Data analytics —through AI and machine learning— are rapidly incorporated into business applications to support policy-making and tackle critical issues such as privacy and business intelligence. Public cloud platforms such as AWS provide dedicated services that allow businesses to incorporate deep learning models easily, even without the need for specialized skills in data modeling or analytics.

Implementing a computer program for machine learning will tell you a lot about the algorithm, and how it operates.

Creating a good ML model at a high level is like designing any other product: you start with the concept, where you agree with the problem you are trying to solve and some possible approaches. When you’ve got a clear path, develop the solution, and check it to see if it suits the requirements. You keep iterating between ideation, prototyping, and testing until the solution is strong enough to put it into the market, at which point you develop it for a wider launch.

Practical Skills

Once you apply machine learning algorithms by hand, you gain useful skills. Skills such as algorithm mastery, skills that can assist in the development of production processes and skills that can be used in the field for classical study.

We learned below skills after years of experience:

• Mastery : The first step in mastering the algorithm is the implementation of an algorithm. When implementing the algorithm, you are expected to grasp it deeply. You can build your own tinkers’ laboratory for internalisation, such as debugging and incorporating steps to determine the operating operation, of the machine it performs over time.
• Production Systems : Usually custom algorithm implementations are important in manufacturing systems because the algorithm must be modified for performance and quality reasons. Better, quicker, less resource-consuming outcomes may eventually result in lower costs and higher business revenue, and the implementation of algorithms by hand can contribute to the development of these solutions.
• Literature Review : You perform studies when you implement an algorithm. You will find and read several traditional and structured algorithm explanations. You will also possibly find and review other algorithm implementations to validate your understanding. You perform basic research and learn how to read and use scientific publications in practice.

Process

You should adopt a method to accelerate your ability to learn from scratch and execute a learning algorithm manually. The quicker and more effective you use the algorithms that you implement, the more the method will be improved and adapted.

• Select programming language : Select the language for programming you want to use. The APIs and standard libraries that you can use in implementation can be affected by this choice.
• Select Algorithm : Pick the algorithm from scratch that you want to implement. Be as descriptive as you can. It not only means the class and algorithm type but also the choice of a certain concept or implementation to be implemented.
• Select Problem : To check and verify the implementation of the algorithm, pick a canonic problem or set of problems. There are no independent machine learning algorithms.
• Research Algorithm : Locate papers, books, blogs, libraries and any other algorithm details you can learn from and read. While you probably want a keystone summary of the algorithm, you want to take a look at the algorithm from many angles. The multiple viewpoints are useful as the overview of the algorithm can allow you to internalize faster and solve roadblocks from any uncertainty or suppositions created in the overview (the algorithm explanations often include ambiguities).
• Unit Test : With each feature write unit trials, also take test driven creation into account at the start of the project so that you are required to consider each code unit’s intent and goals before it is implemented.

Extensions

You will explore how to develop the implementation once you have implemented an algorithm. You can also discuss several examples of improvements:

• Experimentation : In the implementation of algorithms, you will show many of the micro-decisions you took as parameters and analysis variation. It will offer new ideas and unambiguous implementations of algorithms that can be shared and promoted.
• Optimization : Using tools, repositories, different languages, various data structures, templates, internal algorithms, you can explore opportunities to make the implementation effective. Knowledge of conventional computer science algorithms and data structures can be very useful in this form of research.
• Specialization : You should explore ways to define a problem in the algorithm. It is a valuable skill and can be used to build production systems. Improved alger efficiency (like working time) and reliability (like accuracy or other performance measures) can also contribute to a more problem specific algorithm.
• Generalization :Chances can be generated by generalizing a particular algorithm. Programmers (such as mathematicians) are special in complexity so you can see how the algorithm can be implemented completely in more common cases of a problem class or any other challenges.

Limitations

By applying machine learning algorithms by hand, you can learn a lot, but you can also note some drawbacks

• Redundancy : Many algorithms have already implementations, some very reliable implementations that hundreds or thousands of scientists and practitioners worldwide have used. Your implementation may be viewed as redundant, a doubling of the research the Group has already put into.
• Bugs : New code with few users, even with a professional programmer and unit testing, is more likely to have bugs. The use of a standard library will reduce the possibility of bugs in the implementation of the algorithm.
• Non-intuitive Leaps : Many algorithms rely on non-intuitive reasoning or logic for advanced mathematics. An application which does not consider these leaps as being restricted or incorrect can be made feasible.

The use of machine learning models is the process of making the models available to other software systems in production environments. Only when models are used in the manufacturing process do they add value and make implementation an essential step. Nevertheless, the use of machine learning models is challenging. This post is intended to help you understand the root of this challenge and I hope that it will provide useful resources and heuristics to combat this problem.

Few important areas to be observed in coming future:

1. Kubeflow aims at simplifying Kubernetes machine learning. It is still fairly new, with all the risks, but it will allow smaller teams with less DevOps experience to conduct a complex orchestration of containers for learning machines.
2. Server-free solutions, in conjunction with on-demand ML APIs, have evolved, and usual suspects are weighing (Azure Functions, Google Cloud Functions, AWS Lambda).
3. For example, at Netflix they have put much more time in being able to use Jupyter notebooks for production tasks in order to bridge the gap between the field of research and development.
4. More organizations are designing solutions to the problems addressed in the paper – data marking, versioning, automated model testing, validation and lifecycle management.
5. TensorFlow.js consumer machine learning. It still has a very hollow edge and it is not clear to me how confident corporations would allow the competition to read all their model code.
6. Design of ML systems known as “real time,” where models are continuously updated as new data streams arrive. This is partially aided by Apache Kafka’s now established position, but interesting alternatives like Apache Pulsar are gaining traction.