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Articles by Dash Desai
Run 3 useful LLM inference jobs in minutes with Snowflake Cortex
Getting started with AI on enterprise data can seem overwhelming, between getting familiar with LLMs, how to perform custom prompt engineering, and how to get a wide range of LLMs deployed/integrated to run multiple tests all while keeping that valuable enterprise data secure. Well, a lot of these complexities are being abstracted away for you in Snowflake Cortex.
Getting Started with Data Engineering and ML using Snowpark for Python
There is no shortage of great content here on Medium and QuickStart Guides which cover various aspects of working with Snowpark for Python contributed by Snowflakes as well as (Snowflake) Data Superheroes. ( Learn how you can become a Data Superhero!) In this new technical guide I published recently is all about getting your hands dirty as quickly as possible to learn what and how you can accomplish certain data engineering and ML tasks using Snowpark for Python.
Image Recognition in Snowflake using Snowpark Python, PyTorch, Streamlit and OpenAI
A technical guide for building image recognition applications in Snowflake. In this article, we will review how to build image recognition applications in Snowflake using Snowpark for Python, PyTorch, Streamlit and OpenAI’s DALL-E 2 — “a new AI system that can create realistic images and art from a description in natural language”.
Load Data from S3 to Snowflake and Use TensorFlow Model
Learn how to load data from S3 to Snowflake and serve a TensorFlow model in StreamSets Data Collector, a fast data ingestion engine, data pipeline for scoring on data flowing from S3 to Snowflake. Data and analytics are helping us become faster and smarter at staying healthy. Open data sets and analytics at cloud scale are key to unlocking the accuracy needed to make real impacts in the medical field.
Model Experiments, Tracking and Registration using MLflow on Databricks
By Dash Desai, Director of Platform and Technical Evangelism at StreamSets Learn how StreamSets, a modern data integration platform for DataOps, can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps. Data Acquisition And Preparation Machine learning models are only as good as the quality of data and the size of datasets used to train the models.
Model Experiments, Tracking, and Registration Using MLflow on Databricks and StreamSets
Learn how StreamSets, a modern data integration platform for DataOps, can help expedite operations at some of the most crucial stages of machine learning lifecycle and MLOps. Data Acquisition and Preparation Machine learning models are only as good as the quality of data and the size of datasets used to train the models.
StreamSets Transformer Extensibility: Spark and Machine Learning Part One
Apache Spark has been on the rise for the past few years, and it continues to dominate the landscape when it comes to in-memory and distributed computing, real-time analysis, and machine learning use cases. And with the recent release of StreamSets Transformer, a powerful tool for creating highly instrumented Apache Spark applications for modern ETL, you can quickly start leveraging all the benefits and power of Apache Spark with minimal operational and configuration overhead.
Preview and Snapshot Features in StreamSets Data Collector
Hello from your newly-appointed community champion and technical evangelist here at StreamSets! My name is Dash Desai and you will find me writing blog posts and cruising the community forums answering questions about StreamSets Data Collector as well as learning from community members. I will also be presenting at meetups and conferences so if you happen to be attending, please stop by and say hi.
Low-latency Inference Using Databricks ML in StreamSets
Save {{ articles[0].views | formatCount}} Views Join the DZone community and get the full member experience. Join For Free In my previous post, we looked at using TensorFlow models in dataflow pipelines to generate predictions and classifications in real-time. In this post, I will walk you through using Databricks ML models in StreamSets Data Collector for low-latency inference. In Machine Learning there are two major phases.
Scaling Data Collectors on Azure Kubernetes Service-StreamSets: Where DevOps Meets Data Integration
How to Simplify Apache Kafka. Get eBook. In this blog post, I will present a step-by-step guide on how to scale Data Collector instances on Azure Kubernetes Service (AKS) using provisioning agents which help automate the upgrading and scaling of resources on-demand, without having to stop the execution of pipeline jobs. AKS removes the complexity of implementing, installing, and maintaining Kubernetes in Azure and you only pay for the resources you consume.
Cloud Data Warehouses: StreamSets for Snowflake
How to Simplify Apache Kafka. Get eBook. Introduction Data warehouses are a critical component of modern data architecture in enterprises that leverage massive amounts of data to drive the quality of their products and services. A data warehouse is an OLAP (Online Analytical Processing) database that collects data from transactional databases such as Billing, CRM, ERP, etc. and provides a layer on top specifically for fast analytics, reporting, and data mining.
Executing ML Jobs in Azure Databricks From StreamSets
How to Simplify Apache Kafka. Get eBook. In my previous post, I demonstrated how to achieve low-latency inference using Databricks ML models in StreamSets. Now let's say you have a dataflow pipeline that is ingesting data, enriching it, and performing transformations, and based on certain condition(s), you'd like to (re)train the Databricks ML model.
Scaling Data Collectors on Azure Kubernetes Service
How to Simplify Apache Kafka. Get eBook. In this blog post, I will present a step-by-step guide on how to scale Data Collector instances on Azure Kubernetes Service (AKS) using provisioning agents—which help automate upgrading and scaling resources on-demand, without having to stop the execution of pipeline jobs. AKS removes the complexity of implementing, installing, and maintaining Kubernetes in Azure and you only pay for the resources you consume.
Real-Time Machine Learning With TensorFlow in Data Collector
The real value of a modern DataOps platform is realized only when business users and applications are able to access raw and aggregated data from a range of sources, and produce data-driven insights in a timely manner. And with Machine Learning (ML), analysts and data scientists can leverage historical data to help make better, data-driven business decisions-offline and in real-time using technologies such as TensorFlow.
Modernizing Hadoop Ingest: Beyond Flume and Sqoop
Apache Flume and Apache Sqoop were the tools of choice for ingesting data into Hadoop, but their development has slowed and more usable tools are now available. Flume's configuration file and Sqoop's command line pale beside modern tools for defining data flow pipelines. While these tools are great for smoothing out impedance mismatches and database dumps, they are complex to deploy and maintain, require custom coding/scripting, and are not enterprise-friendly.
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