AWS offers over 200 services making cloud computing challenging; therefore, over 73% of enterprises have a central cloud team for guidance and best practice recommendations. On the other hand, startups do not have the financial resources to hire expensive cloud experts facing cloud adoption challenges.
Here are some common struggles startups face with cloud computing:
Unoptimized usage: Startups may not fully utilize the resources they are paying for, resulting in inefficiencies and higher costs.
Lack of cost-control tools: Startups may not have access to the tools and processes needed to manage and optimize their cloud computing costs.
Over-provisioning: Startups may provision more resources than they need in order to ensure they have sufficient capacity, leading to wasted resources and higher costs.
Inadequate monitoring: Startups may not have the tools and processes in place to monitor and track their cloud usage and costs, making it challenging to identify areas for optimization.
To overcome these challenges, startups can take several steps to manage their cloud computing costs, including:
Accurately estimating their usage and resource needs.
Optimizing their usage through automation and efficient resource management.
Implementing cost-control tools to monitor and manage costs.
Right-sizing their resources to ensure they are not over-provisioned.
Continuously monitoring their cloud usage and costs to identify areas for optimization.
We at CRE8IVELOGIX have thought about these challenges and have worked hard to make it effortless for startups to adopt the cloud with their product XLER8R. Using XLER8R, startups can fast-track their cloud adoption, effectively manage their cloud computing costs, and ensure they are getting the most value from their investment.
In this blog post, we will discuss machine learning. If you are just beginning your machine learning journey read on.
Machine learning is a field of artificial intelligence in which we focus on problems that can not be solved using traditional instruction-based algorithms. Instead, we build algorithms that learn based on past experiences without being explicitly programmed. Hence the name machine learning.
Suppose you are a data scientist trying to solve a problem where an email needs to be identified as spam or non-spam.
Traditionally, you might solve these problems by writing complex algorithms and data analysis rules. However, in the case of emails, the number of possibilities is so large that it becomes increasingly difficult to write rules that can accurately predict outcomes.
That’s where machine learning comes in. With machine learning, you can feed large amounts of data into a machine learning model and let it learn patterns and relationships on its own. The model can then use those patterns and relationships to make predictions or decisions based on new data.
One way to think about machine learning is to compare a machine learning model to a student and the process of training a machine learning model to the process of teaching a student.
Like a student, a machine learning model starts with little or no knowledge about the problem space. It learns by being exposed to data, just like a student learns by being exposed to new information.
We train machine learning models by using a large amount of data and tweaking their parameters based on the accuracy of their results. This process is similar to a teacher presenting new information to students and providing feedback on their progress.
Just like students become more knowledgeable and skilled as they continue to learn and practice, machine learning models continue to learn from data and become more accurate at making predictions.
There are some advantages machine learning algorithms have over humans. For one, machine learning models can process significant amounts of data faster than humans.
As a limitation, machine learning models can not think critically or creatively. However, they are evolving rapidly to be very close to human capabilities. On the other hand, humans can think creatively and adapt to new or unknown situations.
Overall, the relationship between machine and human relations is complex, but it is clear that both have their strengths and limitations. By combining the power of machine learning with the creativity and critical thinking of humans, we can solve complex problems and make better decisions.
In summary, machine learning has many practical applications, including improving customer experiences, optimizing business processes, and even making medical diagnoses. It’s a powerful tool that can help you find insights and make predictions in a way that would be impossible with traditional programming techniques. So, if you want to solve complex problems with data, reach out to CRE8IVELOGIX Inc. We have experienced machine learning experts who can help solve your complex machine learning problems.
In this Blog, we will explore why organizations should move to the cloud, what challenges they face and how well-architected CDK from CRE8IVELOGIX can make AWS effortless. Imagine you are part of an organization running its workloads in a traditional data center or on-prem environment. One day you meet a colleague who is a cloud enthusiast and speaks highly about the advantages of building and running cloud-native applications. He also talks about the long-term cost savings associated with running applications in the cloud. Even though the conversation sounded convincing, you think to yourself, why would someone spend the time, effort, and risk of migrating a perfectly running application in an on-prem environment?
Benefits of migrating to the cloud:
Your friend gives you a run down about the cloud benefits and how they all tie down to a single benefit that is cost-saving of up to 80%. When your applications are running in the cloud, there is
No upfront cost: You can sign up and start using available services without paying a dime.
No over-provisioning: You can start with what you need and configure autoscaling to handle the increase in demand.
No hardware maintenance: Cloud provider handles all the hardware maintenance for you.
On-demand availability: Most of the services are available as needed.
Elasticity and Reliability: You can scale to hundreds, even thousands of servers, within minutes.
Global expansion: If the users of your application are spread around the globe, you can achieve global expansion in minutes.
In reality, achieving all of the above is possible in an on-prem environment, but it would cost so much that very few have the budget and resources to do it. On the other hand, the cloud provides all these features out of the box on a pay-as-you-go model.
Eventually, your friend convinced you that getting all these benefits without spending a fortune makes the cloud an attractive option. Whether you are a startup building a green field application or an enterprise migrating an existing application, you can save as much as 80% of the cost of running in an on-prem environment. Running applications in the cloud saves cost and empowers your team to experiment at will, increasing their agility in delivering business differentiating features.
Problems which organizations face when migrating to the cloud:
Cloud development seems relatively straightforward, where cloud providers provide the building blocks in the form of storage, compute, database, and other services, whereas customers build business applications by combining them in meaningful ways.
Although this looks easy on the surface, if you look deep, each of these services needs to be appropriately configured to conform to the well-architected pillars such as security, reliability, performance, operational excellence, and cost-effectiveness. This requires the teams to have in-depth knowledge about configuring these services, shifting their focus from building application features. If your application uses microservice architecture, a better approach is to have smaller teams that focus on their piece of the microservice domain. As a downside, each team could reinvent the wheel and configure cloud services differently, resulting in inconsistent usage across the organization.
After familiarizing yourself with the challenges, you quickly realize the need for a common framework to address these design inconsistencies across different teams. This framework should provide security, operational excellence, reliability, and cost-effective use of services out of the box. In addition, this framework should make it easy to provision well-architected infrastructures for the most common patterns like building Rest APIs and websites.
How well-architected CDK from CRE8IVELOGIX can eliminate inconsistencies and make cloud migration easy:
By utilizing such a framework, teams across the organization should be able to accelerate their application development in a consistent way. However, building such a framework requires experience and in-depth knowledge of cloud services and would require months to develop. Because of the rapidly changing cloud landscape, a dedicated team is essential to keep this framework up to date.
To help startups and enterprises alike, CRE8IVELOGIX Inc. provides a framework called Well Architected CDK. This framework is built by highly experienced architects and developers who enhanced the CDK provided by AWS by adding security, monitoring, scalability, and compliance layers.
Why CRE8IVELOGIX Well-Architected CDK is the best option for cloud migration
Well-Architected CDK also provides patterns that allow developers to spin up commonly used infrastructures like Rest APIs in minutes based on best practices and AWS recommendations. Instead of spending weeks writing infrastructure code, developers only have to write 3-5 lines of code, whereas Well-Architected CDK generates 1000s of lines of code behind the scenes to provision an infrastructure that is well-architected.
It gets even better when a new feature or enhancement is introduced in the well-architected CDK; the users only have to update the version to get all the added enhancements. If you are starting a digital transformation project, CRE8IVELOGIX Inc can partner with you to help fast-track your project by architecting and building a cost-effective solution using well-architected CDK.
Perfectionism means a desire to produce something highly remarkable, exceptional, and unprecedented. It means holding such high standards for yourself that achieving them becomes impossible.
A lot of people confuse perfectionism with giving your best shot; these are two different things. Trying your best in the amount of time you have is good. But trying to achieve an unrealistic skill level or perfection sets you up for failure from the very start. It’s good to have confidence in your abilities and skillset. Still, when you set perfectionism as your goal, you always fall short of your expectations and keep disappointing yourself until you decide to quit your project.
How does perfectionism hold you back?
You feel you’re not good enough:
To succeed and improve yourself in any domain, you constantly need proof that you were born for it or that you are naturally talented to do it well. Since a perfectionist always thinks that his next painting will be a Mona Lisa painting or his next startup will be as innovative and famous as “Facebook, Linkedin, Uber,” he always falls short of his expectations. Although he might’ve written a good book or developed a good application, he doesn’t see any brilliance, magic, or excellence in his creative work. He compares it to the creation of people who’ve spent years honing their craft and gets disappointed. He feels he’s never going to be good enough. Frequently, he’d not even publish that work since he sees it with contempt and disdain. When he doesn’t publish his work, he never learns where he stands and lives in a distorted version of reality in which he’s not good enough.
It holds you back from taking risks:
You always take advantage of templates and themes when you want to build the perfect pitch deck or the most attractive website. The reason is that you fear that going for custom code may end up with something that is not per your expectations and high standards. Although this way, you might build something which appears attractive, it won’t be nearly as engaging as the one you’d have created if you had experimented a bit.
Experiments often result in time wastage. However, experiments and risks cause perspective shifts; We learn new ways of doing things and better ways of doing things. We learn about what works best in our case rather than for everyone. For instance, a person might read on the internet that meditation works best for calming one’s mind. But, he might never find meditation effective. He’d have to try several things like hanging out with friends, reading, and listening to music to learn what calms his mind. Maybe he can shift towards deep meditation practices after learning to focus more after engaging in these relatively easy activities.
On the other hand, if he were a perfectionist, he’d want to perform meditation or complex yoga exercises flawlessly as step one for focusing and will get infuriated when his mind would not stop negative thoughts and become as peaceful as 5am. He’d spend hours researching these exercises on the internet before even trying for the first time. He’d want to get every step right as per the instruction manual. But when he would fail to perform precisely like those yoga or meditation gurus, who have spent years practicing, he’d again feel like an imposter, a good for nothing.
Unexpected change of plans or requests put you to stress:
Suppose you’re a video editor. You’re editing a video. You have a deadline approaching, expecting all the video clips to be stable. But, just at the last hour, you learn some clips weren’t as stable or smooth as you’d want them to be.
At this point, the perfectionist would want to shout, scream, tear off his hair, bump his head against a wall, curse himself, remind himself of the times he has screwed things up before, type up a message to the client that he won’t be able to deliver on time and will need to re-do the whole video. Meanwhile, the client can’t offer any more extensions, and he’d most probably cancel the project when he reads his message.
If, on the other hand, this video editor was not a perfectionist, he’d realize that screw-ups are a part of life; He has learned an important lesson from this experience. He’d improve next time and pay special attention to stability when recording videos. He’d then work on a solution or a workaround, like using a warp stabilizer or slowing down the clip to make it more stable. What he won’t do is “Play the victim!”
So a perfectionist has an “All or nothing” mindset, where he believes in either delivering a project precisely in accordance with his vision or not delivering anything at all.
Perfectionism makes you live in constant stress and worry:
Even if a perfectionist musters up the courage to deliver a project, he’d still be looking for examples proving that his work could’ve been better.
He’d not be happy to see that he has progressed significantly from where he was before. Even when his clients are happy with his work, He will make up future catastrophe scenarios where his work is being mocked or ridiculed by others. When he sees someone doing something in a better way and then he gets infuriated at himself for not taking that route or course of action. He’d never be able to look at his work with pride. He’d rob himself of the satisfaction of giving something your best shot. He’d always find tiny mistakes in his grammar, punctuation, or methodology. He’d be his own worse critic, and his constant self-criticism will soon lead him to abandon his project, which he was once passionate about.
How to battle perfectionism
Publish the work after giving your best shot. Stop listening to the voices inside your head that say, “let’s give it another go,” even after several attempts and proofreading or reviewing it repeatedly.
Tell yourself that whatever the consequence may be, you’ll learn something from it, move forward, and gain momentum, and rejection doesn’t mean the end of the world. You’ll still get more and better opportunities even if your mind tells you that if this project goes south, the ground will open up and swallow you whole.
Redefine your success metrics; For instance, a success metric could be “I have to make it better than the last time.” “I’d consider it a win even if one person gets inspired by this video.” “I’d consider it a win even if I read two pages today.” “I’d consider it a win even if I send a well-articulated and well-researched project bid today.”
Stop comparing yourself to everyone who appears successful on social media. Your only competition should be with yourself.
Take risks, and be open to failures. You should tell yourself that taking fewer risks might mean success in the present. Still, it won’t prepare you for dealing with diverse, challenging situations where a knowledge of different methodologies, techniques, or skill sets is required. When you take risks, you learn multiple ways of doing things, allowing you to choose the most optimal way of doing the assigned task or achieving a required outcome.
Don’t beat yourself up. Realize you’re a work in progress. Tell yourself of all the things you’ve been able to do before remarkably well. Tell yourself you did a good job. Maybe take yourself out to buy icecream to celebrate your first attempt.
Ultimately, I’d like to sum it up by saying, “Fall in love with the process rather than the outcome.”
You’ll never improve the outcome without falling in love with the process.