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Introduction to MongoDB

Posted on February 17, 2021February 17, 2021 by Dexlab

MongoDB is a document based database program which was developed by MongoDB Inc. and is licensed under server side public license (SSPL). It can be used across platforms and is a non-relational database also known as NoSQL, where NoSQL means that the data is not stored in the conventional tabular format and is used for unstructured data as compared to SQL and that is the major difference between NoSQL and SQL.
MongoDB stores document in JSON or BSON format. JSON also known as JavaScript Object notation is a format where data is stored in a key value pair or array format which is readable for a normal human being whereas BSON is nothing but the JSON file encoded in the binary format which is quite hard for a human being to understand.
Structure of MongoDB which uses a query language MQL(Mongodb query language):-
Databases:- Databases is a group of collections.
Collections:- Collection is a group fields.
Fields:- Fields are nothing but key value pairs
Just for an example look at the image given below:-

Here I am using MongoDB Compass a tool to connect to Atlas which is a cloud based platform which can help us write our queries and start performing all sort of data extraction and deployment techniques. You can download MongoDB Compass via the given link https://www.mongodb.com/try/download/compass

In the above image in the red box we have our databases and if we click on the “sample_training” database we will see a list of collections similar to the tables in sql.

Now lets write our first query and see what data in “companies” collection looks like but before that select the “companies” collection.

Now in our filter cell we can write the following query:-

In the above query “name” and “category_code” are the key values also known as fields and “Wetpaint” and “web” are the pair values on the basis of which we want to filter the data.
What is cluster and how to create it on Atlas?
MongoDB cluster also know as sharded cluster is created where each collection is divided into shards (small portions of the original data) which is a replica set of the original collection. In case you want to use Atlas there is an unpaid version available with approximately 512 mb space which is free to use. There is a pre-existing cluster in MongoDB named Sandbox , which currently I am using and you can use it too by following the given steps:-
1. Create a free account or sign in using your Google account on
https://www.mongodb.com/cloud/atlas/lp/try2-in?utm_source=google&utm_campaign=gs_apac_india_search_brand_atlas_desktop&utm_term=mongodb%20atlas&utm_medium=cpc_paid_search&utm_ad=e&utm_ad_campaign_id=6501677905&gclid=CjwKCAiAr6-ABhAfEiwADO4sfaMDS6YRyBKaciG97RoCgBimOEq9jU2E5N4Jc4ErkuJXYcVpPd47-xoCkL8QAvD_BwE
2. Click on “Create an Organization”.
3. Write the organization name “MDBU”.
4. Click on “Create Organization”.
5. Click on “New Project”.
6. Name your project M001 and click “Next”.
7. Click on “Build a Cluster”.
8. Click on “Create a Cluster” an option under which free is written.
9. Click on the region closest to you and at the bottom change the name of the cluster to “Sandbox”.
10. Now click on connect and click on “Allow access from anywhere”.
11. Create a Database User and then click on “Create Database User”.
username: m001-student
password: m001-mongodb-basics
12. Click on “Close” and now load your sample as given below :

Loading may take a while….
13. Click on collections once the sample is loaded and now you can start using the filter option in a similar way as in MongoDB Compass
In my next blog I’ll be sharing with you how to connect Atlas with MongoDB Compass and we will also learn few ways in which we can write query using MQL.

So, with that we come to the end of the discussion on the MongoDB. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

MongoDB Basics Part-I

Posted on February 12, 2021February 17, 2021 by Dexlab

In this particular blog we will discuss about few of the basic functions of MQL (MongoDB Query Language) and we will also see how to use them? We will be using MongoDB Compass shell (MongoSH Beta) which is available in the latest version of MongoDB Compass.

Connect your Atlas cluster to your MongoDB Compass to get started. Latest version of MongoDB Compass will have this shell, so if you don’t find this shell then please install the latest version for this to work.

Now lets start with the functions.

find() :- You need this function for data extraction in the shell.

In the shell we need to first write the “use database name” code to access the database then use .find() to extract data which has name “Wetpaint”

For the above query we get the following result:-

The above result brings us to another function .pretty() .

2. pretty() :- this function helps us see the result more clearly.

Try it yourself to compare the results.

3. count() :- Now lets see how many entries we have by the company name “Wetpaint”.

So we have only one document.

4. Comparison operators :-

“$eq” : Equal to

“$neq”: Not equal to

“$gt”: Greater than

“$gte”: Greater than equal to

“$lt”: Less than

“$lte”: Less than equal to

Lets see how this works.

5. findOne() :- To get a single document from a collection we use this function.

6. insert() :- This is used to insert documents in a collection.

Now lets check if we have been able to insert this document or not.

Notice that a unique id has been added to the document by default. The given id has to be unique or else there will be an error. To provide a user defined id use “_id”.

So, with that we come to the end of the discussion on the MongoDB. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

ARIMA (Auto-Regressive Integrated Moving Average)

Posted on January 27, 2021February 13, 2021 by Dexlab

This is another blog added to the series of time series forecasting. In this particular blog I will be discussing about the basic concepts of ARIMA model.

So what is ARIMA?

ARIMA also known as Autoregressive Integrated Moving Average is a time series forecasting model that helps us predict the future values on the basis of the past values. This model predicts the future values on the basis of the data’s own lags and its lagged errors.

When a data does not reflect any seasonal changes and plus it does not have a pattern of random white noise or residual then an ARIMA model can be used for forecasting.

There are three parameters attributed to an ARIMA model p, q and d :-

p :- corresponds to the autoregressive part

q:- corresponds to the moving average part.

d:- corresponds to number of differencing required to make the data stationary.

In our previous blog we have already discussed in detail what is p and q but what we haven’t discussed is what is d and what is the meaning of differencing (a term missing in ARMA model).

Since AR is a linear regression model and works best when the independent variables are not correlated, differencing can be used to make the model stationary which is subtracting the previous value from the current value so that the prediction of any further values can be stabilized . In case the model is already stationary the value of d=0. Therefore “differencing is the minimum number of deductions required to make the model stationary”. The order of d depends on exactly when your model becomes stationary i.e. in case the autocorrelation is positive over 10 lags then we can do further differencing otherwise in case autocorrelation is very negative at the first lag then we have an over-differenced series.

The formula for the ARIMA model would be:-

To check if ARIMA model is suited for our dataset i.e. to check the stationary of the data we will apply Dickey Fuller test and depending on the results we will using differencing.

In my next blog I will be discussing about how to perform time series forecasting using ARIMA model manually and what is Dickey Fuller test and how to apply that, so just keep on following us for more.

So, with that we come to the end of the discussion on the ARIMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

ARMA- Time Series Analysis Part 4

Posted on January 25, 2021February 13, 2021 by Dexlab

ARMA(p,q) model in time series forecasting is a combination of Autoregressive Process also known as AR Process and Moving Average (MA) Process where p corresponds to the autoregressive part and q corresponds to the moving average part.

Autoregressive Process (AR) :- When the value of Y_t in a time series data is regressed over its own past value then it is called an autoregressive process where p is the order of lag into consideration.

Where,

Y_t = observation which we need to find out.

α₁= parameter of an autoregressive model

Y_t-1= observation in the previous period

u_t= error term

The equation above follows the first order of autoregressive process or AR(1) and the value of p is 1. Hence the value of Y_t in the period ‘t’ depends upon its previous year value and a random term.

Moving Average (MA) Process :- When the value of Y_t of order q in a time series data depends on the weighted sum of current and the q recent errors i.e. a linear combination of error terms then it is called a moving average process which can be written as :-

y_t = observation which we need to find out

α= constant term

β_ut-q= error over the period q .

ARMA (Autoregressive Moving Average) Process :-

The above equation shows that value of Y in time period ‘t’ can be derived by taking into consideration the order of lag p which in the above case is 1 i.e. previous year’s observation and the weighted average of the error term over a period of time q which in case of the above equation is 1.

How to decide the value of p and q?

Two of the most important methods to obtain the best possible values of p and q are ACF and PACF plots.

ACF (Auto-correlation function) :- This function calculates the auto-correlation of the complete data on the basis of lagged values which when plotted helps us choose the value of q that is to be considered to find the value of Y_t. In simple words how many years residual can help us predict the value of Y_t can obtained with the help of ACF, if the value of correlation is above a certain point then that amount of lagged values can be used to predict Y_t.

Using the stock price of tesla between the years 2012 and 2017 we can use the .acf() method in python to obtain the value of p.

.DataReader() method is used to extract the data from web.

The above graph shows that beyond the lag 350 the correlation moved towards 0 and then negative.

PACF (Partial auto-correlation function) :- Pacf helps find the direct effect of the past lag by removing the residual effect of the lags in between. Pacf helps in obtaining the value of AR where as acf helps in obtaining the value of MA i.e. q. Both the methods together can be use find the optimum value of p and q in a time series data set.

Lets check out how to apply pacf in python.

As you can see in the above graph after the second lag the line moved within the confidence band therefore the value of p will be 2.

So, with that we come to the end of the discussion on the ARMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Autocorrelation- Time Series – Part 3

Posted on January 22, 2021February 13, 2021 by Dexlab

Autocorrelation is a special case of correlation. It refers to the relationship between successive values of the same variables .For example if an individual with a consumption pattern:-

spends too much in period 1 then he will try to compensate that in period 2 by spending less than usual. This would mean that Ut is correlated with Ut+1 . If it is plotted the graph will appear as follows :

Positive Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the upward direction or when the error of the time t-1 carries over into a positive error in the following period it is called a positive autocorrelation.
Negative Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the downward direction or when the error of the time t-1 carries over into a negative error in the following period it is called a negative autocorrelation.

Now there are two ways of detecting the presence of autocorrelation
By plotting a scatter plot of the estimated residual (ei) against one another i.e. present value of residuals are plotted against its own past value.

If most of the points fall in the 1st and the 3rd quadrants , autocorrelation will be positive since the products are positive.

If most of the points fall in the 2nd and 4th quadrant , the autocorrelation will be negative, because the products are negative.
By plotting ei against time : The successive values of ei are plotted against time would indicate the possible presence of autocorrelation .If e’s in successive time show a regular time pattern, then there is autocorrelation in the function. The autocorrelation is said to be negative if successive values of ei changes sign frequently.
First Order of Autocorrelation (AR-1)
When t-1 time period’s error affects the error of time period t (current time period), then it is called first order of autocorrelation.
AR-1 coefficient p takes values between +1 and -1
The size of this coefficient p determines the strength of autocorrelation.
A positive value of p indicates a positive autocorrelation.
A negative value of p indicates a negative autocorrelation
In case if p = 0, then this indicates there is no autocorrelation.
To explain the error term in any particular period t, we use the following formula:-

Where Vt= a random term which fulfills all the usual assumptions of OLS
How to find the value of p?

One can estimate the value of ρ by applying the following formula :-

Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Time Series Analysis Part I

Posted on January 18, 2021January 18, 2021 by Dexlab

A time series is a sequence of numerical data in which each item is associated with a particular instant in time. Many sets of data appear as time series: a monthly sequence of the quantity of goods shipped from a factory, a weekly series of the number of road accidents, daily rainfall amounts, hourly observations made on the yield of a chemical process, and so on. Examples of time series abound in such fields as economics, business, engineering, the natural sciences (especially geophysics and meteorology), and the social sciences.

Univariate time series analysis- When we have a single sequence of data observed over time then it is called univariate time series analysis.
Multivariate time series analysis – When we have several sets of data for the same sequence of time periods to observe then it is called multivariate time series analysis.

The data used in time series analysis is a random variable (Yt) where t is denoted as time and such a collection of random variables ordered in time is called random or stochastic process.

Stationary: A time series is said to be stationary when all the moments of its probability distribution i.e. mean, variance , covariance etc. are invariant over time. It becomes quite easy forecast data in this kind of situation as the hidden patterns are recognizable which make predictions easy.

Non-stationary: A non-stationary time series will have a time varying mean or time varying variance or both, which makes it impossible to generalize the time series over other time periods.

Non stationary processes can further be explained with the help of a term called Random walk models. This term or theory usually is used in stock market which assumes that stock prices are independent of each other over time. Now there are two types of random walks:
Random walk with drift : When the observation that is to be predicted at a time ‘t’ is equal to last period’s value plus a constant or a drift (α) and the residual term (ε). It can be written as
Yt= α + Yt-1 + εt
The equation shows that Yt drifts upwards or downwards depending upon α being positive or negative and the mean and the variance also increases over time.
Random walk without drift: The random walk without a drift model observes that the values to be predicted at time ‘t’ is equal to last past period’s value plus a random shock.
Yt= Yt-1 + εt
Consider that the effect in one unit shock then the process started at some time 0 with a value of Y0
When t=1
Y1= Y0 + ε1
When t=2
Y2= Y1+ ε2= Y0 + ε1+ ε2
In general,
Yt= Y0+∑ εt
In this case as t increases the variance increases indefinitely whereas the mean value of Y is equal to its initial or starting value. Therefore the random walk model without drift is a non-stationary process.

So, with that we come to the end of the discussion on the Time Series. Hopefully it helped you understand time Series, for more information you can also watch the video tutorial attached down this blog. DexLab Analytics offers machine learning courses in delhi. To keep on learning more, follow DexLab Analytics blog.

5 Ways Artificial Intelligence Will Impact Our Future

Posted on November 20, 2020November 20, 2020 by Dexlab

Artificial Intelligence, or, its more popular acronym AI is no longer a term to be read about in a sci-fi book, it is a reality that is reshaping the world by introducing us to virtual assistants, helping us be more secure by enabling us with futuristic measures. The evolution of AI has been pretty consistent and as we are busy navigating through a pandemic-ridden path towards the future, adapting to the “new normal”, and becoming increasingly reliant on technology, AI assumes a greater significance.

The AI applications which are already being implemented has resulted in a big shift, causing an apprehension that the adoption of AI technology on a larger scale would eventually lead to job cuts, whereas in reality, it would lead to the creation of new jobs across industries. Adoption of AI technology would push the demand for a workforce that is highly skilled, enrolling in an artificial intelligence course in delhi could be a timely decision.

Now that we are about to reach the end of 2020, let us take a look at the possible impacts of AI in the future.

AI will create more jobs

Yes, contrary to the popular apprehension AI would end up creating jobs in the future. However, the adoption of AI to automate tasks means yes, there would be a shift, and a job that does not need special skills will be handled by AI powered tools. Jobs that could be done without error, completed faster, with a higher level of efficiency, in short better than humans could be performed by robots. However, with that being said there would be more specialized job roles, remember AI technology is about the simulation of human intelligence, it is not the intelligence, so there would be humans in charge of carrying out the AI operated areas to monitor the work. Not just that but for developing smarter AI application and implementation there should be a skilled workforce ready, a report by World Economic Forum is indicative of that. From design to maintenance, AI specialists would be in high demand especially the developers. The fourth industrial revolution is here, industries are gearing up to build AI infrastructure, it is time to smell the coffee as by the end of 2022 there will be millions of AI jobs waiting for the right candidates.

Dangerous jobs will be handled by robots

In the future, hazardous works will be handled by robots. Now the robots are already being employed to handle heavy lifting tasks, along with handling the mundane ones that require only repetition and manual labor. Along with automating these tasks, the robot workforce can also handle the situation where human workers might sustain grave injuries. If you have been aware and interested then you already heard about the “SmokeBot”. In the future, it might be the robots who will enter the flaming buildings for assessment before their human counterparts can start their task. Manufacturing plants that deal with toxic elements need robot workers, as humans run a bigger risk when they are exposed to such chemicals. Furthermore, the nuclear plants might have a robot crew that could efficiently handle such tasks. Other areas like pipeline exploration, bomb defusing, conducting rescue operations in hostile terrain should be handled by AI robots.

Smarter healthcare facilities

AI implementation which has already begun would continue to transform the healthcare services. With AI being in place CT scan and MRI images could be more precise pointing out even minuscule changes that earlier went undetected. Drug development could also be another area that would see vast improvement and in a post-pandemic world, people would need to be better prepared to fight against such viruses. Real-time detection could prevent many health issues going severe and keeping a track of the health records preventive measures could be taken. One of the most crucial changes that could be revolutionary, is the personalized medication which could only be driven by AI technology. This would completely change the way healthcare functions. Now that we are seeing chat bots for handling sales queries, the future healthcare landscape might be ruled by virtual assistants specifically developed for offering assistance to the patients. There are going to be revolutionary changes in this field in the future, thereby pushing the demand for professionals skilled in deep learning for computer vision with python.

Smarter finance

We are already living in an age where we have robo advisors, this is just the beginning and the growing AI implementation would enable an even smarter analytics system that would minimize the credit risk and would allow banks and other financial institutes to minimize the risk of fraud. Smarter asset management, enhanced customer support are going to be the core features. Smarter ML algorithms would detect any and every oddity in behavior or in transactions and would help prevent any kind of fraud from happening. With analytics being in place it would be easier to predict the future trends and thereby being more efficient in servicing the customers. The introduction of personalized services is going to be another key feature to look out for.

Retail space gets a boost

The retailers are now aiming to implement AI applications to offer smart shopping solutions to the future buyers. Along with coming up with personalized shopping suggestions for the customers and showing them suggestions based on their shopping pattern, the retailers would also be using the AI to predict the future trends and work accordingly. Not just that but they can easily maintain the supply and demand balance with the help of AI solutions and stock up items that are going to be in demand instead of items that would not be trendy. The smarter assistants would ensure that the customer queries are being handled and they could also be helping them with shopping by providing suggestions and information. From smart marketing to smarter delivery, the future of retail would be dominated by AI as the investment in this space is gradually going up.

The future is definitely going to be impacted by the AI technology in more ways than one. So, be future ready and get yourself upskilled as it is the need of the hour, stay updated and develop the skill to move towards the AI future with confidence.

What are the Job Opportunities Available in the Field of AI?

Posted on November 2, 2020November 2, 2020 by Dexlab

Artificial Intelligence or, AI is an advanced technology that is busy taking the world in its strides. With virtual assistants, face recognition, NLP, object detection, data crunching becoming familiar terms it is no wonder that this dynamic technology is being integrated into the very fabric of our society. Almost every sector is now adopting AI technology, be it running business operations or, ensuring error-free diagnosis in the healthcare domain, the exponential growth of this technology is pushing the demand for skilled AI professionals who can monitor and manage the AI operations of an organization.

Since AI is an expansive term and branches off in multiple directions, the job opportunities available in this field are also diverse. According to recent studies, AI jobs are going to be the most in-demand jobs in the near future. Multiple job roles are available that come with specific job responsibilities. So, let’s have a look at some of these.

Machine Learning Engineer

An machine learning engineer is supposed to be one of the most in-demand jobs available in this field, the basic job of an ML engineer center round working on self-running software, and they need to work with a huge pile of data. In an organization, the machine learning engineers need to collaborate with data scientists and ensure that real-time data is being put to use for churning out accurate results. They need to work with data science models and develop algorithms that can process the data and offer insight. Mostly their job responsibility revolves around working with current machine learning frameworks and working on it to make it better. Re-training machine learning models is another significant responsibility they need to shoulder.

If recent statistics are to be believed the salary of a machine learning hovers around ₹681,881 in India.

Artificial Intelligence Engineer

AI engineers are indeed a specialized breed of professionals who are in charge of AI infrastructure and work on AI models. They work on designing models and then test and finally, they need to deploy these models. Automating functionalities is also important and most importantly they must understand the key problems that need AI solutions. AI engineers need to write programs, so they need to be familiar with several programming languages, having a background in Machine Learning Using Python could be a big help. Another important responsibility is creating smart AI algorithms for developing an AI system, as per the specific requirement that needs to be solved using that system.

In India, an AI engineer could expect the salary to be around ₹7,86,105 per year, as per Glassdoor figures.

Data Scientist

A data scientist is going to be in charge of the data science team and need to work on the huge volumes of data to analyze and extract information, build and combine models and employ machine learning, data mining, techniques along with utilizing numerous tools including visualization tools to help an organization reach its business goals. The data scientists need to work with raw data and he needs to be in charge of automating the collection procedure and most importantly they need to process and prepare data for further analysis, and present the insight to the stakeholders.

A data scientist could earn around ₹ 7,41,962 per year in India as per the numbers found on Indeed.

AI Architect

An AI architect needs to work with the AI architecture and assess the current status in order to ensure that the solutions are fulfilling the current requirements and would be ready to scale up to adapt to the changing set of requirements that would arise in the future. They must be familiar with the current AI framework that they need to employ to develop an AI infrastructure that is sustainable. Along with working with a large amount of data, an AI architect must be employing machine learning algorithms and posses a thorough knowledge of the product development, and suggest suitable applications and solutions.

In India an AI architect could expect to make around ₹3,567K per year as per Glassdoor statistics is concerned.

There are so many job opportunities available in the AI domain, and here only a few job roles have been described. There are plenty more diverse job opportunities await you out there, grab those, just get artificial intelligence certification in delhi ncr and be future-ready.

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