Abhay Reddy

Generative AI Marketing Use Cases: Where Do You Want To Start?
Medium
December 06, 2023

Marketing has seen an explosion of innovation reshaping the field. Generative AI is one such game-changer technology that allows today’s marketers to stay ahead of the curve. It allows for innovative solutions to age-old problems by automating repetitive tasks, increasing scalability, personalizing interactions, and enhancing creativity. While 98% of executives said that Gen AI was a hot topic of discussion with their board [1], only 14% of organizations were using Gen AI for Marketing and Sales [4]
I have highlighted the key Gen AI use cases in Marketing to get started.

Generative AI in Marketing: Understanding with Examples
Medium
December 04, 2023

ChatGPT (OpenAI) has revolutionized the way we interact with artificial intelligence and made the technology more mainstream. Generative AI is expected to add trillions of dollars to the global economy with 75% of value coming from use cases in marketing, sales, software engineering, R&D, and customer ops. The survey reported that only 14% of organizations were using Gen AI for Marketing and Sales [4]. The best way to understand Gen AI in Marketing is to look at how some of the top companies leveraged this technology to stand out.

Generative AI in Marketing: Do the Benefits Outweigh the Risks?
Medium
November 29, 2023

What is generative AI? Generative AI (Gen AI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new novel and unique data with similar characteristics. [source]

The more data the AI models consume the more they learn. They are currently able to deliver human-level results, but with time they will be able to deliver superhuman-level results, transforming productivity. The data sources fed into a model could include marketing trends, customer purchasing behavior, demographics, digital interactions, web analytics, and more. The value and accuracy of data used for training determine the effectiveness of the outcome. The trained model can then generate content such as articles, banner ads, blogs, personalized videos, emails, and more, that resonate with the target audience.

Marketing has seen an explosion of innovation reshaping the field. Generative AI is one such game-changer technology that allows today’s marketers to stay ahead of the curve. It allows for innovative solutions to age-old problems by automating repetitive tasks, increasing scalability, personalizing interactions, and enhancing creativity. While 98% of executives said that Gen AI was a hot topic of discussion with their board [1], only 14% of organizations were using Gen AI for Marketing and Sales [4]

Understanding Containers and Kubernetes 101
Medium
December 29, 2022

Containers are units of software that contains the code and all dependencies so that an application can run across platforms such as desktops, data centers, and cloud. It provides an abstraction at the application layer. Each container runs as an isolated process while sharing the same OS kernel.

The application container market is expected to grow by CAGR 32% between 2020 and 2028. [Verified Market Research]

With the explosive growth of container use, many complexities arose such as how to manage and schedule multiple containers across platforms, how to scale up, how to enable communication between them, and more. Kubernetes was introduced as a way to solve these challenges by Google in 2014. It was adopted by Cloud Native Computing Foundation (CNCF) in 2016.

Kubernetes is an open-source system for automating software deployment, scaling, and management of containerized applications. It has an expansive open-source ecosystem and is the market leader in this segment. All major players such as Google, Docker, Red Hat, Microsoft, AWS, Wind River, and VMware have adopted/supported Kubernetes.


Key benefits of Kubernetes:

Simplifies container management, and is highly scalable
Automation capabilities can handle the scheduling, and deployment of containers regardless of location (on-premise, cloud, VM, or hybrid).
Highly portable across multiple platforms. Deployments can be sent to one or more cloud services without losing functionality or performance.
It can auto-scale up or down to increase efficiency and reduce waste. Can create new containers while dealing with a heavy workload
Service discovery and load balancing. If traffic to a container is high, Kubernetes can load balance and distribute the traffic to ensure stability. It supports numerous third-party load-balancing tools.
Self-healing. Runs routine health checks and restarts or replaces containers that fail.
Declarative technology by using the desired state manager. It receives information about the cluster’s current state and sends instructions to move them toward the operator’s desired state at a controlled rate.
Allows for rolling back an application change if something went wrong
Has built-in logging and monitoring capability
In-built GUI dashboard. Makes the management of containers easier
Storage orchestration: Allows you to automatically mount a storage system of your choice, such as local storage or public cloud.
Simplified DevOps with the introduction of GitOps. Kubernetes automatically updates the deployment to match the git status in case of divergence. The git repository acts as the primary source of truth.
Kubernetes is well-suited for complex applications and large-scale deployments.

It does have some challenges

Official documentation can be expansive and complex.
Installation is complicated
Migrating existing applications can be a difficult process.
Lack of skill and training. Kubernetes has extensive functionalities and ever-increasing third-party add-ons. While this is generally positive, it can be overwhelming to folks who are starting out. The learning curve is steep.
Security is a concern.
May not be suited for simple applications and less complex workloads.
Architecture

Differences Between Virtual Machines (VM) and Containers
Medium
December 10, 2022

As the application container and virtual machine market continue to grow, there has been a tremendous interest in how they differ, and which of them is the better approach for specific projects. The keyword ‘virtual machines vs containers’ was searched 2400 times (on average) per month in the US alone [source: Google]

Virtual Machine provides an abstraction of the physical hardware. A Hypervisor allows multiple Virtual Machines (VM) to run on a single server. Each VM has a full copy of OS, app binaries, and libraries

Containers provide abstraction at the application layer. Code and all dependencies are packaged together so that an application can run across platforms such as desktop, data center, and cloud. Each container runs as an isolated process while sharing the same OS kernel.

Comparing Traditional Deployment vs Virtual Machine vs Containers
Medium
November 20, 2022

Traditional Deployment: Organizations installed and ran applications on a physical server. This approach while simple had many limitations

Cons:
-No isolation of resources
-Overutilization by one app could crash the entire system
-Scaling issues
-Long Downtimes
-Expensive to maintain physical servers


Virtual Machine provides an abstraction of the physical hardware. A Hypervisor allows multiple Virtual Machines (VM) to run on a single server. Each VM has a full copy of OS, app binaries, and libraries

Pros:
-Better utilization of resources than traditional methods
-Applications are isolated
Cons:
-OS images are heavy (GB) and have a slow bootup process
-Applications are not portable
-Not Scalable
-Can get expensive

Containers provide abstraction at the application layer. Code and all dependencies are packaged together so that an application can run across platforms such as desktop, data center, and cloud. Each container runs as an isolated process while sharing the same OS kernel.

Pros
-Lightweight (Mbs) and quick bootup
-Containers are highly portable
-Inexpensive
-Highly scalable
Cons:
-Applications are not fully isolated. Security is a concern.
-Management is critical as containers can be spun out at a rapid pace
-Skill shortage

Evolution of Containers: Past, Present, and Future
Medium
November 16, 2022

Containers are units of software that contains the code and all dependencies so that an application can run across platforms such as desktops, data centers, and cloud. It provides an abstraction at the application layer. Each container runs as an isolated process while sharing the same OS kernel. Container adoption has grown rapidly, and much faster than expected.

1970 saw the beginning of process isolation with the Introduction of Unix v7. It allowed for the segregation of file access for each process.

2004: Solaris Containers was released. It combined system resource controls and boundary separation.

2005: Open Vz was released. It provided operating system-level virtualization technology for Linux.

2016: Google launched Process Containers. It was designed for limiting, accounting, and isolating resource usage.

2008: LXC (LinuX Containers) was the first and most complete implementation of Linux container manager.

2013: Docker engine was first released. Container use has since exploded in popularity.

2014: Kubernetes was announced by Google in 2014. It is an open-source system for automating the deployment, scaling, and management of containerized applications.

2016: Kubernetes was adopted by Cloud Native Computing Foundation (CNCF) in 2016. The first major security vulnerability CVE-2016-5195 was also revealed.

2017: All major players such as Google, Docker, Red Hat, Microsoft, AWS, and VMware had adopted/ supported Kubernetes.

Present:

Kubernetes is now the gold standard. There has been an explosion of companies catering to the management of containers, CI/CD, and DevSecOps.
Growth of hybrid and multi-cloud environments
Future:

Hyper abstraction
Adoption of Serverless technologies
Focus on Security
Containers at the intelligent edge, and for mission-critical applications
Increase in complexity
Skill shortage

DevOps vs CI/CD vs DevSecOps SDLC Models
Medium
November 02, 2022

There will be more than 55.7 billion connected devices by 2025, and 75% will feature IoT connectivity which is estimated to produce 80 zettabytes of data. [Source]. IT spending in 2022 touched $4.4 Trillion [Source] and is only expected to grow further.

DevOps (2009) is an Agile methodology encompassing Development (Dev) and Operations (Ops). It enables end-to-end lifecycle delivery of features, fixes, and updates at frequent intervals. Agile adoption inherently left the Operations department behind with deployments piling up faster than they could be released. This trend ultimately pushed the rise of DevOps

Waterfall vs Agile vs DevOps SDLC Models
Medium
November 02, 2022

SDLC models have evolved over the years to meet customer and industry needs. Below table illustrates some of the differences between the three key models.

Waterfall Model (1970) provided a linear sequential approach to managing software projects. Each phase depends on deliverables from the previous one. The sequence includes Requirement, Design, Development, Test, Deploy, and Maintenance. This model dominated for more than 2 decades


Agile manifesto was created in 2001 and is now the most popular software development methodology. It is a highly dynamic and iterative approach where you do not need the complete set of requirements to start with. You can develop some features and check customer response before taking the next step. Large Scale Scrum (LeSS) and Scaled Agile Framework (SAFe) are some additional evolutions of agile methodology.

DevOps (2009) is an Agile methodology encompassing Development (Dev) and Operations (Ops). It enables end-to-end lifecycle delivery of features, fixes, and updates at frequent intervals. Agile adoption inherently left the Operations department behind with deployments piling up faster than they could be released. This trend ultimately pushed the rise of DevOps

Evolution of Software Development Life Cycle (SDLC) Models
Medium
November 02, 2022

Software Development Life Cycle (SDLC) is the process of planning, developing, testing, deploying, and maintenance of software systems. The goal is to create and deploy high-quality software that meets customers’ needs and is completed within cost and time estimates.

Key factors driving SDLC model evolution
- Accelerate software delivery, and reduce time to market
- Continuously incorporate customer feedback
- Reduce project risk and cost
- Increase productivity, collaboration, and business alignment
- Enhance software quality and security
- Frequent updates to complex infrastructure without disruptions (No-downtime)
- Increase automation.
- Personnel/Developer/Skill shortage.
- Rapid evolution of technologies: hybrid clouds, containers, Low code development platforms, 5G, IoT, AI, Big Data

Below is a brief history of how SDLC models have evolved over the years to meet industry needs.

Top 10 Technology Trends That Are Transforming Our World
Medium
October 17, 2022

Below is a list of technologies that are receiving the most interest, funding and are transforming our world. Most of them transcend applications and industries.

1.AI/ML (Artificial Learning & Machine Learning): The ability of machines to learn and act intelligently making it possible to automate complex tasks that were long thought of as impractical for machines to perform. Many people think of this field as the creation of Skynet, but we are miles away and still trying to automate complex but basic functions. We may get to a Sentient AI soon enough, but I am more interested in the plethora of applications that are still to be automated across industries.

Top 10 Biotechnology Trends
Medium
October 17, 2022

Below is a list of Bio technologies that are receiving the most interest, funding and are transforming our world.

1. #CRISPR Gene Editing is one of the most exciting technologies in biotech and pharma. It’s a technique by which genomes of living organisms can be modified precisely, cheaply, and easily. It can be used for the creation of new medicines, agricultural products, and even genetically modified organisms.

2. Personalized medicine: Traditional drugs follow a one-size-fits-all approach. Trials are used to establish which formula is the most beneficial to the widest segment of society. Remember that half of any drug advert goes towards listing the risks for the rest. With the advent of modern genomics, it’s possible to formulate medicines that are tailor-made to an individual’s unique DNA makeup. The cost of sequencing has drastically fallen from $2.7 billion a decade ago to just under $200 today. Analyzing DNA patterns makes it easier to identify the likelihood of diseases in a population segment, make early diagnoses and create targeted treatment plans. The FDA approved the first-ever gene therapy (Kymriah, 2017) that uses a patient’s own white blood cells to treat acute lymphatic leukemia.

3. AI/ML (Artificial Learning & Machine Learning): The ability of machines to learn and act intelligently making it possible to automate complex tasks that were long thought of as impractical for machines to perform. AI has helped Bio-Tech and Pharma companies to expand the scope and scale of their research by analyzing large data sets and reducing the time to market for new drugs. It helps companies develop personalized medicines that are tailor-made to individuals. Accenture calculates that by 2026, AI alone could save U.S. healthcare $150 billion annually.

Top 10 Military Technology Trends to Watch
Medium
October 17, 2022

Below is a list of technologies that are receiving the most interest, funding and are transforming militaries.

1. Unmanned aerial vehicles (Drones) are piloted either remotely or autonomously. They are already used in many applications such as military drones, photography, law enforcement, and firefighting. The price point of such drones varies from very cheap ($10-$50) to $100’s of millions depending on their sophistication and intelligence. There are currently significant investments being made in drone taxis and in military applications. Drones have completely upended military conflicts (Nagorno-Karabakh war, Ukraine-Russia war). Just wait for the cheap swarm drones that are getting much more pervasive and could easily overwhelm air defenses.

Top 10 Sites for Learning Digital Marketing
Medium
October 09, 2022

Below is a list of blogs (and sites) that allow you to get familiar with digital marketing concepts such as SEO, demand generation, marketing automation, e-commerce and digital transformation.

B2B, B2C Marketing and In-between
Medium
October 02, 2022

While some companies fall exclusively within these segments, others have product lines that cater to personas that fit into both B2B (Business 2 Business) and B2C (Business 2 Customers). Some obvious examples include companies such as Uber (Customer App and Driver App) or Yelp (Customer facing reviews and Business interface). Even some large B2B companies have a mix of products/software where customers do not exclusively behave as B2B, but rather as a combination of both. ex: One of the products could be a large expensive instrument that requires many layers of approvals and long lead times, but other products could be consumables/SAAS with lower price point that can be purchased by an individual (at a company) in a way that is similar to a B2C customers (instantaneous purchase). B2C marketing tactics and self actualization (eCommerce) are more suited in such cases.

Marketing Technology Stack (Martech)
Medium
October 02, 2022

As marketing transitions to be more digital and data centric, the number of large and small companies catering to this sector has grown exponentially from around 150 in 2011 to 7000+ in 2020. The tech stack covers the entire customer lifecycle and has accelerated marketing productivity. Below are some of the core software that I have used for demand generation in the past.

ABM (Account Based Marketing) 101
Medium
October 02, 2022

ABM is an extremely effective tactic that treats key accounts as a market of one. It allows marketing to focus on specific large companies that account for a large share of the revenue

Top 10 B2B Marketing Trends In A COVID World
Medium
October 02, 2022

COVID19 pandemic has resulted in a shift in how B2B companies market to their customers. Lets explore some of the changes and the trends for this year

Demand Generation: Marketing and Sales Collaboration
Medium
September 30, 2022

Demand generation is no longer just about MQL (Marketing Qualified Leads) or pipeline/opportunity creation. It is essential to get all the way to revenue or closed-won — this is especially true given the long lead times involved in B2B transactions. Continued focus on buyer intent, additional nurture and sales collaboration has become imperative. An integrated approach with reproducible and aligned activities across teams such as marketing, BDR/SDR, sales and customer teams are required to meet a company’s revenue objective

Top 6 Demand Generation Trends to Watch in 2022 and 2023
Medium
September 28, 2022

COVID19 pandemic has resulted in a shift in how B2B companies market to their customers. Below are top 6 trends to watch out for in 2022 and 2023
1. Increased Focus on Revenue/Closed-Won
2. Complete Alignment Between Marketing and Sales
3. Think and Plan Ahead
4. Self-Actualization
5. Social Analytics and Intent Data
6. Third-Party Cookies

List of Marketing Tactics
Medium
September 27, 2022

There are many types of marketing tactics that a modern marketer can use to promote his products, solutions and services. You must choose the right tactics that are relevant to your customers, products and industries. Pilot tactics and use data to expand those that work and stop the ones that don’t.

Better Understanding Of Pressures Faced By Agencies and In-House Marketing
demandsimplified.markering
February 24, 2018

Most companies hire one or more agencies to run either individual campaigns or complete functions. My experiences with agencies has been extremely positive, but a conversation with multiple agency employees at a conference got me thinking about the varying pressures faced by them and in-house marketing teams.

Transforming A Traditional Marketing Organization In To Digital And Data Centric
demandsimplified.markering
February 24, 2018

Traditional marketing organizations are structured by functions such as Communications, SEO/SEM, Web, PR, Partner Marketing, Field Marketing, MARCOM… and the list goes on. These functions are structured independently, and operate in silos with little to no interaction. Essentially, this type of org prevents companies from adapting quickly to business necessities, or even executing unified objectives effectively.

Is Digital Marketing So Different Across Industries?
demandsimplified.markering
February 24, 2018

A common question asked by recruiters and interviewers alike is if digital marketing experience in one industry carries over to another. Ex: How will you be able to market in ABC industry, when your experience is in XYZ industry. Fair question, but it should not be the overriding criterion for an experienced digital marketer. The reason for this is that: Digital Marketing or marketing principles remain the same across industries. That said, it is important/critical that the digital marketer read/learn about the products, and research industry specific customer behavior. The marketer doesn’t have to be a domain expert, but they should understand the field pretty well. After that, focus on the product capabilities, and the best way to convince customers. Ex: In Healthcare, you should not only market using regular channels such as SEM, Emails, Webinars…, but you should also consider advertising in science journals, and health care related 3rd party publications and tradeshows.

SEO: One of the most used but least understood terms in digital marketing
demandsimplified.markering
February 23, 2018

One of the most used but least understood terms in digital marketing is SEO (Search Engine Optimization). It is often used to justify any changes that go on a webpage. Ex: “Lets use this link to improve SEO” or “Lets stuff these keywords in the content” or “lets include this keyword on multiple pages so that we can rank higher”. All marketers understand that its important to rank high in search results, but the paradox is that all companies cannot rank #1 for all search terms. The goal instead should be to rank as high as possible (within page 1 and preferably rank #1) for specific generic keywords (even long tail keywords) that are core to the company. By default, companies should rank #1 for their branded keywords. It is easier for large industry leaders or disruptors to dislodge the top results for key generic terms (such as ‘5G’ or ‘AI’), but smaller companies entering the fray are better off targeting long tail keywords such as ‘5G for medical’ or ‘AI for cloud applications’ etc depending on the relevancy and search volume.

Sr. Director, Demand Generation and E-commerce
Wind River Software Systems
June 15, 2019

Global head of demand, growth marketing and e-commerce. Responsible for driving significant revenue and pipeline across horizontal and vertical markets. Built a robust demand framework, and teams to strategize and execute on aggressive sales objectives.
- Transformed the demand team from generating $7M pipeline/year to $150M+ pipeline/year within 2 years.
- Performance contributed to driving valuation from sub-$500M to $4B exit via acquisition.
- Launched an e-commerce initiative to automate the buying process; and for customers to self-actualize lower value orders (sub-$30K). Expected upside was around $30M+/year.
- Built and mentored a core team of 7+ marketers and multiple cross functional teams to drive integrated campaigns such as SEO, SEM, SM, Webinars, Events and Content Marketing across NA, EMEA, China, Japan and APAC regions.
- Doubled MQL (Marketing Qualified Leads) production every year while exceeding industry standard quality metrics.

CEO of Demand Market
Demand Market
September 01, 2022

Marketing – one of the last few bastions of traditional ways of doing business – is fast evolving into a much more digital and analytics centric field that is expected to be revolutionized by software and AI. Demand Market provides a practical guide to digital marketing including demand, web, ecommerce and analytics.

Generative AI Marketing Use Cases: Top 10
Medium
December 06, 2023

Content: Generate quality content that is highly specialized to a company and industry. This is one area with the biggest impact, as in the past, marketing organizations would have to hire highly proficient technical writers who understood product capabilities and industry trends. AI can now be trained with content ideas based on a set of keywords, topics, and existing assets to generate fresh content such as blogs, product descriptions, ads, and social media. This helps maintain a consistent and engaging online presence.

Personalization: Marketers can now create personalized messages that are tailor-made to individual user signals. It is possible to deliver these personalized messages at scale across multiple channels. It is a key driver for increasing customer engagement and the likelihood of conversion. Personalized content includes emails, website experience, videos, product recommendations, and more.

Targeted Advertising: AI algorithms can analyze vast amounts of customer behavior data and decipher their likelihood of purchasing. These platforms can create targeted ads that incorporate a customer's needs, history, preferences, and purchasing patterns. The ads are then delivered on a channel that the customer frequents. This automated and highly targeted ad strategy results in higher engagement and conversion rates while lowering cost per lead.

Images: Create images from text descriptions. The graphics can be generated to include a user’s preferences and with a company's brand in mind. Gen AI allows for the creation of hundreds of variations that can then be quickly tested.

Videos: Generate videos from text for training, education, and ABM (Account Based Marketing) campaigns. A single video can be customized and translated into multiple language variations. They can include a user’s preferences, a celebrity's likeness, and with a company's brand in mind.

Sales: Most CRMs now incorporate AI that allows sales to leverage Gen AI and predictive analytics to understand a prospect’s propensity to purchase and assist them throughout the sales cycle. Sellers can now automate manual tasks, decipher actionable insights from sales calls, create personalized messages, schedule meetings, and nudge customers for follow-up. The ability to respond quickly at the right time helps accelerate sales.

Increased Creativity: Gen AI helps artists explore new styles or design variations that were based on their original work. This helps increase creative cycles. They can also design new media forms without having to start from scratch.

Chat and Search: Next Gen chatbots that are context-aware can mimic human-like conversations. These AI algorithms can analyze historical data, user behavior, and preferences to provide personalized search results. They allow for 24/7 resolution of queries without the need for expensive human resources.

Analytics: AI models can analyze large amounts of data providing insights into marketing trends, campaign performance, and customer preferences. These platforms can also anticipate customer behavior, identify patterns, and predict future trends. Marketing can use this information to make informed decisions to stay ahead of the curve without the need for highly skilled technical resources.

Brand Monitoring. AI platforms can monitor social platforms, news, and other channels in real time for industry trends, brand sentiment, product placement, market conditions, competitor pricing, and customer behavior. Marketers can leverage this information to promptly respond to customer feedback, manage brand reputation, and even dynamically adjust prices.

Benefits of Using Generative AI in Marketing
Medium
November 29, 2023

Increased Efficiency: Gen AI can automate repetitive and tedious tasks, increasing the speed of execution, freeing up resources, and increasing efficiency. 58% of marketers reported increased performance as the top benefit of using Gen AI [source]

Personalization: Marketers can now create personalized messages that are tailor-made to individual user signals. It is possible to deliver these personalized messages at scale across multiple channels. It is a key driver for increasing customer engagement and the likelihood of conversion. Personalized content includes emails, website experience, videos, product recommendations, and more.

Increase Sales: Most CRMs now incorporate AI that allows sales to leverage Gen AI and predictive analytics to understand a prospect’s propensity to purchase and assist them throughout the sales cycle. Sellers can now automate manual tasks, decipher actionable insights from sales calls, create personalized messages, schedule meetings, and nudge customers for follow-up. The ability to respond quickly at the right time helps accelerate sales.

Reduce Cost: Automation saves time and reduces costs associated with manual labor, targeting inconsistencies, and lack of scalability. 50% of marketers reported cost efficiencies as the top benefit of using Gen AI [source]

Data insights: AI models can analyze large amounts of data providing insights into marketing trends, campaign performance, and customer preferences. These platforms can also anticipate customer behavior, identify patterns, and predict future trends. Marketing can use this information to make informed decisions to stay ahead of the curve without the need for highly skilled technical resources.

Creativity: Gen AI helps artists explore new styles or design variations that were based on their original work. This helps increase creative cycles. They can also design new media forms without having to start from scratch. 47% of marketers reported faster creative cycles as the top benefit of using Gen AI. [source]

Customer Satisfaction: The ability to have one-on-one interactions with customers based on their needs, interactions, and behaviors allows marketers to increase customer engagement and satisfaction.

Risks of using Gen AI in Marketing
Medium
November 29, 2023

Some of the Risks Include:

Security: Employees can upload confidential information onto platforms such as ChatGPT without realizing the consequences. As the model learns, they can incorporate the sensitive information into future results. It is important to choose a platform that prioritizes the handling of secure data in a manner that is consistent with a company’s policies.

Bias: AI platforms learn from what they have been programmed for and the data that was fed into them. Biases in programming and data are generally reflected in the results.

Privacy: The models learn from large data sets. Exposing personnel records or sensitive photos could result in them being incorporated into the results, resulting in privacy and legal concerns. Non-consent deep fake videos and images are a major concern that should be addressed.

Regulation: Governments have only recently started introducing legislation and some safeguards. They are well behind the curve. Many restrictive industries are hesitant to embrace Gen AI due to a lack of regulations governing its use. Future regulations could be a bit more severe to prevent misuse and this could limit the technology use for genuine uses.

Creative Limitations: Gen AI is not truly independent, nor does it have the emotional intelligence of humans. It understands the content of the data that it consumes and responds accordingly. It helps amplify human abilities. Many of the Gen AI applications use the same underlying models and are trained on similar datasets resulting in a lack of differentiation.

Generative AI Marketing Statistics
Medium
November 29, 2023

Marketing has seen an explosion of innovation reshaping the field. Generative AI is one such game-changer technology that allows today’s marketers to stay ahead of the curve. It allows for innovative solutions to age-old problems by automating repetitive tasks, increasing scalability, personalizing interactions, and enhancing creativity. While 98% of executives said that Gen AI was a hot topic of discussion with their board [1], only 14% of organizations were using Gen AI for Marketing and Sales [4]

Kubernetes Architecture
Medium
December 29, 2022

Components:

Cluster is the highest level of abstraction in Kubernetes. It contains all the nodes, pods, and a master.

Master Node/Control Plane controls the deployment of pods and hence the worker nodes. It is responsible for ensuring that the cluster attains a desired state that is defined by operators in a declarative manner.

API Server is responsible for handling external and internal requests, and determining if a request is valid or not before processing it

Etcd stores the overall state and configuration of the cluster at any given point in time.

Scheduler distributes unscheduled pods across the available worker nodes. It tracks and ensures that the workload is not scheduled in excess of available resources.

Resource Controller is a control loop that monitors and regulates the state of clusters. It receives information about the cluster’s current state and sends instructions to move them toward the operator’s desired state.

Worker Nodes are physical or virtual machines that can run pods as part of a cluster

Pods are an abstraction that represents a group of one or more containers and configurations that govern how they should run. Each pod is assigned an unique IP address that allows applications to use ports without the risk of conflict.

Container Engine such as Docker is responsible for running the containers.

Kubelet receives direction from the control plane and is responsible for starting, stopping, and maintaining containers organized into pods.

Proxy is Responsible for routing traffic to the appropriate container based on IP and port number.

Service defines a logical set of pods and policies about who can access them. A service allows Kubernetes to route traffic to an application regardless of where the pod is running.

Virtual Machines vs Containers Table
Medium
December 10, 2022

Virtual Machine provides an abstraction of the physical hardware. A Hypervisor allows multiple Virtual Machines (VM) to run on a single server. Each VM has a full copy of OS, app binaries, and libraries

Containers provide abstraction at the application layer. Code and all dependencies are packaged together so that an application can run across platforms such as desktop, data center, and cloud. Each container runs as an isolated process while sharing the same OS kernel.

Table Comparing Traditional Deployment vs Virtual Machine vs Containers
Medium
November 20, 2022

Traditional Deployment: Organizations installed and ran applications on a physical server. This approach while simple had many limitations
Cons:
-No isolation of resources
-Overutilization by one app could crash the entire system
-Scaling issues
-Long Downtimes
-Expensive to maintain physical servers

Virtual Machine provides an abstraction of the physical hardware. A Hypervisor allows multiple Virtual Machines (VM) to run on a single server. Each VM has a full copy of OS, app binaries, and libraries
Pros:
-Better utilization of resources than traditional methods
-Applications are isolated
Cons:
-OS images are heavy (GB) and have a slow bootup process
-Applications are not portable
-Not Scalable
-Can get expensive


Containers provide abstraction at the application layer. Code and all dependencies are packaged together so that an application can run across platforms such as desktop, data center, and cloud. Each container runs as an isolated process while sharing the same OS kernel.
Pros
-Lightweight (Mbs) and quick bootup
-Containers are highly portable
-Inexpensive
-Highly scalable
Cons:
-Applications are not fully isolated. Security is a concern.
-Management is critical as containers can be spun out at a rapid pace
-Skill shortage

Evolution of Container Technology
Medium
November 16, 2022

1970 saw the beginning of process isolation with the Introduction of Unix v7. It allowed for the segregation of file access for each process.

2004: Solaris Containers was released. It combined system resource controls and boundary separation.

2005: Open Vz was released. It provided operating system-level virtualization technology for Linux.

2016: Google launched Process Containers. It was designed for limiting, accounting, and isolating resource usage.

2008: LXC (LinuX Containers) was the first and most complete implementation of Linux container manager.

2013: Docker engine was first released. Container use has since exploded in popularity.

2014: Kubernetes was announced by Google in 2014. It is an open-source system for automating the deployment, scaling, and management of containerized applications.

2016: Kubernetes was adopted by Cloud Native Computing Foundation (CNCF) in 2016. The first major security vulnerability CVE-2016-5195 was also revealed.

2017: All major players such as Google, Docker, Red Hat, Microsoft, AWS, and VMware had adopted/ supported Kubernetes.

Present:

Kubernetes is now the gold standard. There has been an explosion of companies catering to the management of containers, CI/CD, and DevSecOps.
Growth of hybrid and multi-cloud environments
Future:

Hyper abstraction
Adoption of Serverless technologies
Focus on Security
Containers at the intelligent edge, and for mission-critical applications
Increase in complexity
Skill shortage

SDLC Models Evolution (1)
Medium
November 02, 2022

Software Development Life Cycle (SDLC) is the process of planning, developing, testing, deploying, and maintenance of software systems. The goal is to create and deploy high-quality software that meets customers’ needs and is completed within cost and time estimates.

SDLC Models Evolution (2)
Medium
November 02, 2022

Software Development Life Cycle (SDLC) is the process of planning, developing, testing, deploying, and maintenance of software systems. The goal is to create and deploy high-quality software that meets customers’ needs and is completed within cost and time estimates.

Waterfall vs Agile vs DevOps SDLC Models | A Comparison
Medium
November 02, 2022

DLC models have evolved over the years to meet customer and industry needs. Below table illustrates some of the differences between the three key models

https://abhaykarthik.medium.com/waterfall-vs-agile-vs-devops-sdlc-models-8cc11b1a6edc

SDLC Models Evolution (Animated)
Medium
November 02, 2022

Evolution of Software Development Life Cycle (SDLC) Models (Animated Infographic)

Top 10 Technology Trends That Are Transforming Our World
Medium
October 17, 2022

Below is a list of technologies that are receiving the most interest, funding and are transforming our world. Most of them transcend applications and industries.

1.AI/ML (Artificial Learning & Machine Learning): The ability of machines to learn and act intelligently making it possible to automate complex tasks that were long thought of as impractical for machines to perform. Many people think of this field as the creation of Skynet, but we are miles away and still trying to automate complex but basic functions. We may get to a Sentient AI soon enough, but I am more interested in the plethora of applications that are still to be automated across industries.

Top 10 Biotechnology Trends to Watch
Medium
October 17, 2022

1.CRISPR Gene Editing is one of the most exciting technologies in biotech and pharma. It’s a technique by which genomes of living organisms can be modified precisely, cheaply, and easily. It can be used for the creation of new medicines, agricultural products, and even genetically modified organisms.

Top 10 Military Technology Trends to Watch
Medium
October 17, 2022

1.Unmanned aerial vehicles (Drones) are piloted either remotely or autonomously. They are already used in many applications such as military drones, photography, law enforcement, and firefighting. The price point of such drones varies from very cheap ($10-$50) to $100’s of millions depending on their sophistication and intelligence. There are currently significant investments being made in drone taxis and in military applications. Drones have completely upended military conflicts (Nagorno-Karabakh war, Ukraine-Russia war). Just wait for the cheap swarm drones that are getting much more pervasive and could easily overwhelm air defenses.

Top 6 Demand Generation Trends
demandsimplified.markering
September 28, 2022

While the 2021 demand trends in a COVID world continue to stay relevant, I wanted to highlight some new ones to watch out for in 2022 and 2023.

Top 10 Demand Generation Trends Post COVID
demandsimplified.markering
September 28, 2022

COVID19 pandemic has resulted in a shift in how B2B companies market to their customers. Lets explore some of the changes and the trends for this year

Top B2B Marketing Tactics
demandsimplified.markering
March 01, 2018

Top marketing tactics that are best suited for B2B companies.

Top B2C Marketing Tactics
demandsimplified.markering
March 01, 2018

Top marketing tactics that are best suited for B2C companies.

Alchemist Accelerator Mentor
Alchemist Accelerator.com
September 01, 2022

Mentor program to coach startup founders. Counseling on GTM, ecommerce, product and demand generation.

Wind River Website and E-Commerce
Wind River Software Systems
June 01, 2021

- Launched the new windriver.com website with advanced CMS, search, brand, SEO and content.
- Launched an e-commerce initiative to automate the buying process; and for customers to self-actualize lower value orders

Takarabio Website and E-Commerce
Takarabio
January 01, 2019

Launched takarabio.com website with advanced ecommerce, responsive content, licensing, compliance, live chat and ABM (personalization).

Bio-Rad Genomics Website
Bio-Rad
June 01, 2017

Responsible for strategy and execution of all marketing campaigns within Bio-Rad Gene Expression business unit (8 medical Instruments and 100k+ consumables). Drove double digit revenue growth by leveraging integrated marketing campaigns, e-commerce, and transforming the genomics website