What’s behind the Virtual Machines?

Are Virtual machines making our world more virtual?

These days virtual machines are running everywhere on the internet, from Microservices to big modules of code running on these virtual machines. We might not know the difference between an application running on a single server or multiple parts running of the same application running on multiple instances of virtual machines, that’s the best part about virtual machines. We might not get the difference, but they will make us get the most of our hardware resources.
Virtual machine is hardware level virtualization, a VM provides a real computer emulation and are implemented using software emulation programs. The end user gets the same experience of an operating system hosted on physical hardware with a virtual machine running on a machine. Virtual machines use the hardware more efficiently and increases the productivity than a single OS running on a dedicated hardware. Multiple VM’s running on the same hardware can perform different jobs at the same time can effectively improve resource utilization.

A virtual machine monitor (VMM) or a Hypervisor is a program that runs on a machine and helps the host in creating multiple virtual machines that can be run simultaneously, pooling all the resources of the host machine and sharing among the virtual machines. A Hypervisor can manage multiple instances of different types of operating systems like MacOS, Linux or Windows, running on multiple instances in a single physical hardware all at the same time. Hypervisor will manage the distribution of CPU, memory, bandwidth or disk space among those instances. Regardless of the type of hypervisors, virtual machines and the guest operating system will work without any difference.

There are two types of Hypervisors  as listed below: Continue reading

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Salesforce Deployment Tools

A typical development process requires building, testing, and staging before releasing to a production environment. During this development cycle, one might migrate many times, either to keep development organizations in sync or to move changes through development organizations toward production and this is what we call Salesforce deployment.

Salesforce deployment is the migration of metadata from one Salesforce organization to another. If you are looking to enhance your Salesforce DevOps (Continuous Integration and Continuous Deployments) practices, this blog could get you started with the basics.

There are number of deployment tools available each having its own pros and cons. Some of them are listed below:

Change sets

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Is Race Condition a myth in Salesforce?

Since the introduction of the concept of multi-threading, there has been a drastic change in the way we code. Now multiple threads run in parallel, hence many tasks can be performed at the same time.

Salesforce too adhered to this concept taking a step forward in its endeavor and provided a multi-threaded environment.

But everything has its pros and cons and so did parallelism. Though it completely changed the way programs are executed but brought with it a new concurrency bug what we call as race condition.

When does it actually occur?

Race condition occurs when two thread operate on same object without proper synchronization and there operation interleaves on each other. Continue reading

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Predictive Analytics

The success of any organization depends on its vision and its ability to shape the future course of action with unmatched accuracy and timeliness. Wouldn’t it be great if this prediction part is made efficient, easy and streamlined for your employees which could ultimately lead to better fortunes!!

Predictive Analytics is one such technique that holds the potential to rise your business up-ahead from the market competition.  Continue reading

Posted in Artificial Intelligence, Machine Learning. Tagged with , , , .

Machine Learning with Python

Machine Learning and Artificial Intelligence are considered as an integral part of the future technologies.

Artificial Intelligence is an area focused on developing intelligent machines that work and react like humans. To achieve this Artificial Intelligence considers all the traits that can help achieve the feat, these traits include perception, learning and planning. Machine learning on the other hand focuses on development of programs in such a way that systems can access data and use it to learn for themselves Artificial Intelligence focuses on making machines smart i.e. react as the situation demands whereas machine learning is based on providing machines access to data, making them learn themselves which makes their decisions learnt rather than smart.

For purview of our topic lets focus on Machine Learning now. Continue reading

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Machine Learning vs. Artificial Intelligence-The identical twins or are they really?

Since you are reading this, I assume you are aware of, or at least have heard about Machine Learning and Artificial Intelligence. Being two of the hottest buzzwords in the industry right now, these are often used interchangeably leading to some confusion. However, these two have different meanings and applications. The two terms are very strongly related though, as they share a containership relationship between them where the former is a subset of the later. Lets dive deep into these topics and try to find the reason for this confusion and related solutions.

Why this confusion?

The main culprit behind this confusion is the interchangeable use of these two terms and the limited knowledge of the subject among the developer as well as user community. Artificial intelligence is heavily dependent on machine learning,

Machine Learning and AI confusion

Machine Learning and AI confusion

which has led to the perception that both terms refer to the same thing. This confusion has spread like wildfire in the industry and only people who are experts in this field, know the clear distinction among these terms.

Artificial Intelligence-The Big Brother

Artificial Intelligence is the intelligence demonstrated by machines which emulates a human like thinking and behavior, allowing them to make their own decisions in real life situations. Going by the computer science definition, AI is referred to as the study of intelligent agents, which are devices that perceive their environment and take actions accordingly in order to maximum fulfillment of their goals. These agents mimic certain cognitive functions, which humans relate with the human mind, like problem solving and learning. AI, traditionally, attempts to solve problems such as Reasoning, Knowledge Representation, Learning, Planning, Natural Language Processing etc. Generating an intelligent agent which can think like humans is the long-term goal since it makes use of all the former techniques mentioned.   Continue reading

Posted in Artificial Intelligence, Machine Learning, Salesforce AI, Salesforce Einstein, Salesforce Machine Learning. Tagged with , , , .

Use TransmogrifAI to jumpstart Salesforce machine learning

Salesforce released TransmogrifAI, a machine learning library written in Scala that runs on top of Spark. This can be potentially deployed on any cloud such as Heroku/PostgreSQL platform. What all is involved in TransmogrifAI?

  • Language: Scala
  • Underlying engine: Apache Spark data processing engine
  • Deployment platform: A standalone local machine or cloud platform like Heroku

Let us explore a bit more about these new players in the scene and whether they will align with our need to build robust machine learning models. The entry barrier to using the TransmogrifAI library is likely to be the new tech stack that a typical Salesforce developer needs to scale up to.  Continue reading

Posted in Salesforce AI, Salesforce Einstein, Salesforce Machine Learning. Tagged with , , .

Salesforce DevOps (CI/CD) InfoGraphic

Start your journey to Salesforce DevOps with this infographic that captures introduction to different aspects of DevOps in Salesforce platform. It compares different approaches to Salesforce DevOps and finally has a deep-dive on Salesforce DX as well. Be it a free tool or a commercial one, do exercise caution while choosing any of the approaches – since it takes 6-12 months investment to stabilize a DevOps approach.

Salesforce DevOps InfoGraphic

Salesforce DevOps InfoGraphic


Need help with Salesforce DevOps?

Call us at 855-Mirketa or write to us at info (at) mirketa.com to get FREE consultation on how to get started with Salesforce DevOps.

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What is driving the need for Salesforce DevOps?

Salesforce DevOps is picking up steam with the recent focus on source driven development on Salesforce platform, particularly through increasing adoption of continuous integration and continuous delivery (CI/CD) using Salesforce DX. What contributed to this shift from ‘Changesets’ and ANT migration tool approaches? Following are some crucial factors: Continue reading

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“Playing with the Sentiments”-a blog on Sentiment Analysis

People have always had an interest in what other people think, or what opinion they hold. Since the inception of the internet, increasing numbers of people are using websites and social media platform for expressing their opinion. Due to platforms such as Facebook, Twitter etc., it has become feasible to analyze and extract the public opinion on a certain topic, news story, product, or brand. Opinions that are mined from such services can be valuable. Data mined from these sources can be analyzed and presented accordingly to easily identify the online mood (positive, negative or neutral). This allows individuals or business to be proactive as opposed to reactive when a negative conversational thread is emerging. Alternatively, positive sentiments can be leveraged to identify product advocates as well to shape the business strategy by seeing the parts of the strategy that are working.

Salesforce Sentiment Analysis

Sentiment Analysis

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Posted in Learn Salesforce, salesforce certified, salesforce consultant, Salesforce Einstein, Uncategorized. Tagged with , , , .