Accelerating Machine Learning with the Databricks Lakehouse Platform

Accelerating Machine Learning with the Databricks Lakehouse Platform

Increasingly, many customers and companies are interested in Databricks. Why is this unicorn so valuable? What can their technology do for you?

The purpose of this blog post is to elaborate on this, explain why so many firms are implementing Databricks Lakehouse Platforms within their data estate, and outline several of the ML, AI, and data science, streaming, and analytics areas where Databricks can add value, as per the list of scenarios and associated value below:

Scenarios made possible by Databricks

Why Databricks Lakehouse Platform?

Many customers newer to the data science space don’t understand the depth of capabilities aggregated in the Databricks Lakehouse Platform and its importance as a technology. The Databricks Lakehouse Platform allows organizations to facilitate data engineering, analytics, BI, data science, and machine learning by providing the governance and performance capabilities of a data warehouse combined with the flexibility and machine learning support of data lakes.

The platform allows all modern data & analytics concepts of data engineering, machine learning, and SQL analytics while supporting multiple languages (Python, Scala, R, Java, and SQL) for easy, seamless, and improved collaborations.

Databricks environment is separated into three main workspaces as below based on different types of workloads used in organizations:

  • Databricks Data Science & Engineering
  • Databricks Machine Learning
  • Databricks SQL

All the workspaces provide a unified environment with enterprise capabilities such as provide role-based access control (RBAC), encryption, networking, automatic scaling, versioning, and more. It also supports popular compliances such as SOC 2 Type II, HIPAA, ISO 27001, GDPR, and more.

In this blog, the focus is on Databricks Datalakehouse Platform. It provides machine learning runtime (for all popular ML libraries including TensorFlow, PyTorch, and scikit-learn), collaborative notebooks, and Feature Store, and Managed MLflow to manage the full lifecycle of ML projects.

The data for easy, seamless integration of data in the Delta format, and enables analysis of this data in a team environment, with collaborative notebooks created for execution, and the full lifecycle of ML projects.

Apache Spark, mlFlow, and Delta Lake logos

What does Databricks do that is unique?

Databricks Lakehouse Platform uses open-source technologies Apache Spark, Delta Lake, and MLflow at its core. This enables an organization to access a rich set of talent pools and innovations. Apache Spark makes the management of large-scale distributed computing easier.  Delta Lake makes storing data cheap and scalable in the cloud data storage provided by the major cloud vendors. MLflow supports managing the end-to-end machine learning lifecycle at scale with responsible AI features.

Additionally, Databricks allows seamless experience and migration across clouds as it stores all assets in open-source format compared to proprietary format by other vendors.

These factors together mean that Databricks Lakehouse Platform offers a superior platform basis for most machine learning & AI teams. It allows seamless computing, collaboration, and support of the most common languages meaning algorithms can be easily built, and collaborated upon, features can be created, shared, stored, and leveraged again, while teams can add efficiency to their operations through modern applications of ML Flow and ML Ops best practice.

What value can it add?

Leveraging the Databricks Lakehouse Platform can add massive value to both data engineering workflows as well as ML Ops lifecycles & data science teams.

Data engineers benefit from having a single source of truth and a unified approach with Delta Lake to transform their data. A unified transformation layer with gold-silver-bronze tie-ins allows them to improve data quality, create requisite tests, and add structure to unstructured data, increasing its value and ultimate usability.

Data Scientists benefit from a unified, streamlined, collaborative process built upon a shared notebook environment. Good MLOps (Rehana to insert a link to Rajesh’s blog) can add efficiency to the team and make a data scientist even more able to scale and develop tools rapidly. We’ve seen massive acceleration here – one data scientist with the right Databricks implementation can do what a team used to 5-6 years ago.

Customer example: MLOps implementation for a major North American water bottling company

In a previous project, Neal Analytics worked with a major North American water bottling company to develop a machine learning model for spot bidding that could optimize shipping and distribution costs. The problem? While the model worked as a pilot, the bottling company had no formal process for deploying it into production.

To help the company overcome this challenge, Neal Analytics helped them implement MLOps processes and policies leveraging an Azure Databricks-based MLOps infrastructure. As a result, the water bottling company was able to integrate the ML-based spot bidding engine into their existing workflows and business processes.

Check out our customer story to learn more.

How can I Implement it?

It is easy to leverage the Databricks Lakehouse Platform for a host of different purposes. Neal has created a series of offers based on the different customer personas & states in the customer journey we see with Databricks:

  1. Getting started – Neal has created a Delta Lake in 30 Days offer to help get you started with a deployment of Databricks.
  2. Our Customer Analytics in 30 Days & associated accelerator enables rapid deployment of Databricks for a series of use cases focused on customer AI, demonstrating the value Databricks can bring within AI use cases.
  3. Many mature teams are interested in getting the most out of their data science resources. Our MLOps in 30 Days offering enables data scientists to be the most productive.

All these offers leverage Microsoft’s DPI 30 program and can be funded by them if your scenario qualifies. Click here to learn more!