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Introduction to Scala Developer

Scala is the new world, well this is what makes the Scala Developers. Scala being a general-purpose programming language provides a platform for both functional as well as object-oriented programming approaches. Scala Developers are the ones who works properly on Scala Language working with the various methods and functions related to it. Scala Developer with the help of Scala as their main language work on the various object-related programming concepts. The Scala Developers play a vital role in Data Science and Data Analytics. Various Data Tools are written with Scala as the main language providing various libraries related to Data Processing and analysis.

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Scala provides us with various API and libraries that make the developers comfortable for working with an object-oriented programming approach. Scala integrates the features of object-oriented programming making it easier for developers from Java or any object-oriented concept background.

Why Scala Developer?

Scala Developers are preferred in the market now day the reason being Scala is easy to learn, with developers having the access to write the queries in much simpler are easier form. It is a type-safe language from where we can work with both object-oriented as well as functional programming. The architecture of Scala makes a developer easy to understand and has more exposure to the industry.

The lines of code and the rules needed to write a JAVA code are comparatively much more complex and SCALA provides the features for writing it in a simpler way making it developers’ choice. The compilers used by the Scala developer are much smarter and environmentally friendly.

Let us check with an Example:

Let us create a List in JAVA.


import java.util.ArrayList; import java.util.List; public class HelloWorld{ public static void main(String []args){ list.add("1"); list.add("2"); list.add("3"); System.out.println(list); } }


Let’s check that in scala:


object HelloWorld { def main(args: Array[String]) { val list = List("1", "2", "3") print(list) } }


Scala developers came up with any method such as a map, flatmap making the looping and iterations easier for developers.

From this, we saw Why do we need Scala Developers and the benefit of having them in the industry.

Working Roles of Scala Developer

Let us see some Working roles of a Scala Developer:

Scala Developers deal with the object-oriented programming approach.

Scala Developers works with functional Data.

Scala can be used for various data analytics working model also like machine learning lib, R

Used for an end to end application development.

Various Big Data Environments are being set up using Scala as the main language.

End to End business development models is carried out in Scala Framework.

A huge exposure from the machine learning domain to web apps is available in Scala.

Many multi-core CPU arches is made using Scala language.

Many java related roles can also be clumped together with Scala making its exposure exposed.

Skills Required

A perfect Scala Developer must enrich the following Skills:

Must have programming experience.

Able to work on logic building and project architecture.

Having hands-on various programming rules like initialization of a variable, looping, memory allocation.

Having a computer background will definitely work.

Good knowledge of Scala IDE.

Able to extract the functionalities provided.

Analytical building approach.

Good knowledge of JAVA programming will always work.

Having the knowledge of Spark, BIG Data always works as Spark is written in Scala providing all the basic libraries for Big Data Processing.

Knowledge of Scala-based testing tools such as- Scala test, Specs2

Knowledge of Scala Build Tools (Sbt).

Knowledge of Scala Framework and libraries such as Scalaz, Cats.

Basic Scala functions such as- Pattern Matching, Case Class, Trait.


From the above article, we saw the importance of Scala Developer in the real world. From various examples and classifications, we tried to understand how the SCALA DEVELOPERS work and its usage in Scala Programming.

We also saw the skills required and the working role of Scala Developer, Also the working roles of Scala developer gave a clear picture about the industry exposure for the developers.

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Complete Guide To Mongodb Objectid()

Introduction to MongoDB ObjectId()

MongoDB objectid() returns a new objectid value, objectid in MongoDB consisting of 4-byte timestamp value which represented an objectid creation and is measured in seconds. Objectid is very important and useful to return a new objectid value, objectid in MongoDB consisting of 12-byte random value. A 3 byte incrementing counter is used to initialize random value, objectid in MongoDB will accept the hexadecimal string value for the new objectid. The hexadecimal parameter is an optional parameter that was used with objectid, type of hexadecimal parameter is a string.

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Syntax and Parameter

In the below syntax hexadecimal value is divided into three segments in MongoDB.

The first segment contains the 4-byte value, which represented the second since the UNIX epoch in MongoDB.

The second segment will contain 5-byte random value.

The third segment will contain 3 byte counter starts with a random value.

Objectid: Objectid is very important and useful in MongoDB to return a new objectid value. Objectid in MongoDB was contained three methods is get a timestamp, to String and value of. To create a new objectid in MongoDB, we need to declare objectid as the method. This we can define objectid as a unique identifier for each record.

Hexadecimal: This parameter is essential and useful in MongoDB to define hexadecimal value. Hexadecimal in MongoDB objectid define as value of the variable, we can define a variable in the place of a hexadecimal value. Each time declared variable using objectid in MongoDB would return a unique hexadecimal value.

How ObjectId() works in MongoDB?

Below is the working of objectid is as follows. This was basically provided three methods of objectid.




1. The first method is gettimestamp it will contain a timestamp. It is an essential and useful method of objectid. It will return the timestamp portion of the objectid.

2. The second method will contain a toString; it will convert the string. MongoDB toString will return the string representation of objectid.

The value of the method in objectid will returns a lowercase hexadecimal string in MongoDB. This value will contain str attribute of objectid.

We can declare a variable with objectid. The below example shows declare objectid.

A = objectid ()

The objectid is nothing but default primary key of the document, which was usually found in the id document field at the inserted document.

This objectid will contains 12-byte binary BSON type which contained 12 bytes. The driver and server will generate objectid using a default algorithm.

Objectid is very important and useful in MongoDB to return a new objectid value, objectid in MongoDB consist of 12-byte random value.

The hexadecimal parameter is an optional parameter that was used with objectid, type of hexadecimal parameter is a string.

The objectid() is return a new objectid value, objectid in MongoDB consists of 4-byte timestamp value which represented an objectid creation and measured in seconds.

A 3 byte incrementing counter is used to initialize random value, objectid will accept the hexadecimal string value for the new objectid.

If we want to define our own hexadecimal value in MongoDB, it will enable definer to define hexadecimal value.

This we can define objectid with hexadecimal value as a parameter or a method. We can also define objectid as a method in MongoDB. Objectid is also known as a unique identifier.

MongoDB objectid will create automatically when we have inserted a new document within the collection.

Examples to Implement MongoDB ObjectId()

Below are the examples mentioned:

Example #1 – Create objectid at the time of document insertion

Below example states that create objectid at the time of document insertion. At the time of document insertion, objectid will automatically be generated.



Explanation: In the above example, we have inserted three documents. But we have not inserted an objectid field. Objectid field will automatically be created at the time of document insertion.

Example #2 – Generate new objectid

The below example shows create new objectid. At the time of creating a new objectid, we have to define A as variable.


A = ObjectId()


Example #3 – Specify a hexadecimal string

In the below example, we have to define the hexadecimal string. The hexadecimal string will create the object. The hexadecimal string will return the same hexadecimal string which we have to define in the example.



Example #4 – Access hexadecimal string

The below example is an objectid that access hexadecimal string using an str attribute. It will return hexadecimal value using an str attribute.


ObjectId ("807f191a810c19729de860ae").str


Example #5 – Objectid using gettimestamp

In the below example, we have called the gettimestamp method to generate objectid. Gettimestamp is a handy and important method to generate objectid.




Example #6 – Objectid using toString

The below example shows objectid using the toString method. In the below example, we have called the toString method to generate objectid, toString is a handy and important method to generate objectid.




Example #7 – Objectid using valueOf

The below example shows objectid using the valueOf method. In the below example, we have called the valueOf method to generate objectid in MongoDB, valueOf is a handy and important method to generate objectid.





Objectid is very important to return new objectid value, objectid consist of 12-byte random value. Thus objectid() is return a new objectid value, objectid in consisting of 4-byte timestamp value which represented an objectid creation and measured in seconds.

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A Complete Guide To Tensorflow Dataset

Introduction to TensorFlow Dataset

Basically, TensorFlow acts as a collection of different kinds of dataset and it is ready to use in another word, we can say that it is one kind of framework that is used for Machine Learning. The main purpose of the TensorFlow dataset is that with the help of the TensorFlow dataset, we can build the pipeline in a machine learning application.

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What is TensorFlow Dataset?

Profound learning is a subfield of AI or we can say that machine learning is a bunch of algorithms that is propelled by the design and capacity of the cerebrum. Basically, TensorFlow is used for the AI framework or we can say that machine learning framework and it directly gives the straightforward solution to implement the AI concept.

You can utilize the TensorFlow library due to mathematical calculations, which in itself doesn’t appear, apparently, to be all through the very novel, yet these assessments are finished with information stream charts. These hubs address numerical tasks, while the edges address the information, which as a rule are multidimensional information exhibits or tensors, which are imparted between these edges.

TensorFlow Dataset Example Model

Let’s see the example of the Tensorflow dataset as follows:

Datasets is another approach to make input pipelines to TensorFlow models. This API is considerably more performant than utilizing feed_dict or the line-based pipelines, and it’s cleaner and simpler to utilize.

Normally we have the following high-level classes in Dataset as follows:

Dataset: It is a base class that contains all the methods that are required to create the transformed dataset as well it also helps initialize the dataset in the memory.

TextLineDataset: Basically we need to read the line from the text file, so for that purpose we use TextLineDataset.

TFRecordDataset: It is used to read the records from the TFRecord files as per requirement.

FixedLengthRecordDataset: When we need to read the fixed size of the record from the binary file at that time we can use FixedLengthRecordDataset.

Iterator: By using Iterator we can access the dataset element at a time when required.

We need to create the CSV file and store the data that we require as follows: Sepailength, SepalWidth, SetosLength, SetosWidth, and FlowerType.


In the above-mentioned input value, we need to put it into the CSV file which means we read data from the CSV file. The first four are the input value for a single row and FlowerType is the label or we can say the output values. We can consider a dataset of input set is float and int for the output values.

We also need to label the data so we can easily recognize the category.

Let’s see how we can represent the dataset as follows:


types_name = ['Sepailength','SepalWidth',' SetosLength',' SetosWidth']

After the training dataset, we need to read the data so we need to create the function as follows:


def in_value(): ………………… return({'Sepailengt':[values], '……….'}) Class Diagram for Estimators

Let’s see the class diagram for the Estimators as follows:

Estimators are an undeniable level API that diminishes a significant part of the standard code you recently expected to compose when preparing a TensorFlow model. Assessors are likewise truly adaptable, permitting you to abrogate the default conduct on the off chance that you have explicit prerequisites for your model.

We can use two ways to build the class diagram as follows:

Pre-made Estimator: This is a predefined estimator’s class and it is used for a specific type of model.

Base Class: It provides complete control over the model.

Representing our Dataset

Let’s see how we can represent the dataset as follows:

There are different ways to represent the data as follows:

We can represent datasets by using numerical data, categorical data, and ordinal data, we can use anyway as per our requirement.


import pandas as pd_obj data_info = pd_obj.read_csv("emp.csv") row1 = data_info.sample(n = 1) row1 row2 = data_info.sample(n = 1) row2


In the above example we try to fetch the dataset, here first we import the pandas to implement the AI program after that we read data from the CSV file as shown, here we have an chúng tôi file and we try to read the data from that file.

The final output or we can say that result we illustrated by using the following screenshot as follows.


Similarly, we can display the second row same as above.

Importing Data TensorFlow Dataset

Let’s see how we can import the Data TensorFlow dataset as follows:


import tensorflow as tf_obj A = tf_obj.constant([4,5,6,7]) B = tf_obj.constant([7,4,2,3]) res = tf_obj.multiply(A, B) se = tf_obj.Session() print( se.close()


By using the above we try to implement the TensorFlow dataset, here first we import the TensorFlow as shown, and after that, we write the two different arrays A and B as shown. After that, we make the multiplication of both arrays and store results into res variables. In this example, we also add a session and after the complication of the operation, we close the session.

The final output or we can say that result we illustrated by using the following screenshot as follows.


Freebies TensorFlow Dataset

Basically, the Tensorflow dataset is an open-source dataset that is the collection of datasets we can directly use during the machine learning framework such as Jax, and all datasets we can set by using the TensorFlow as per requirement.

It also helps us to improve our performance.


From the above article, we have taken in the essential idea of the TensorFlow dataset and we also saw the representation of the TensorFlow dataset. From this article, we saw how and when we use the TensorFlow dataset.

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Complete Guide On Tensorflow Distributed?

Introduction to TensorFlow Distributed

A TensorFlow API allows users to split training across several GPUs, computers, or TPUs. Using this API, we can distribute existing models and learning code with a few source codes. It takes a long time to train a machine learning model. As dataset sizes grow larger, it becomes increasingly difficult to train models in a short period. Distributed computing is used to overcome this.

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What is TensorFlow Distributed?

TensorFlow provides distributed computing, allowing multiple processes to calculate different parts of the graph, even on different servers. This can also allocate computation to servers with strong GPUs while other computations are performed on servers with more memory. Furthermore, TensorFlow’s distributed training is based on data parallelism, which allows us to run different slices of input data on numerous devices while replicating the same model architecture.

How to use TensorFlow distributed?

Tf. distribute is TensorFlow’s principal distributed training method. Strategy. This approach allows users to send model training across several PCs, GPUs, or TPUs. It’s made simple to use, with good out-of-the-box performance and the ability to switch between strategies quickly. First, the total amount of data is divided into equal slices. Next, these slices are chosen depending on the training devices; after each slice, a model can be used to train on that slice. Because the data for each model is distinct, the parameters for each model are likewise distinct, so those weights must eventually be aggregated into the new master model.

1. Generate asset data records in the package

2. Using Dask, pre-process and serialize asset data in a distributed manner for each batch (or other scalers)

3. Create a TFRecord file for each session with serialized binary sets.

tf.distribute. the Strategy was created with the following important objectives in mind:

 Switching between strategies is simple.

Mirrored Strategy

tf. distribute is a mirrored strategy. Mirrored Strategy is a technique for performing synchronous distributed training on many GPUs. Using this Strategy, we can construct clones of our model variables mirrored across the GPUs. These variables are collected together as a Mirrored Variable during operation and kept in sync with all-reduce techniques. NVIDIA NCCL provides the default algorithm; however, we can choose another pre-built alternative or develop a custom algorithm.

Creating mirrored type

mirrored_strategy = tf.distribute.MirroredStrategy()

TPU’s Strategy

One can use tf.spread.experimental.TPUStrategy to distribute training among TPUs. It contains a customized version of all-reduce that is optimized for TPUs.

Multiworker Mirrored Strategy

It’s a very specific strategy – multimachine chúng tôi manage the process, it replicates variables per device across the workers. So reduction is dependent on hardware and tensor sizes.


Going distributed allows us to train all of the huge models at the same time, which speeds up the training process. The architecture of the concept is seen below. A-C API separates the user-level code in multiple languages from the core runtime.


Distributed Master:

The distributed master prunes the graph to get the subgraph needed to evaluate the nodes the client has requested. The optimized subgraphs are then executed across a series of jobs in a coordinated manner.

Worker Service:

Each task’s worker service processes requests from the master. Kernels are sent to local devices by the worker service, which runs them in parallel. While training, workers compute gradients, typically stored on a GPU. If a worker or parameter server breaks, the chief worker controls failures and ensures fault tolerance. If the chief worker passes away, the training must be redone from the most recent checkpoint.

Kernel implementation

Several action kernels are performed with Eigen: Tensor, which generates effective feature code for multicore CPUs and GPUs using C++ templates.

Practical details to discuss

Creating two clusters before the servers are connected to execute each server in a separate process.


Example of TensorFlow Distributed

To execute distributed training, the training script must be adjusted and copied to all nodes.

work = ["localhost:2222", "localhost:2223"]

jobs = {"local": tasks}

Starting a server

ser2 = tf.train.Server(cluster, j_name=”local”, task_index=1)

Next Executing on the same graph

see2 = tf.Session(

The next modification done in the first server is reflected in another server.

print(“Value in second session:”,


The above steps implement a cluster where the two servers act on it. And the output is shown as:



We now understand what distributed TensorFlow can do and how to adapt your TensorFlow algorithms to execute distributed learning or parallel experiments. By employing a distributed training technique, users may greatly reduce training time and expense. Furthermore, the distributed training approach allowed developers to create large-scale and deep models.

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A Complete Guide To Vegan Muscle Building

This post has been updated. It was originally published on 1/7/2023.

When articles proclaim that “veganism is growing in popularity,” it’s not just because Instagram and Youtube makes it feel that way. It’s because veganism is, in fact, growing in popularity.

Some fast facts:

On Dec. 30, 2023 more than 14,000 people formally promised (by signing up through this website) to not eat animal products in January. Participation in Veganuary has more than doubled every year since the campaign began in 2014.

Last year, restaurant consulting group Baum + Whitman identified plant-based food as a major trend for 2023. “That’s still true” for 2023, the copywriter(s) note [PDF], adding that this year lab-grown meats “look like profound long-range game changers.” (The brief writer also describes cows as “prolific poopers,” so BRB going to hire them to write for Popular Science.)

In 2023, Nestle—whose brands range from Hot Pockets to Coffeemate to Haagen-Dazs to Digiorno—also identified plant-based foods a trend the company, in the words of its Executive Vice President of Strategic Business Units, “believe[s] is here to stay and amplify.”

6 percent of U.S. consumers now claim to be vegan, up from 1 percent in 2014. That’s a 500 percent increase, or a difference of 1.6 million people.

Ariana Grande is vegan.

If you are one of the millions of folks who now call themselves vegan (or plant-based or whatever) and you have divulged this fact to anybody, you have probably been asked about your protein sources and intake. People may have wondered about your muscle mass, or your strength. And it’s not a totally unreasonable concern. Generous protein intake is essential for maintaining and building muscle. Eggs, meat, and dairy make up roughly 62 percent of the protein consumed by US adults—and that number may actually be greater when you consider that 8 percent of the protein consumed “could not be classified” (hot dogs?). The remaining 30 percent is plant protein, the largest dietary source of which is bread, which doesn’t exactly have a reputation for being protein-rich.

When I transitioned out of animal products a year ago—there are many science–backed reasons to reduce your animal product intake—the move seemed to run counter to my goals for athleticism and overall beefcakiness (pun intended). I’ve drained hours researching what humans need to build muscle optimally. When I tried to find research on how people who don’t eat egg whites, whey protein powder, or 93-percent-lean ground beef can optimize their gains, the Google Scholar well ran dry.

But even without recent or replicated peer-reviews papers, we have proof enough that getting buff with plant-based protein is, in fact, possible. There are enough vegan bodybuilders and Olympic athletes to show us it can be done. But how? I asked four experts and compiled their knowledge below.

Our panel: (1) Dr. Anastasia Zinchenko, a vegan bodybuilder, powerlifter, and coach with a PhD in biochemistry and books full of high-protein bake recipes. (2) Jordan David, a vegan bodybuilder, health coach, and founder of Conscious Muscle, which sells coaching, apparel, and supplements. (3) Dr. Rachele Pojednic, an assistant professor of nutrition at Simmons University. (4) Kendrick Farris, a vegan weightlifter who represented the U.S.A. in the 2008, 2012, and 2023 Olympic Games.

How much protein the aspiring buff vegan should eat

As previously mentioned, the survival of our species does not require as much protein as many Westerners are led to believe. Adequacy, according to the US recommended dietary allowance, is just 0.8 grams of protein per kilogram of body weight. So, for example, a person who weighs 170 pounds (or 77 kilograms) should eat about 62 grams of protein (that’s 77 x 0.8). Most people (especially meat eaters) get that without even really trying. A peanut butter sandwich on wheat bread, for example, has about 18 grams of protein.

[Related: You don’t have to eat vegan to reduce your environmental impact.]

Zinchenko, who shares tips on her website Science Strength, recommends vegans eat 2.4 grams of protein per kilogram of body weight (or about 1.1 grams per pound). That’s a lot higher than what governmental organizations recommend, but her clients want to bulk up, not simply be healthy. And muscle gain requires the amino acids in protein. (More on that below.)

People who go to David for coaching are prescribed 1 gram of protein per pound of body weight, which shakes out to just a bit less protein than Zinchenko prescribes. Pojednic, a nutritionist, recommends training athletes get anywhere from 1.6 to 2.2 grams of protein per kilogram of body weight.

For context, a 170-pound individual would consume 185 grams of protein on Zinchenko’s plan, 170 grams of protein on David’s plan, and 123-169 grams of protein on Pojednic’s plan. That difference in protein consumption equates to roughly two protein shakes, or a block and a half of tofu.

The difference between vegan proteins and proteins that come from animal products

If you aren’t vegan, Zinchenko says, you can get away with eating 2.1 grams of protein per kilogram of body weight, because the amino acid profiles in animal products are slightly better for making muscle. Pojednic notes that amino acid composition is “one of the key distinctions” between a vegan and an omnivorous diet.

Both Zinchenko and Pojednic call out amino acids leucine and lysine, in particular. Animal proteins generally have more of these amino acids than plant proteins (here’s a graph), which is significant because these building blocks seem to be particularly good drivers of muscle protein synthesis. You can boost your levels by incorporating a supplement, which Zinchenko recommends, though Pojednic notes that there are plenty of vegan sources of leucine: soy isolates (like soy protein powder), seaweed and spirulina, sesame seeds, sunflower seeds, and tofu.

[Related: What to know before investing in a pair of running shoes]

“It’s not just about getting enough protein,” Zinchenko says, “The distribution of amino acid types is also important. It’s like building a house. It must have windows, bricks, and doors. You can have all the bricks in the world, but without a door, it’s not going to be a house.”

That doesn’t mean you need to make sure each serving of protein includes a mix of all the necessary amino acids, Pojednic says. If you’re eating a variety of foods throughout the day, there’s no need to pair up rice and beans for every meal. Your body can still put all the pieces together.

Fiber and protein bioavailability

Even if your lunchtime salad has all the nutrients you want, your body may not be absorbing them. Some foods are harder for the human digestive system to turn into nutrients. Compared to meats, eggs, and dairy, vegetal proteins are not as bioavailable, meaning your body might not actually get to use all the protein contained in the raw spinach you scarfed.

Whole (also known as unprocessed) foods, which are recommended by the USDA, contain fiber and other substances that can limit absorption in the small intestine. “If you eat a raw vegan diet, you may need to aim for 2.7 grams per kilogram of body weight, which is just an insane amount of broccoli and beans,” Zinchenko says.

Whole foods generally take longer to digest, which is why serious muscle-builders may want to chug a protein shake on a relatively empty stomach—and why both David and Zinchenko recommend some sort of protein supplementation. Pojednic suggests it too, particularly if your stomach can’t handle a full meal after lifting.

Do vegans need to supplement?

Vegans need to take a B12 supplement. Most of the B12 humans get in their diet comes from animal products, as a result of microorganisms being processed in the guts of cattle and sheep. Without foods from those critters, it’s a lot harder to get adequate B12, which means vegans rely on fortified plant milks and cereals or supplementation. (Unless they are really into eating seaweeds like spirulina or dried nori, which contain B12.)

Otherwise, no, you don’t need to use a protein shake or branched chain amino acid powder—but they can make it a whole lot easier to get your protein in. Otherwise, 150 grams of protein a day (without just oodles of carbs) can be overwhelming.

The ideal ratio of vegan protein sources

Zinchenko encourages vegan lifters to “err on the side of caution” and eat about 50 percent of your protein from legumes (beans, peas, soy, etc.), 25 percent from grains, and 25 percent from nuts and seeds to make sure you’re getting adequate amounts of the necessary amino acids.

Meal timing

“The most recent recommendations clarify not just how many grams of protein you should eat, but also how those grams are pulsed throughout the day,” Pojednic says. “Scientists are thinking now that there’s only a certain amount of protein your muscles can uptake and utilize in one sitting. If you flood your system with amino acids, at some point they’re a little bit wasted.”

Aim to get 0.25 and 0.4 grams of protein per kilogram of body weight per meal. Or, to put it way simpler, space out your protein over 3 or 4 meals a day, not just all at once in a mega smoothie.

The other science-backed tip is to make sure you’re eating 20-30 grams of protein within 30 minutes (up to an hour is probably fine) of training. “The literature shows that ingesting both protein and carbs in that window promotes muscle growth and recovery, which helps you stay up on your training regimen.” Yes, that can be a protein shake—but it can also be a peanut butter sandwich.

But the most crucial aspects of gaining muscle have nothing to do with being vegan.

It’s not all about getting enough amino acids. You’ve got to eat enough calories to gain mass, and you’ve got to train hard. Farris, who went plant-based in November 2014 (between Olympics appearances), is a world-class athlete who happens to be vegan—and he doesn’t track his protein at all. Still, he was able to “make some gains and, more importantly, stay healthy.” He says a vegan diet has let him recover faster. “If you can do that, you can do more work. You can beat your body up more. Simply, just train.”

[Related: What’s the best total-body exercise?]

(It’s worth noting that part of the reason Farris had no qualms about changing his diet while in a high point of his career was because he spent his prime lifting years (19-22) gaining strength without reliable access to any type of food at all. “If I could lift and do it all when I didn’t have access to regular meals, how was I going to get weaker eating enough food but switching out the ingredients?”)

In case that doesn’t drive the point home, know that every single person I spoke to for this article mentioned the importance of simply eating enough food.

“A big problem for vegans is that they can easily under eat,” Zinchenko says. “Especially active people who eat a lot of whole foods. Without calories, your body can’t make muscle.”

“The main thing is high-volume weight training and getting adequate nutrients,” David says. “That’s it. There are no shortcuts. The harder you hit it, the more you feed it, the more it will grow.” (I believe we were talking about butts at this point in the conversation.)

“Obviously diet is going to give you that tiny push at the end, but the training and the dedication is really what’s going matter in the long term for high-level athletes,” Polojic says.

Oh, and for what it’s worth, we sorely need more research on vegans. “Even the studies that examine vegan protein powders are not done on vegans,” Zinchenko says. “If there is somebody who would like to donate money to study vegan muscle growth, I would be happy to run the study.”

Chatbot Pricing – Complete Guide In 2023

Impulse investing in expensive chatbots to drive digital transformation can hamper growth and waste company resources, as the conversational AI market has a wide price range and many options.

We wrote this article so that business leaders make informed decisions on conversational AI solutions by learning:

Types of chatbot pricing plans,

Costs involved in purchasing chatbots,

Pricing plans from top chatbot vendors

How to choose the chatbot option for your needs.

What are the types of chatbot pricing plans? Free plan chatbots

Free plan chatbots are a starting place for small businesses that want to automate customer support services. Most chatbot companies offer free chatbots which are beneficial for companies with a limited budget and little to no experience with chatbot development. Free chatbots offer basic features, but often come with restrictions such as:

The number of customer conversations

A limited number of staff accounts

Chatbot integration capabilities

You can start experimenting with a free plan before moving on to different pricing plans with more features or no restrictions.

Subscription chatbots Enterprise chatbots

Many conversational AI platforms also offer solutions for enterprises with an enterprise plan. The chatbot cost for enterprise solutions is higher, as they offer fully customized chatbot services (i.e. a dedicated account manager) to ensure the business goals of utilizing a chatbot are met successfully (see Figure 1).

Figure 1: Haptik’s pricing

Source: Haptik

Conversational AI firm Haptik offers chatbots and intelligent virtual assistants (IVAs) that enable businesses from a variety of sectors to interact with their clients on WhatsApp, mobile apps, websites, and more. Haptik has a large portfolio of customers, ranging from SMEs to enterprises. You can see the capabilities of conversational AI solutions by requesting a demo from Haptik.

What are the costs involved in purchasing a chatbot?

Chatbot pricing, especially for enterprise chatbots, is comprised of many additional costs. These costs are paid for features such as enhanced privacy, maintenance, and development. We will examine these costs in detail to inform potential customers about how the pricing plans of chatbots are formed.

1. Chatbot software platform cost

Chatbot providers have specified chatbot platforms that are used to develop, deploy, and modify conversational AI solutions. The chatbot software platform is commonly present on all pricing plans from free to enterprise, but features depend on the chosen plan.

2. License costs

Some business owners may have concerns about the security of the data collected through chatbots, such as who has access to it or where it is stored. To mitigate these concerns, chatbot vendors often include license costs to govern the use and distribution of chatbot solutions.

3. Development and installation costs

Chatbot companies provide technical assistance to companies looking to outsource chatbot development. Developers from the chatbot company build and deploy bots tailored to the client’s business needs. Technical assistance often includes installation, which is required to integrate the chatbots into existing systems/applications for running operations smoothly.

In addition to technical assistance, chatbot companies can offer insight into the design and marketing that goes into chatbots. These costs are associated with a graphic designer and a content creator, which works to ensure the end product is in-line with your business requirements. A chatbot with a good user experience will leave a positive impact on customers, which will lead to a higher return on investment.

4. Support and maintenance costs

You might need to improve or change chatbot features as your business grows, which requires an ongoing relationship with the chatbot vendor. Subscription or enterprise plan chatbot solutions serve as long-term partners to the clients and assist them where they need it. Some applications include:

Solving encountered bugs or issues

Meeting additional requests

Keeping systems and platforms up-to-date.

5. Usage costs

These costs include additional costs that are paid to third parties. An example is WhatsApp chatbots, which require a WhatsApp business API provided by an official WhatsApp Business partner. WhatsApp charges businesses on a per-conversation basis, so exceeding the free conversation limit translates to usage costs for the business.

Pricing plans from top chatbot vendors

You can view the table of top chatbot vendors with information regarding pricing plans from their websites below. 

VendorPricing PlanFree Trial / DemoFree PlanStarter PlanEnterprise Plan HaptikSubscriptionYesNo$5000.00 /yearUndisclosed Kore.aiUsage-basedYesYes$0.01 / user requestSession-based pricing (undisclosed) IBM Watson AssistantSubscriptionYesYes$140.00 /monthUndisclosed Yellow.aiUsage-basedYesNoUndisclosedUndisclosed $4 per 1 million charactersNo Azure Bot ServicesUsage-basedYesYes$0.50 per 1,000 messages in Premium Channels (non-Microsoft or open)No

How to choose the chatbot option for your needs 1. Determine the use case

Chatbots are not developed to meet general business needs, they are tailored toward specific use cases such as conversational marketing or answering FAQs. It is essential to determine clear use cases prior to investing in a chatbot, which will directly influence the chatbot flow, the integrations, and the associated costs.

2. Determine your bot requirements

Bots can be created with a decision tree or rule-based format, where there is a predetermined flow that designates how customer conversations proceed. An alternative is AI chatbots, trained with natural language processing (NLP) tools. AI chatbots improve their performance over time as they gain insights from customer queries, but they are costlier compared to rule-based chatbots. 

Determining bot requirements goes hand-in-hand with the use case, as some use cases such as lead generation may require a more sophisticated AI chatbot.

3. Perform a cost-benefit analysis

Analyze the cost of the process you are trying to automate with a chatbot, with metrics such as:

Staff costs,

Completion time,

Customer satisfaction rate,

Actual and forecasted demand.

Once you can paint a clear financial picture of the process, you can compare it with the conversational AI solutions of your selected vendor.

Further readings

To learn more about the best conversational AI solution providers you can read:

To learn more about conversational AI solutions you can download our conversational AI whitepaper:

If you need more information to find the best chatbot companies, you can reach us:

This article was drafted by former AIMultiple industry analyst Berke Can Agagündüz.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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