This is a major American designer and marketer of children’s apparel. The use a line assortment application for mapping product attributes to facilitate planning.
One of the most important challenges that had to be solved with software engineering was harmonizing development and testing environments for everyone working on a project. For example, if you had a project where developers were using Windows and Linux, there would be certain problems that some developers would have that the others wouldn’t. It meant that certain bugs would only show up for certain developers. However, containers such as Docker for data science sought to solve that problem, and it has had a massive impact on the software development industry. It has helped in solving many challenges for data scientists. The next industry that this innovation is touching is MLOps and data science.
The Move to Containers like Docker in Data Science
Data scientists are starting to encounter some of the same problems that software engineering and DevOps professionals have had to solve before. Data science requires many people to work together on the same data set, which can be difficult if they are using different environments. On top of that, there can be problems with operating systems and other tools. It is almost impossible to know whether a problem that was encountered occurred due to the actual data set or the environment. The way developers solve this problem is by using containers like software engineering professionals have done before. Which has made Docker an important data science tool. Though data science is a lot different, but there are enough similarities to warrant the use of containers.
What Is Docker?
Docker is one of the most important companies in the container industry that has the potential to impact even data science workflows. It provides software that helps you manage and maintain containers. It also has a massive database of containers that can be easily provisioned with only a few lines of code. A container is essentially a smaller version of a virtual machine. They share the same kernel, and there is enough isolation to ensure that there are no problems with the host system. Containers are also lightweight by design so that they can easily be moved from one physical server to the next. By using containers such as Docker for data science, data scientists can easily share data, code, and features with each other. They can harmonize their development environments to ensure that everyone is on the same page. These factors have made Docker an important data science tool.
Where to Use It in Data Science
One of the most important things to note about ML and data science is that most of the time is spent preparing data. Data scientists also spend a lot of time on data processing and other associated applications that don’t get them any results. Docker can be used in this step to reduce the setup time. They can create one Docker container that can later be shared with other developers. By distributing the container and associated components, they spend a lot less time preparing things. Thus, the majority of the time can be spent actually running algorithms on their data and code. It also allows skipping the configuration step for most software distributions. This makes Docker quite indispensable for data scientists.
How Docker Works and Its Ecosystem
Docker provides its own lightweight container application that fits inside the kernel. It then has an entire ecosystem of software components it can depend on. These components allow developers to easily provision the containers that they want. Docker essentially gives you a kernel virtualization layer, software tools, and an ecosystem for managing containers. It makes the transition easy, and it is something that every data scientist should seek to learn as it is growing in popularity as a data science tool. It can reduce the setup time, and data scientists as well as other professionals can dramatically shorten the time it takes to get answers from data.
With data being so vital to many organizations, it is becoming more important than ever to understand how to regulate and be transparent with that data. There are many inside and outside forces that will change the way you have to govern your data management. Data comes with many responsibilities, and you need to have policies in place to ensure that you are accountable for any problems that might happen. There are many laws and privacy issues at stake, and it is something you always have to think about.
Regulating Your Data Internally
Organizations must have effective data regulation and data management policies that govern how they acquire, use, and retain data. Not only will it enable the staff to operate more efficiently, but it can also save an organization time and money that may ensue from future audits and laws. Data regulations are updated almost every year, and organizations must stay ahead of them to ensure that business is not impacted due to any potential risks. Organizations that can regulate their data internally with the help of a robust data management strategy will be the ones that would stay ahead in the game. Internal regulation is of the many pillars that can transform your data management policies into a worthwhile endeavor.
Be Aware of External Limits Placed On You By Current Laws & Policies
Laws are fragmented at the moment. Every country has its own laws governing data, and organizations need to be aware of almost all of them. These laws will only increase in the future, so organizations must have data regulation and data management policies that are ahead of where regulators are going. There are many privacy regulations coming into place as well that govern how organizations work with certain data. On top of that, there are security issues that can lead organizations into trouble due to inadvertent errors. As it gets more crucial to use data, hackers will become an ever more increasing presence and threat in this industry.
Regulating Your Data At Every Step
There are three areas where organizations need to regulate their data efficiently. This must be done to ensure that the right data management and data regulation policies are in place. These areas are collection, retention, and usability. While collecting the data, organizations have to look at how the data is obtained and cleansed. It could mean looking at how an organization transforms this data in the database to work with it in a more efficient manner. Organizations also have to look at how certain pieces of data are governed. It is crucial to understand this step, as the majority of the upcoming laws will be based on how data is retained by an organization. Finally, what is an organization doing with the data that has been collected? It is an important question that organizations must be able to address in the future. For example, if an organization doesn’t use the data for the reason it was collected, it could risk placing sanctions on the business and thus, derail it. Questions like this will become more important to answer in the future and would shape up an organization’s data management policies.
Transparency Is the Key to Robust Data Management & Data Policies
Every organization must be transparent with the way they manage data. That means telling people exactly what their data has been collected for and what would be done with it. Organizations should also have the same data management policies built into their business workflows. Employees should know exactly what is acceptable and what isn’t.
Keep Up an Accountable Culture with Your Data Regulation
Holding people accountable will be a crucial part of an organization’s culture. If people are held accountable, they will be more likely to maintain strict standards inside the organization.
What is the most efficient way to organize your data science project? The reality is that the answer to this question is a very complicated one. Data is all about extracting valuable insights from mountains of information. However, there has to be an efficient pipeline that takes you from data to better answers. It is what makes the data science industry so vital. Your workflow can be the difference between success and failure in your organization. An organization with an inefficient data science pipeline will spend most of its time not getting any results. Your organization needs to develop a library of best practices to ensure you have a workflow that works.
Start with Your Business Needs
One important thing to understand is that it takes an entire team to develop an application using data science. However, the first step is always understanding what your business needs. Many companies jump into data science because it is the cool new hip technology. They try to integrate machine learning into their applications without a good reason. Every data science project has to start with your business. Why are you starting this project? How will it improve your software? Will it make your customers happier? These are the types of questions you need to ask yourself before starting this long and difficult journey.
Use Cloud Infrastructure and Open-Source Applications
A big thing you can do to optimize your data science pipeline is to adopt open-source tools and cloud infrastructure. The biggest benefit that open-source tools bring to the table is that they are free. In machine learning, they are also the industry-standard way of doing things. That means there are many resources out there that will show you how to work with them. Cloud computing is also popular with machine learning practitioners. It means that there will be a wealth of resources to help you with any problems you might have moving to the cloud.
Create Data Science Workflows That Work for You
At the end of the day, data science is all about solving problems for your company. It is why your workflow should be tuned to your specific situation. You can do that by building the right team and creating the processes needed to create the workflow that fits your needs. Many tools like Jupyter and Docker make it easier for you to create your own custom workflow. You also have cloud services like AWS and Google Cloud to help you build those workflows.
Network with Your Machine Learning Community
When embarking on machine learning projects, you should realize that it is all about having a good community. Networking with other data scientists and machine learning practitioners will help your company get outside perspectives. It will also help you keep in touch with the latest developments in the industry. Machine learning and data science are rapidly changing technologies, and they will be really advanced a few years in the future. To stay ahead, you have to know what is going on at all times. That means building a strong network within this community.
Adopt a Scientific Approach
When it comes to solving your problems in the best way possible, you must use a scientific approach. That means focusing on solving problems using a step-by-step process instead of trying to adopt the newest tools. You want to create easily reproducible solutions, as that will tell you whether your algorithms are working or not. You also ensure that you have a firm grasp of the various methods in this industry.
Choose Good Tools
Tool selection is another factor that determines how well you do in this industry. Many data scientists only work with certain tools, and you won’t be able to hire those people if you choose something else for your project. It is crucial to adopt the most popular tools in the industry, as it will give you the biggest reach.
Data science requires a deep understanding of your business and specific tools. Without this knowledge, you won’t be able to create the workflow that takes you on the journey to solving your data science problems. It also prevents you from wasting time on solutions that your business didn’t need. There is a specific sequence of steps that are required to solve any data science problem.
What Problem Does Your Business Have?
Instead of looking at data science as a technology problem, businesses must start looking at things from a value perspective. What value does data science bring to your business? Businesses get value from data science by using these technologies to solve problems. It starts with defining a vision for your company data science can help you achieve. What is that vision? How can acquiring and manipulating data help you achieve this mission? It is crucial to have answers to these questions as data science and machine learning are not fun most of the time. In reality, you will spend most of your time during these projects on acquiring and cleaning data. Only about 5% of your time is spent on machine learning and integration into your projects.
Assembling Your Team
As with most journeys in business, it all starts with having a great team to help you achieve your goals. Your team should be comprised of top-level executives that are defining the mission for the company. The most important thing to remember is that data is an enabler for your organization. It enables your organization to make better decisions by having more detailed insights that come from data. Having a team that enables you to gather, clean, and process data effectively is crucial to this journey. You also need to be mindful of budgets and your access to infrastructure. While data science software is mostly open-source, cloud servers are not free. Part of assembling your team is also choosing the tools your team will use to work with this data science project.
Gathering the Data
The data you gather will make or break your data science project. The data you gather also has to be processed and clean effectively to enable you to get the right results. Improperly cleaned data will give you bad results, and it is why these steps are usually what your company will spend 95% of its time on. This step involves taking raw data and cleaning it to present it in the best format for your team.
Once data has been gathered and sufficiently cleaned, you are now ready to run experiments on this data. That is where the strength of your data science team comes in. The better your people, the more creative solutions and ideas they’ll be able to come up with to get the best answers from your data. It is also the time where the tools you chose can help you or make things worse for you. Tool selection is a crucial part of data science, and you will really feel it when it comes to running experiments. Tools that are hard and unwieldy will cause you to take a lot longer to complete the same tasks.
Implementing Your Solution
Finally, every data science project ends with the implementation of your machine learning solution. However, it usually doesn’t end there, as you will spend a lot more time tuning and improving your solution. The reality is that data science projects never really end. They get better as the data and algorithms improve. That is how the journey from an idea to a finished project is in data science. Once you understand this workflow, it will speed up and improve your projects immensely.
Kubernetes has gotten a lot more popular in recent years. However, it feels like many companies are moving towards using Kubernetes without the need for it. They have a perfectly workable situation that doesn’t need Kubernetes, but because it is the cool thing to do, they do everything they can to move to this program. The reality is that most companies do not need Kubernetes. It is a great program for running containerized applications, and it forms the foundation of modern DevOps culture. It is also the cool thing in technology, as it was made by Google, which is one of the Halo companies for the software engineering industry. That is to say that people are more willing to adopt the software that Google uses for its own internal purposes. However, the reality is most companies are not Google. Your software engineering needs do not scale to the level that Google’s does.
What Kubernetes Is Responsible For
As we moved to containers to host our applications, we needed a way to manage a growing number of containers in our infrastructure. This is where Kubernetes comes in. It enables you to efficiently manage and route traffic between your containers. It is quite scalable, and it is one of the many reasons why it is so great. It was developed by Google, and they host thousands to even millions of containers. It is a big part of DevOps, as it makes it more efficient to create a streamlined continuous integration and deployment pipeline. However, just because it is cool does not mean that it will fit your specific use case. Most current web users have monolithic applications, and they are still trying to force Kubernetes into that role.
Can’t You Improve What You Already Have?
Your first instinct might be to try to fit Kubernetes into whatever workflow you have, but this would not be a good idea. The first thing you need to do is to focus on improving your already monolithic application. TDD or Test-Driven Development is a great way of achieving that goal. It essentially allows you to test your application as you develop. You can then scale your application using configuration tools like Chef. You can also automate the way you configure servers and other essential functions. The best part about this approach is it allows you to scale the pieces of your application that become bottlenecks. For example, a database server can easily be scaled by adding additional services. You don’t need to abandon your current approach and moved to a complete microservices architecture. It is one of the many reasons why Kubernetes wouldn’t be good in this situation.
Add to Your Monolithic Application First
Eventually, you will reach a point where it is no longer possible to scale your application. At this point, you should try to add different pieces around your application to enable it to scale well. Adding Kubernetes at this point would be difficult, as it doesn’t contain many tools for security, deploying to a multi-cloud environment, and it also adds complexity. You could probably add various functions from cloud providers before trying to adopt the approach that requires Kubernetes.
When to Move to Microservices and Kubernetes
When you get here, it might be tempting to abandon everything and adopt Kubernetes without any other thoughts. However, it is crucial to understand that you would need to dedicate people to running and managing Kubernetes itself before you could adopt it. It is one of the most important reasons that it does not make sense to adopt it unless you have a really big microservices architecture that needs the functionality that Kubernetes provides. Despite Kubernetes being so powerful, it isn’t always the choice, and you can do many other things before moving towards this way of managing your application.