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Janardhanan PS's Blog (13)

Machine Learning at the Edge

Edge computing moves workloads from  centralized locations  to remote locations and it can provide faster response from AI applications.  Edge computing devices are getting deployed increasingly for monitoring and control of real world processes like people tracking, vehicle recognition, pollution monitoring etc.  The data collected at the devices gets transported to centralized cloud servers over data pipelines and are used to train machine learning models. Training models needs lot…

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Added by Janardhanan PS on March 23, 2020 at 12:18am — No Comments

ACID to BASE Transformation

In chemistry, we have strong and weak ACIDs. There exists strong and weak BASEs also. In Relational Database Management Systems (RDBMS), ACID stands for Atomicity, Consistency, Isolation and Durability. The volume of ACID transactions handled by RDBMS has undergone a big data transition with the evolution of mobile devices. SQL stands for Structered Query Language, but commonly refers to traditional RDBMS. RDBMS is not designed to be distributed and optimized for space, not for speed of access.… Continue

Added by Janardhanan PS on March 16, 2020 at 9:16pm — No Comments

What are Data Pipelines ?

If you have learned temporal parallelism used to speed up CPU execution, you came across instruction pipelines aka pipeline processing. In pipeline processing, you will have many instructions in different stages of execution. The term "Data Pipeline" is a misnomer representing a high bandwidth communication channel used for data transportation between a source system and a destination. In certain cases the destination is called a sink. Pipelines by definition allow flow of a fluid…

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Added by Janardhanan PS on March 14, 2020 at 6:51am — No Comments

Overcoming Challenges in Implementation of Successful AI products

IT professionals acquire AI expertise in a short time by attending couple of online courses. Once the courses are completed successfully, these new gen AI experts look for opportunities to hit problems with the newly acquired AI hammer. It is a challenge to identify problems that need AI for a solution. Majority of the problems seen as an AI problem can be solved easily by traditional statistical methods. But the IT child with new AI hammer in hand thinks that all problems in this…

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Added by Janardhanan PS on March 9, 2020 at 7:00pm — No Comments

What are Data Lakes ?

Some of you are highly organized, disciplined, and keep all your belongings arranged in an ordered manner. You can easily remember the location of all these items and will be able to fetch them in a short time. But there are people who are highly unorganized and keeps all their belongings in a random manner and will have no clue on where to look for an item when they really need it. This results in a search operation on all storage spaces in the house when they want to fetch an item. There is… Continue

Added by Janardhanan PS on March 2, 2020 at 9:37pm — No Comments

How you can explain Machine Learning models ?

Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most of the cases, users do not understand how these models make predictions. The lack of understanding makes it difficult for policy makers to justify their decisions. Most of the ML models are black boxes that do not explain on its own why it reached a specific recommendation or a decision. This forces the users to say…

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Added by Janardhanan PS on February 27, 2020 at 7:00pm — No Comments

Data Tanks for Incremental Training of Machine Learning Models

You are familiar with the term data lake. A data lake is a repository used to store unlimited volume of data. These days, most of the cloud service providers allow us to host scalable data lakes for storing data as it arrives. For using these data lakes, it is not required to structure the data and we can run different types of applications on it. Usually they are applications for big data analytics and machine learning. These applications need entire data to be present in one data lake and… Continue

Added by Janardhanan PS on February 24, 2020 at 7:00pm — No Comments

Ability to generalize - A measure of intelligence ?

Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model's ability to make accurate predictions from never before seen data. Well generalized models possess intelligence to work on data from new scenarios. This is true for human intelligence also. Children start learning from examples and initially they fail to respond properly to unforeseen situations. Gradually they acquire generalization skills to respond to all… Continue

Added by Janardhanan PS on February 16, 2020 at 9:02pm — No Comments

Overcoming surprises faced by Programmers migrating to Machine learning projects

Traditional programmers hit a stone-wall when they start working on Machine Learning (ML) development projects. They were used to coding based on design derived from requirements, logic/rules and when they move into a new paradigm in which rules are automatically generated from data, they are surprised. In ML paradigm, we do not find any output code and we see an output in the form of a binary object called model that goes into production. You may be wondering without measuring lines of code,… Continue

Added by Janardhanan PS on February 10, 2020 at 7:14pm — No Comments

Human Learning and Machine Learning - How they differ ?

Is there any similarity in learning process between humans and machines. Human learning process varies from person to person. Once a learning process is set into the minds of people, it is difficult to change it. But, in Machine Learning (ML), it is easy to change the learning method by selecting a different algorithm. In ML, we have well defined processes to understand and estimate the accuracy in learning. Estimation of human learning is usually done through examinations and it cannot be… Continue

Added by Janardhanan PS on February 4, 2020 at 8:43pm — No Comments

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

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Added by Janardhanan PS on January 20, 2020 at 11:30pm — 1 Comment

Importance of Hyper-parameters in Model development

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…

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Added by Janardhanan PS on January 16, 2020 at 12:00am — 1 Comment

Overcoming Barriers in Machine Learning adoption in corporate world

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit…

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Added by Janardhanan PS on January 8, 2020 at 7:00pm — No Comments

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