Estimating the Time, Cost, and Deliverables of a Machine Learning App Project

The conclusion of every ML project is a solution that makes enterprises better than they currently are. They also make all business processes streamlined. Then again, developing such an app is an entirely different ballgame. Nonetheless, it’s an achievable task, and here you’ll learn how.

The importance

So, what’s the point of wading through the uncharted waters of ML? Why should you even bother with the benefits of machine learning? Well, here are a few compelling reasons.

• It lets you deliver personalized experiences.
• It allows you to incorporate advanced search systems.
• You can use it to predict the behavior of your users.
• It lets you enhance your digital security standards.
• It allows you to engage your users deeply

You can probably guess how valuable these perks are to any business. However, the time, expenses, and deliverables of your project will depend on these benefits.

The models

Now, it’s time for you to take a look at the models of Machine Learning. These models will allow you to adjust the time and expenses of your project.

1. Supervised learning: In this process, developers will provide data to the system by labeling the inputs and outputs of the algorithm. Due to these labels, it’s possible to train the system to pinpoint the data patterns inside the algorithm.

2. Unsupervised learning: This process also involves feeding data to the system. However, developers won’t label the outputs here. It lets the system identify the data and ascertain patterns from the information. As soon as the developers finish storing the same, the future inputs get assigned to the pattern to generate an output.

3. Reinforcement learning: Just like unsupervised learning, developers won’t label the data fed to the system in reinforcement learning. The Machine Learning types differ on the ground that the developers will tell the system about the output is correct. However, they’ll do it only when they receive an appropriate output.

Project lifecycle

Here’s a breakdown of the lifecycle of an ML project.

1. Setting up the plan

• Defining the project and its requirements
• Identifying project feasibility
• Discussing the tradeoffs of the general model
• Creating project codebase

2. Gathering and labeling the data

• Creating the documentation of labeling
• Building the pipeline of data ingestion
• Validating the quality of the data

3. Exploring the model

• Establishing the baseline for the performance of the model
• Creating a straightforward model with the initial data pipeline
• Attempting parallel ideas in the earliest stages
• Pinpointing the SoTA model for the problem and reproducing the outcomes

4. Refining the model

• Performing model-centric optimizations
• Debugging the models while adding complexities
• Running error analyses for unearthing failure modes

5. Testing and evaluating

• Assessing the model on a test distribution
• Revisiting the model evaluation metric to ensure it is driving the expected user behavior
• Creating tests for input data pipeline, model inference function, and explicit scenarios appearing during production

6. Deploying the model

• Exposing the model via REST API
• Deploying the new model to a subset of users to ensure everything is running smoothly
• Creating the ability to revert the models to their previous versions
• Monitoring the data in real-time

7. Maintaining the model

• Retraining the model to prevent repetitive or redundant behavior
• Educating the team about events of transferences in the ownership of the model

Project scope estimation

At this point, the developer team will define the ML app project estimates based on the phases explained below.

1. The discovery phase: This phase involves creating the roadmap of the project by defining the problem. It lasts anywhere between 7 and 14 days. During this time, the developers will explore all operational inefficiencies and address the same.

• The deliverable – It generates a problem statement that will define the triviality or complexity of the project.

2. The exploration phase: The objective of this phase is to fabricate the proof of concept. After that, the developers will install the same APIs. This phase will continue for 6 to 8 weeks.

• The deliverable – The proof of concept is the only deliverable here.

3. The development phase: At this stage, the team will work iteratively until they achieve something production-worthy. Estimations will be much more precise at this stage as the uncertainties will cease to exist. This phase will continue a bit longer than 4 months.

• The deliverable – This stage will yield a Machine Learning solution ready for production.

4. Continuous improvement phase: Now that the ML solution you wanted to create is in circulation, developers will maintain it. They’ll also take the necessary step to improve the digital product. Just remember that this improvement phase is continuous. It won’t end until you choose to discontinue the application.

Estimating the costs

When you started reading this, you wanted to know the cost of machine learning services. Well, stating a price is somewhat difficult in complex projects like these. However, it’s possible to estimate the same based on the following.

1. The Machine Learning app type cost

• The first type of ML app comes with an already existing model architecture and dataset. In short, business owners can get their hands on such projects for free.
• The second ML app type requires in-depth research. For instance, incorporating Machine Learning in an entirely new domain or various data structures compared to conventional models. The expenses of such a project are usually beyond the reach of startups.
• The third one deserves your attention as you shouldn’t have trouble paying for it. It involves taking already existing architectures and algorithms and changing them.

2. Data-related costs: An ML app won’t exist without relevant data. However, gathering high-quality data or inspecting the quality standards of the accumulated data is the main chore.

3. Research-related costs: The research section for ML mobile app development will be of entry-level feasibility exploration, experimentation, and algorithm search.

4. Production-related costs: This section is all about the expenses associated with the infrastructure, integration, and maintenance. Cloud computation can reduce your expenditures, but it’ll depend on the algorithm’s complexity.

Wrapping it up

Determining the workforce and time required to complete the project isn’t challenging. It’s possible to estimate the expenses if the developers build the app on modular designs and follow an agile approach.

Then again, difficulties will start cropping up when you estimate the value based on time and effort. If you choose to work with an agency that excels in building ML-based solutions, it should be an “easy street” for you.