Data-related Issues:-

The development of a reliable ML-system depends not only on excellent coding — the quality and quantity of the training data play a pivotal role.

Performance-related Issues:-

The sufficient algorithm performance is another key cost-effective factor, as often a high-quality algorithm requires several rounds of tuning sessions.

  1. Accuracy Rate Varies
    It’s worth noting that the performance rate varies according to the client’s business objective and the cost of wrong predictions. A broker would take advantage of the system that produces 55% of correct predictions ensuring profits. However, a 90.9% accurate system aimed at diagnosing disease, with treatment being lethal to false-positive patients, is by far not satisfactory.

Milestone and Timeline:-

1. Discovery & Analysis Phase {By 2021}

The purpose of this phase is to conduct a feasibility study and set business and project objectives.

2. Data Collection and Preparation Phase {By mid-2022}

The biggest advantage of any AI-based application is the ability to process complex data and extract valuable insights from it. The more data it processes, the more accurate it becomes. The results are strictly dependent on the data quality — if the algorithms or ML models take mediocre data as input, they output disappointing results.

3. Prototype Implementation and Evaluation Phase {By end- 2022}

Prototyping is a great technique that allows software professionals to validate requirements and design choices. Prototypes are quick and cheap to produce and flexible to adjust. The risks and costs associated with software implementation are significantly reduced, as the requirements are well-discussed before the development begins.

4. Minimum Viable Product (MVP) {By 2023}

An MVP is a real product with a set of functional features developed based on the prototype findings. The MVP relies on the client’s actual data and is exposed to a small group of real customers as a simplified version of the ultimate product solution.

5. Product Release {By mid- 2024}

At the last stage, the product with a complete set of predefined features is developed and then launched into the market. The preceding steps emphasize the requirements elicitation and validation — therefore, the end product is made with minimal risks. The cost of this phase is usually estimated during the previous stages. Also, if the project is successful, the development team helps add extra features to the solution and customize it for specific customer needs.

6. Maintenance and Support

After the successful project release, it is essential to maintain primary metrics and collect feedback. Situation awareness and timely response grants the ability to increase revenue and deliver expectational results. Often as the amounts of data grow, it is crucial to benchmark algorithm performance and quality of the ML model results.



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