Many startups would like to incorporate a machine learning component into their product(s). Most of these products are unique in terms of the business, the data that is required to train the machine learning models, and the data that can be collected. One of the main challenges that these startups have is the availability of data specific to their business problem. Unfortunately, the quality of the machine learning algorithms is dependent on the quality of the domain specific data that is used to train these models. Generic data sets are not useful for the unique problems that these startups are solving. As a result, they cannot rollout a feature involving machine learning until they can collect enough data. On the other hand, customers ask for the product feature before their usage can generate the required data. In such a situation, one needs to rollout a machine learning solution incrementally. For this to happen, there must be a synergy between the data and the algorithms that have the ability to process this data. To enforce this synergy, we propose a computational model that we refer to as “Data Fingerprinting”. Continue reading Data Fingerprinting to enable Incremental Improvement in Machine Learning Complexity
Our mission is to compare the node.js frameworks on productivity.
In one of my previous blogs I have benchmark the various node.js frameworks performance against native http call and native mongodb driver and native combination was clear winner in term of performance.
so, Why not use only native http and native mongodb driver. well one of the the key aspect and usp of node.js frameworks is that they provide lot of abstractions and as a developer you don’t have to write boiler plate , repetitive code . so lets see what our research has come up with against this concept. Continue reading Comparing productivity of node.js frameworks
Our mission is to compare the node.js frameworks on performance (completed no of requests per second).
Node.js performance tests were performed on the Ubuntu subsystem(2 core , 2 GB RAM) on a VM provisioned from Digital Ocean. The tests only utilize the most basic capabilities of the frameworks in question, therefore the main goal was to show the relative overhead these frameworks add to the handling of a request. This is not a test of the absolute performance as this will vary greatly depending on the environment and network conditions. This test also doesn’t cover the utility each framework provides and how this enables complex applications to be built with them. Continue reading Comparing performance of node.js frameworks