The different phases of developing, deploying, and finally using an algorithm are illustrated in the following diagram:

As we can see, the process starts with understanding the requirements from the problem statement that detail what needs to be done. Once the problem is clearly stated, it leads us to the development phase.

The development phase consists of two phases:

• The design phase: In the design phase, the architecture, logic, and implementation details of the algorithm are envisioned and documented. While designing an algorithm, we keep both accuracy and performance in mind. While searching for the solution to a given problem, in many cases we will end up having more than one alternative algorithm. The design phase of an algorithm is an iterative process that involves comparing different candidate algorithms. Some algorithms may provide simple and fast solutions but may compromise on accuracy. Other algorithms may be very accurate but may take considerable time to run due to their complexity. Some of these complex algorithms may be more efficient than others. Before making a choice, all the inherent tradeoffs of the candidate algorithms should be carefully studied. Particularly for a complex problem, designing an efficient algorithm is really important. A correctly designed algorithm will result in an efficient solution that will be capable of providing both satisfactory performance and reasonable accuracy at the same time.
• The coding phase: In the coding phase, the designed algorithm is converted into a computer program. It is important that the actual program implements all the logic and architecture suggested in the design phase. 

The designing and coding phases of an algorithm are iterative in nature. Coming up with a design that meets both functional and non-functional requirements may take lots of time and effort. Functional requirements are those requirements that dictate what the right output for a given set of input data is. Non-functional requirements of an algorithm are mostly about the performance for a given size of data. Validation and performance analysis of an algorithm are discussed later in this chapter. Validating an algorithm is about verifying that an algorithm meets its functional requirements. Performance analysis of an algorithm is about verifying that it meets its main non-functional requirement: performance.

Once designed and implemented in a programming language of your choice, the code of the algorithm is ready to be deployed. Deploying an algorithm involves the design of the actual production environment where the code will run. The production environment needs to be designed according to the data and processing needs of the algorithm. For example, for parallelizable algorithms, a cluster with an appropriate number of computer nodes will be needed for the efficient execution of the algorithm. For data-intensive algorithms, a data ingress pipeline and the strategy to cache and store data may need to be designed. Designing a production environment is discussed in more detail in Chapter 13, Large Scale Algorithms, and Chapter 14, Practical Considerations. Once the production environment is designed and implemented, the algorithm is deployed, which takes the input data, processes it, and generates the output as per the requirements.