There is an AI dilemma where despite the allure of artificial intelligence, most enterprises are struggling to succeed with AI. Databricks, a leader in unified analytics seeks to solve that with the creation of Unified Analytics.
Data is the key to AI, but data and AI sit in technology and organizational silos, which is why a majority of organizations say that data-related challenges are the most common obstacle when moving AI projects to production.
Databricks’ new capabilities unify data and AI teams and technologies. They have prouduct for developing an end-to-end machine learning workflow, called MLflow. To simplify distributed machine learning, there is Databricks Runtime for ML. And to complete their suite of solutions, they have Databricks Delta for data reliability and performance at scale.
Go with the Flow
When it comes to building a web or mobile application, organizations know what to do because there are toolkits, workflows, and reference architectures that are available. Organizations are forced to piece together point solutions and secure highly specialized skills to achieve AI, because there is no framework for machine learning
To dramatically simplify the machine learning workflow, there is now MLflow, an open source, cross-cloud framework. Enterprises can do things like execute and compare hundreds of parallel experiments, package their code for reproducible runs, leverage any hardware or software platform, or deploy models to production on a variety of serving platforms using MLflow.
Simplifying and Enabling Distributed Deep Learning
With the use of natural language processing, image classification and object detection, Deep Learning continues to grow in popularity. Increased data volumes enable organizations to build better models, as a result of that, with the data complexity requirements increasing training time. Their solution eliminates this complexity with pre-configured environments that are tightly integrated with the most popular machine learning frameworks.
Runtime for ML address the need to scale deep learning. They do this through GPU support for both AWS and Microsoft Azure so that data scientists can feed data sets to models, evaluate, and deploy on one unified engine.
To Simplify Data Engineering
Right now, data engineers are currently struggling to simplify data management and provide clean data, hindering the success of AI initiatives. Currently, big data architectures are being built using a variety of systems, which increases cost and operational complexity. It can take organizations as much as seven months (or more) to bring AI projects to a close, with 50% of the time spent on data preparation.
With Databricks Delta, hundreds of applications can now reliably upload, query, and update data at massive scale, with lower costs, with their Unified Analytics Platform. Organizations are no longer required to make a tradeoff between storage system properties or spend their resources moving data across systems. The ability to simplify data engineering by providing high performance at scale is now there because of data reliability through transactional integrity and the low latency of streaming systems. All this ultimately makes data sets ready for machine learning.