visualizing data using Machine Learning.

The Databricks analytics platform is super flexible and extremely friendly for developers to use APIs like Python, R, etc. Suppose a data frame is created in Python with Azure Databricks, this data can be loaded into a temporary view using Scala, R or SQL with a pointer referring to this temporary view. This allows the developer to code in multiple languages in the same notebook. Not only Databricks supports multiple languages, it allows us to integrate with many Azure services like Blob Storage, Data Lake Store, SQL Database and BI tools like Power BI, Tableau, etc. Data professionals can use this platform for collaborating clusters and workplaces and thus increasing their productivity.

Databricks is quite similar to Jupyter Notebook, but with additional features that make it better than that. Databricks can run the same program or script on different computers. So instead of running our code only on one single computer, we can run it in different computers called clusters. With Databricks, it is easier to create clusters using the same code, same processor capacity but with shorter waiting time.

Another set of prominent features of Databricks are:

  1. Databricks Workspace – Databricks offers an interactive workspace that allows data scientists, data engineers and businesses to collaborate and work closely together on notebooks and dashboards.
  2. Databricks Runtime – Including Apache Spark, they are an additional set of components and updates that ensures improvements in terms of performance and security of big data workloads and analytics. Since it is a fully manages service, various resources like storage, virtual network, etc. are deployed to a locked resource group, which can be deployed in your own virtual network.
  3. Databricks File System (DBFS) – This is an abstraction layer on top of object storage. This allows you to mount storage objects like Azure Blob Storage that lets you access data as if they were on local file system.

Databricks is one of the best platforms to run Machine Learning and Artificial Intelligence. It lets data scientists to choose from a broad set of AI frameworks like Spark MLlib, TensorFlow, Pytorch, Caffee2 and others. Databricks’ Unified Analytics Platform powered by Apache Spark enables organizations to accelerate innovation by bringing together data and AI technologies, improving collaboration between data engineers and data scientists, making it simpler to prepare data, train models, and deploy them into production. The Unified Analytics Platform is a category of solutions that look at the entire lifecycle of AI – all the way from preparing the datasets, feature engineering, model development, training, to deployment of models into production. It truly unifies data with AI throughout the dev-to-production lifecycle.

Databricks has also developed MLflow, an open source toolkit for data scientists to manage the lifecycle of machine learning models. Machine Learning relies on a number of tools. For each stage involved in building a model, data scientists use at least half-a-dozen tools. Each stage requires extensive experimentation before settling for the right toolkit and framework. MLflow from Databricks is aimed at reducing the complexity through an abstraction layer that talks to a variety of tools and frameworks.  This toolkit can be effectively used by individual data scientists or even large teams involved in building machine learning models.

MLflow addresses three essential challenges in building and managing ML models like:

  1. Insight into the way each parameter and hyperparameter influence a model.
  2. A consistent way of performing experiments while evolving a model.
  3. Simplified model serving across multiple environment for inference.



Sindhu Shree