Predicting Success of your Salesforce Development Project

Can you really predict the success of your Salesforce Development /Integration project with a high degree of confidence at the beginning of your project?

The answer is No!

But you can rate your people, process and tools that are required for success and use that to predict success with a ‘good enough’ confidence level.

Salesforce lightning

Pillars of Salesforce Development Project success.

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Posted in Agile, Apex Development, Salesforce cloud Implementation, salesforce consultant, salesforce customization, salesforce development, Salesforce DevOps, Salesforce Implementation. Tagged with , , , .

A Primer in Software Testing

Software testing is a field that gained currency in the late 90s and ’00s for different reasons. As people moved from client-server to web applications, it got a new life as software systems will behave differently. Software testing was re-invigorated with Web 2.0. Subsequently, it was in focus again with mobile applications and tailoring user experience for mobile devices. Lately, focus on software testing is again on the go with new architectural patterns including micro services architecture. Model-driven testing and behavior-driven development have been other testing approaches that have influenced the overall industry. Today, cloud-based applications (such as Salesforce) that can scale on-demand and cloud-based storage are in vogue; these require not-so-different test practices. Continue reading

Posted in Agile, Software Testing.

The Future For Analytics, Data And What It Can Do For Businesses?

Times are changing; this has been a term in use since the rapid progress of humanity since the 18th century. Today, it can be used even on a daily basis. Technology remains the cornerstone through which humanity evolves, it is now embedded in every single part of our lives today and imagining a future without it would be quite challenging. This is why going forward, there is always going to be a discussion about its impact and how it will change as time comes.

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Posted in Artificial Intelligence, Sales Cloud, Salesforce AI, Salesforce cloud Implementation, Salesforce Einstein, Service Cloud.

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 is 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 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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Posted in apex develeopment, Apex Development, force.com app development, salesforce development, Salesforce DevOps, salesforce integration, sfdc, Uncategorized. Tagged with , , , , .

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

Posted in Apex Development, force.com app development, Learn Salesforce, Salesforce, salesforce certified developer. Tagged with , , .

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 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 learned rather than smart.

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

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

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 the user community. Artificial intelligence is heavily dependent on machine learning,

Machine Learning

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.

Now, there are two ways in which the intelligent agents can achieve this, the first one being by using a set of if/else

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

statements which provide the answer for each problem statement. This is a very orthodox, ineffective and tedious way of doing this. Another way is to use Machine Learning which is more flexible & dynamic, being able to learn from the data it processes and improve upon the results incrementally in real time.

Due to disagreements on any established paradigm to be used in machine learning, there is still no fixed approach which works effectively. Some of the popular approaches are as follows:

  • Cybernetics and brain simulation
  • Cognitive Simulation
  • Statistical Learning

Machine Learning-An Overview

Machine Learning is a field of artificial intelligence which makes use of statistical techniques and functions to give a computer the ability to learn itself from the provided data, without the need of any explicit programming. This act of the machine system learning by itself progressively improve its performance for a specific task. The heart of machine learning models lies in the dataset which is being used to train the model as well as the algorithms which operate on the data. These algorithms learn from the provide data and make predictions, overcoming static and fixed programming instructions by employing data-driven decisions through a model which it builds from the training data given.

There are two learning types in machine learning- supervised learning and unsupervised learning. The former takes a well labeled data set to operate upon and makes deductions based on it. The later one takes in raw, unlabeled data and finds patterns in the data based on which new data predictions are made.

Closely related to the field of Computational Statistics, machine learning also focuses on making predictions though the use of computers. Mathematical optimization is also tightly coupled with ML since it provides the theory, methods and application domains to the field. Machine Learning is also used in the field of Data Analytics to generate complex models and algorithms which lend themselves to prediction; commercially this is referred to as Predictive Analytics. These models help data scientists, researchers, engineers and experts, to produce reliable results, repeatedly and uncover hidden insights within the data

There are N number of algorithms present which can be utilized to solve your machine learning problem. Each has its own strengths and weaknesses and its fit depends completely on the dataset and the use case. Some of the popular algorithms/methods being used are Decision tree learning, Artificial Neural networks, Inductive Logic Programming, Deep Learning, Bayesian Networks and many more.

Conclusion

Well, this confusion, however small it is, must be cleared since this might lead to problems down the line as the scale of artificial intelligence and machine learning increases. In large and critical implementations, using them interchangeable should be unacceptable and the community should be made more aware about these concepts and their differences. You can reach us for all your AI needs since we, being the AI & ML experts, can help you design a custom solution for your machine learning problem.

References

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 , , .