First Growth Phase: The Hard Work
As soon as starting a platform, its essential to determine its minimum critical network to be successful. This applies especially to creating social platforms; you should start small and focus on growing the required network size to launch them successfully.
Depending on the kind of platform being launched, its minimum-need network size may differ accordingly.
Apps such as Slack or Google Workspace should only need to be launched across one team or company in an organization; when it comes to networks or payment services, however, you may require more outreach.
An excellent example is Bank of Americas rollout of their first credit card; acceptance was first required in Fresno, California, to build a network and properly align users and retailers.
Critical networks remain consistent regardless of size, regardless of whether there are several users or several thousand.
You should attempt to size up your first network before expanding until reaching a point where its effects allow your business to thrive and flourish. At this stage, establishing and replicating your first networks will require much manual effort and personal persuasion.
Free platforms or parts may help remove entry barriers for users who find joining too tricky; doing so is particularly critical if they possess more significant potential and contribute more. Tinder sent famous friends as an example of promoting their app at events.
Understand and manage the "hard sides."When creating a platform, it is essential to recognize that those who bring the most outstanding value are those on its "hard" side.
Depending on the platform chosen, this may mean selling to sellers in a marketplace, attracting beautiful women via dating apps, video content creators or power users with office software as examples of hard workers.
The value proposition is critical when building a platform since it will determine its core value proposition and become the focal point when expanding.
To ensure maximum user satisfaction, keep this aspect happy as part of your growth strategy.
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Platforms At The Tipping Point - Organic Growth
Tinder, LinkedIn, Airbnb, Dropbox and Reddit all reached their tipping points differently, each example highlighting the significance of engaging users to reach critical mass and reach a tipping point once a network or platform achieves critical mass, growth and spread become much more straightforward - whether organic growth in users or through tactics such as an invitation-only strategy or referral mechanisms.
Engagement of users is the cornerstone of reaching tipping points in networks and platforms. Tinder hit its tipping point by hosting parties between famous college students; LinkedIn used an invitation-only strategy that permitted users to invite people they believed might be interested; Dropbox employed an effective referral program, which both gave back and increased signups.
Understanding tipping points will enable businesses to successfully develop products that will reach large audiences. By employing invite-only strategies and viral content production, they can increase user activity on their platforms - reaching tipping point status while drawing users.
Growing and scaling Platforms
Platforms that have successfully launched and reached their tipping point now enter a crucial stage of their development - scaling up.
When expanding a platform, it is vital to recognize three types of network effects.
Engagement/ Acquisition in Engagement of Contract
Economic As products gain traction in their network, the "Engagement Effect" is essential in spreading them further.
More users become engaged with the platform through referrals from colleagues and use it in different situations. Overuse may lead to closed loops; for example, people sharing photos that require signing in exclusively on Facebook.
With increased engagement comes increased activity from inactive or discontinued users returning.
This " Acquiring Effect " could be described as viral growth due to organic usage within a network. This effect becomes particularly effective when coupled with features that encourage collaboration/invitation; the more influential the viral effect, the more quickly the word of the product will be spread.
Economic effects can be challenging to comprehend as potential problems could arise before a platform is launched.
Network effects or business models rely heavily on having more networks connected; ad revenue will only come in with more excellent networks attracting ads; premium pricing leads to even further revenue opportunities.
Also Read: How Much Does Mobile App Development Cost to Develop a Fitness and Workout App?
The Ceiling On Growth
After experiencing rapid expansion and market share gains, even the most successful platforms can reach a plateau.
While it is impossible to avoid "flattening entirely", many believe this to be an endless journey - such as with Facebook having attracted most Western users already and little further gains to be had there from them - daily innovation needs to take place if users are to be engaged and monetized effectively. There are many reasons for platforms reaching their capacity limits and experiencing hockey stick growth that appears to stop short of reaching their full potential.
Saturation
Saturation is one of the greatest threats startups face as they reach their growth plateau. Once popular products reach a critical mass of users, it becomes harder for growth to continue at its current rate due to fewer new customers signing on and less effective marketing channels that were once efficient becoming less so over time.
Growth eventually begins to decline to more manageable levels.
A Revolt In The Network
Every platform has a minority that contributes significantly to its network and, therefore, the platform as a whole.
When these users recognize their influence and value, a revolt may follow when they realize their importance and value to the network as a whole. When businesses grow more significantly than anticipated, platforms become a source of disruption; Uber drivers were an excellent example of this phenomenon as they demanded better prices and benefits.
The Dynamics Of
Once a product becomes mainstream, its target audience can expand rapidly. But as it becomes popular among users, its dynamics change and user experience becomes less desirable; as its network grows, it becomes harder for original uniqueness to remain.
Overcrowding
Overcrowded platforms can impede their development. Too many users clogging a platform makes it harder to locate relevant content and people, leading them to abandon it and thus hindering its growth.
Search functionality, algorithm feeds, or curation features may offer solutions; startups prioritizing bottom-up marketing (i.e. focusing on smaller customers first) may reach this ceiling more quickly than others.
Keep Competitive Edge - Economic Moat
Warren Buffet popularized a concept known as the competitive moat, or an area of superior strategic value that can be protected.
Most platforms can be easily replicated; only their network and trust infrastructures present difficulty. Both factors help defend platforms against attacks and can ultimately add strategic value for large platforms in the long run.
The Advantages And Disadvantages Of Large Networks
Each company competes for access to similar networks, with small firms often having distinct advantages due to speed and lack of "sacred cows".
However, maintaining their market position on larger platforms may be more straightforward due to established relationships with customers, experienced employees and already-tested products on the marketplace. This could result in market changes where smaller competitors take over large portions of a community from larger rivals (Facebook overtook Myspace; AirBnB surpassed Craigslist, etc.).
It also means their strategies must be managed carefully to remain effective health and fitness mobile app development.
Cherry Picking And Defending Networks
Cherry-picking in platform development is not uncommon. While its effects are often prominent and readily accessible to everyone, smaller platforms must pay special attention to scraping and building their network; more extensive networks typically find it more challenging to defend all their members.
This makes taking "cherries" much more straightforward for smaller startups.
Managing Expectations
Big platforms offer many advantages to smaller firms in that they can launch products more rapidly, with greater ease, and more effectively than an unknown firm.
Unfortunately, this may lead to unexpected consequences as users may have high expectations but fail to recognize its true worth - an example being Google+, where user expectations outstripped performance; 90 million people signed up within one week despite engagement levels so low that many never even spent any time using the platform itself!
Bundling To Maximize The Value Of Your Network
Bundling services and products allow established platforms to launch new platforms more rapidly. By connecting an established network with the new platform, the question of "How do we launch a brand-new platform?" can easily be overcome.
With its combination of Word and Excel in one package, Microsoft Office stands as an exemplary product of this trend. Furthermore, the company tried to ensure interoperability among Office applications - the rest is history!
Why Does A Business Need To Scale Up Software Delivery?
Businesses of all sizes realize the significance of scaling software delivery for business success, whether or not the global software epidemic exists.
Scaling can meet their businesss growing needs while speeding up responses to production errors through DevOps; code updates must also be delivered instantly into test environments and production. Though some companies can achieve this with continuous integration/deployment (CI/CD), this feat sometimes remains impossible even with this practice.
Why?
Businesses that experience intermittently high traffic, like e-commerce websites or health apps, must respond swiftly and provide necessary assistance on time when requested by users.
Businesses that fail to do this become increasingly vulnerable as they expand. They may not be able to reduce time-to-market or confirm changes quickly enough - just one consequence arising from insufficient scale.
There may also be many others.
The Top Challenges in Delivering Software at Scale
Large teams working within a distributed system face several unique challenges. Multi-team environments create interdependencies, which increase complexity.
All this leads to ineffective software delivery processes. Here are the challenges teams encounter when developing software at scale.
Developer Burnout
Burnout is common among developers working within inefficient systems. If theres a critical bug that needs fixing by Monday, developers may take on additional responsibilities over the weekend to find its root cause before Monday arrives - this process can become very laborious and cumbersome.
Technical Debt
Engineering teams often rely on quick fixes, pre-made solutions or quick implementation techniques to speed up release cycles; unfortunately, this often results in a large technical debt that must later be paid back.
When theres insufficient planning and organization within teams, this situation often arises as technical debt becomes an unproductive drain on productivity.
Products of Poor Quality
Teams that fail to adhere to best practices during deployment could create applications and microservices of subpar quality, whether due to inadequate resources, poor communication between team members, unrealistic timelines, or a lack of domain expertise as culprits.
This results in low-grade software.
Release Of Potentially Risky Software Releases
Releases of software can be unpredictable and full of risks. Things dont always go as planned during or after release, even after it has been successful.
These risks could include poor-quality releases, increased production costs, incorrect deadline estimations, or miscalculated completion dates; these potential issues could increase the time required for software release if left unaddressed.
Frequent Production Faileds
Failure in production can have catastrophic repercussions for an organization. , for instance, the Hawaii Emergency Management Agency sent out a false alert regarding a possible missile attack in Hawaii due to someone making an innocent mistake, leaving residents confused and fearful for their lives.
Therefore, it is vital for companies to address configuration, application or other production-related issues during runtime to maintain continuity of production services.
Error Resolution May Take More Time
As teams must work faster, errors become more likely. Delivery teams attempting to monitor code flow manually could take much longer to deliver their products due to the lack of an effective system for investigating and solving problems, which may lead to more extended downtime.
SREs Who Are Frustrated
SREs can become easily frustrated when applications or services go offline for extended periods. The responsibility to ensure applications and services run seamlessly for customers places SREs under unnecessary strain, resulting in unnecessary stress for themselves and unnecessary pressure from them for not meeting expectations.
Slow Time-to-Market Organizations who do not adhere to the deployment of applications will find their time-to-market decreased because failing to adhere to compliance requirements can delay product releases to market.
Reducing time-to-market can have many advantages for companies: competitive edge, reduced R&D expenses, enhanced customer experience and even increased revenue can all be seen from this one step!
Operational Cost Challenge
Generative AI models tend to consume significant power. OpenAI has yet to reveal the size and parameter count of its GPT-4 model; all estimates indicate it contains at least five times as many parameters as its predecessor, GPT-3 (which already had 175 billion parameters).
As part of an inference process, creating answers to prompts requires multiple forward passes, leading to steep inference and training costs for ChatGPT. Training costs have been estimated at between $4-5 million, while recent analysis indicates an inference cost estimated at approximately $700,000.
Length of Prompts for AI Generative Models The length of prompts plays an integral part in determining the price of AI generative models, with longer prompts providing richer context and personalizing responses, improving accuracy and performance across various applications.
Longer prompts also increase inference costs; GPT-4 doubled its operation costs per token sampled when moving from 8K up to 32K context windows, underscoring this relationship between prompt length and cost.
Training, scaling and scaling AI apps is often prohibitively expensive, limiting their deployment to larger enterprises with virtually unlimited resources.
Many smaller businesses have innovative ideas but limited budgets, thus limiting them from fully capitalizing on generative artificial intelligences transformative power.
Environmental Impact and Sustainability Challenge.
Generative AI apps high energy use is another cause for concern when it comes to environmental sustainability. Recent research has exposed the significant contribution of the Information and Communications Technology (ICT) sector to global greenhouse gas emissions, estimated between 2-4%.
As modern massive generative models require significant energy consumption, their carbon footprint increases rapidly with each expansion in size or scale of these machines.
How to Scale Generative AI Models
Reduced operational and environmental costs are associated with the scaling of generative fitness and health app development AI.
This requires a multifaceted approach. We explore below three promising paths.
Building Specialized Models
Generative AI models have evolved significantly, becoming smaller and computationally more efficient while not compromising performance.
One promising approach to achieve this is by optimizing models specifically for specific tasks or applications - they will require less computation time and can train on smaller datasets more effectively than their counterparts.
Imagine large general models as Renaissance Scholars with broad but perhaps superficial knowledge bases. In contrast, specialized models could be seen as modern experts with deep domain knowledge who may provide more effective solutions than Renaissance scholars at solving todays most pressing problems.
While large models may produce impressive output capability, they may not always offer optimal solutions in each situation.
Specialized models provide practical inference speed and cost advantages that can help scale generative AI. We can develop more sustainable and cutting-edge solutions through developing these specialized models.
Acceleration Of Inference Through Quantization And Compliment
Quantization and compilation techniques can help optimize generative models, thus lowering costs while increasing sustainability.
Quantization can significantly decrease memory requirements and computation costs by decreasing the numerical precision of models without experiencing significant performance loss, cutting the cost of inference considerably for both specialized and general generative models using this technique.
Compilation involves translating neural network representations (typically expressed as high-level operations and layers) to formats suitable for running on specific hardware types (CPUs, GPUs or AI accelerators).
This involves optimizations such as operator fusion to reduce computational complexity for faster inference with reduced latency.
AI applications that utilize faster inference capabilities may experience more excellent scaling. When processing more queries per second, cloud costs decrease, and applications using models with rapid inference abilities better use every dollar invested in computing power.Additionally, faster inference helps to lower carbon emissions from generative AI by decreasing GPU processing times and thus consuming less kW power, leading to lower power usage costs, which in turn lower CO2 emissions.
Solution Needs for Generative AI Optimisation & Scaling
Companies can reduce operational costs and the environmental impact of scaling AI models by employing these strategies.
Though creating and implementing complex models requires a high level of technical knowledge and is time-consuming for machine learning experts with deep learning expertise, rapid and efficient solutions must be found quickly to reduce risk, accelerate development timelines and protect companies from losing market opportunities or their competitive edge.
Also Read: Fitness & workout app tech stack - possible options 2023
What is the ISD Solution?
ISD is an application built upon Spinnaker that provides services like verification gates, policy enforcement and approval gates.
Over 40 tools like GitHub Jenkins DockerHub can easily be integrated. Furthermore, multiple cloud deployments can be easily managed using ISDs orchestration module, which runs on Spinnaker as its foundation.
ISD allows DevOps teams to create flexible pipelines quickly, while users can add new tools, clusters or pipelines as required.
Being easily scalable makes ISD ideal for businesses wishing to customize the system according to their requirements.
ISD Platform Benefits
Implementing ISD is a tool for continuous delivery.
Automated Pipelines that Enable Software Delivery in Multi-Cloud Quickly
ISD allows users to streamline delivery workflows with easily created pipelines. This tool simplifies configuration for users and automatically updates pipeline versions as required.
Users can manage thousands of pipelines while enforcing policies in each one - providing live visibility during execution and diagnostic capabilities.
How To Roll Out
DevOps can approve delivery pipelines faster with ISD. Developers can resolve issues faster by allowing teams to receive instant feedback.
Make Faster Decisions
ISD gives developers access to important insights about their applications health, such as which pipeline is the fastest, slowest or if there are any errors.
The data gathered gives developers more confidence in making decisions about fitness and wellness app development solution.
Eliminate risks
ISD allows developers to minimize risk scenarios by automatically monitoring the risks of each software change before releasing them into production.
Teams can also eliminate risk by following security best practices at all stages of deployment and using user authentication to secure the release pipelines.
Track Delayed Pipeline
ISD gives you real-time insight into all of your pipelines and applications. Compliance managers can identify the who, what and when of pipelines and apps with audit reports.
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The Conclusion Of The Article Is:
Continuous Delivery allows for faster software release cycles while decreasing deployment risk, increasing time to market, and improving developer efficiency.
Building a network platform can be challenging, as it involves cultivating trust among users, developers, and partners - not an easy feat - and creating an ecosystem of users, developers, and partners. When reaching the tipping point where users and developers find your platform indispensable, scaling becomes essential to maintaining market share.
However, there is only so much room before it hits an eventual ceiling; most successful platforms protect themselves by continually innovating while capitalizing on "their moat."
