Architecting Database Applications on Windows Azure

90% of application developed on premise or on cloud are likely to have a database of some nature. The database of choice could be very many ranging from blobs , tables, big data i.e No SQL to SQL Azure. In the last decade we have seen most database tend to relational in nature its only […]

Real-time Financial Stocks Analysis Architecture

In the prior 2 posts, the focus was more on using machine learning techniques like regression to predict gold buy / sell signals. While the models that we built ,give an idea on how to get to a final buy and sell signal for gold with the assumption data is clean and always available. Without […]

Azure Data Factory–DE glossed

Having worked on the Apache stack for sometime, I decided to look at Azure Big Data stack.  My starting point is data ingest.For most big data projects the journey starts out with data ingest, clean, transform and have it ready for analysis. Azure Data factory is MSFT Azure offering for cloud based data integration service […]

Parallelization of R code using Azure Infrastructure

Working on large data sets, exploring which machine learning algorithm fits the bill is a daunting task. Moreover these ML algorithm can run into hours and days in certain cases. There is always a need of having compute resources available on the fly. R in principle is single threaded by nature.  To support parallel constructs […]

LSTM–Digging Deep Part 1

Introduction LSTM (Long Short Term Memory) is gaining a lot of recognition in recent past. LSTM are an interesting type of deep learning network, they are used in some fairly complex problem domain such as language translation, automatic image captioning and text generation. LSTM are designed specifically for sequence prediction problem. This post starts of […]

LSTM for Stock Markets

LSTM Basics got us to a point of understanding simple LSTM. When it comes real life scenarios the picture gets more complicated a simple 1 layer LSTM just doesnt do the job. Usually multi layer LSTM are required where each layer does a part of the job then sends the output to next layer and […]

Blockchain and MDM Synergies

As the team at Vision Insight AI continue to build in the intelligent way of doing Data Management. The world around us has one harder topic to consider i.e. Blockchain. The current state of data is we are highly distributed by nature, for example banking, healthcare, transportation, energy, manufacturing, and other sectors, the trend is […]

Thoughts on Distributed Tensor Flow Execution

Introduction Having worked on DNN on a single machine with reasonable GPU compute can still take large network to train for days altogether. There is always the option to use TF on a cluster on cloud environment. I apparently wanted to start with distributing my workload across the GPU and CPU and then eventually move […]

Azure Data Share

Azure Data Share a new feature to share data between organizations. The regions are slightly limited to 3 as of now but will grow in the future. Under the hood it’s a PaaS based share service which expose selected data from Azure storage to the selected parties and has a couple of features thrown in […]

AI MDM Management

Background Master Data Management has been around for over a decade now. As we enter the new age where Data has become currency, most companies tend to spend a lot of there time bringing in data from various sources or systems. Having moved in the data to a common platform they relentlessly work from data […]