In a recent post we had listed out analysis done and conclusions made when asked by a customer to convert their existing API to GraphQL. While exploring GraphQL with .NET framework we realized there is a large gap in examples and demos on using GraphQL with full scale .NET framework and EntityFramework. We took some […]
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Why we advised our customer against GraphQL
GraphQL has captured a lot of developer interest over the last year or so with many production level systems making the switch from REST type APIs to GraphQL. When one of our existing customers asked us to decide if we should switch their existing APIs over to GraphQL, the team was pretty excited to finally […]
Extracting Text from Images:- Google a notch better than Azure and AWS!
Extracting text from images has been worked on for many years now and finds applications in many domains like Banking , Legal, Healthcare, education and entertainment! With the advent of machine learning, text extraction from images is being offered as a Cognitive API by many AI/ML providers like AWS Rekognition, Azure Computer Vision,and Google CloudVision […]
Cognitive Image Analysis:- Azure and Google come out winners!
Image Analysis is defined in wikipedia as “…the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques” With the advent of machine learning, Image Analysis is being offered as a Cognitive API offering by many AI/ML providers like AWS Recognition , Azure Computer Vision,and Google CloudVision . […]
Introducing the Cognitive API Integrator
We are launching the Cognitive API Integrator today for developers, Engineering Managers, CIOs or anyone who needs to choose a Cognitive Service provider to build out a business service. Our Cognitive API Integrator aggregates cognitive services across major providers (currently Microsoft Azure, Amazon Web Services & Google Cloud) and provides all the results against a […]
ML Series – #7:- Preparing your training data
In the last article of this series we talked about generation of a test set and also the different ways in which we can generate it (i.e. Random sampling, Stratified sampling). Once we have separated our dataset into training set and test set we will not look at our test set until we have finished […]
ML Series – #0:- AI, ML, Auto-ML, Cognitive Services and all that Jazz!!!
We live in a world of buzzwords!! Media, news, professional and self opinioned blogs, articles and magazines don’t really help when it comes to untangling the mesh of words that come with any new technological advancement! The latest buzzwords in the happening “IT” world today are AI, ML, Deep Learning, Cognitive Services, Auto-ML and the […]
ML Series–#6:- Is Random Sampling good enough?
Picking up on the last article where we talked about the first few steps we take when exploring our data, the last step i.e Data cleaning is probably the most time and resource intensive step. One of the first things you do when presented with a data sample (probably even before the Explore and Correlate […]
ML Series – #5:- The first few baby steps to an ML network!
Continuing with our ML series , it is finally time to load up some data and start having some fun! Now that we are familiar with Python, pandas and matplotlib we know that there are a set of basic commands that we can use to get through the first few steps. We created a mind […]
ML Series – #4:- Our Learning Path for Python!
Now that we are done with the basics of Machine Learning, what it is, how many types , why is data oh-so important ,its time to get our hands dirty!! When it comes to programming there really is no other way to learn other than …by…coding We were quite new to the world Python when […]