Innovation is a crucial tool in every business. Most businesses prefer embracing machine learning projects when innovating. When using machine learning models, one of the essential aspects is data.

The data used must be correct lest the model will not reach the production stage. A machine learning model that lacks data is rendered useless.

The data gathered for use in machine learning models, regardless of the size, must be meaningful.

The creation of machine learning projects is a field that data scientists have excelled at. The models not only represent but also predict the data in the real world.

However, effective deployment of machine learning models is considered more of an art than a science. Statistics indicate that more than 80% of the projects in data science do not reach the production stage.

Data collection involves gathering data for machine learning models. Machine learning projects are a process comprising several steps.

The most crucial stage of the machine learning process is the preparation of a dataset. Dataset preparation involves procedures that help convert all the information gathered into data suitable for machine learning.

Dataset preparation also encompasses the establishment of the suitable mechanisms to be used in data collection. Data collection involves collecting all the necessary information required for your machine learning model.

Established companies have been collecting data for machine learning for the longest time. They have a load of data to use in their machine learning models.

Lack of data to use in machine learning projects is common for small, medium, and start-up companies.

There are two significant mechanisms used in data collection: data warehouses and ETL and data lakes and ELT.

1) Data warehouses and ETL (Extract, Transform, and Load)

Data warehouses involve the storage of data in warehouses. The data collected and stored in data warehouses are usually structured data. Structured data in a company includes payrolls, sales records, and CRM.

The data is then transformed into valuable data before loading it into the warehouse.

When using this approach, you do not know which data will be helpful and which one will not advance. Using the data warehouse approach, you will be required to use a business intelligence interface to access data.

2) Data lakes and ELT (Extract, Load, and Transform)

The data lakes mechanism involves the storage of both structured and unstructured data. Unstructured data include images, sound records, videos, PDF files, etc.

When using this approach, you collect data and store it for later use. The data is only processed when required. Data lakes are considered the best approach for machine learning projects.

1) Formulate the problem before data collection.

The first step is to establish what will help in your machine learning project. Once we know what is helpful, we will know which data to collect. Problem formulation involves conducting a data exploration.

To formulate the problem, you need to follow the classify, cluster, regress, and rank approach.

2) Establish a suitable data collection mechanism to use.

A data collection mechanism helps in converting all the data into centralized storage. Establishing a data collection mechanism is vital in eliminating data fragmentation.

Businesses are comprised of several departments. As data passes through the departments and the different tracking points in a department, it is prone to soiling.

While converging all data to centralized storage is hard to achieve, it is manageable. A data specialist does data collection for machine learning, but in cases where you cannot afford one, you can use a software engineer with experience in databases.

3) Check for data quality.

The data collected for machine learning projects should be relevant. Machine learning algorithms are bound to fail if the data used is flawed. When checking for data quality, here are the things that you need to look at.

• Data tangibility to human error
• Technical problems associated with data transfer
• Omitted values in your data
• Relevancy of your data to the task you want to venture in
• Imbalanced data

4) Format data.

Once you have enough quality data, you need to file the data to fit the format you are using for your machine learning projects. The attributes of each dataset must be consistent with your file format.

Data formatting is essential when you have collected data from several sources or in case different people have manually updated your dataset.

5) Reduce data.

If gathering data for use in machine learning projects, you may end up collecting big data. When preparing the data for specific tasks, you need to reduce it.

There are two main approaches for reducing data: attribute sampling and record sampling. When reducing data using the attribute sampling approach, the first step involves establishing the target attribute to guide you.

Assume the values that are critical to your task and those that will add length and complexity. Under record sampling, you eliminate data with erroneous, missing, or fewer values to make the prediction more accurate.

1) Formulate the problem before data collection.

The first step is to establish what will help in your machine learning project. Once we know what is helpful, we will know which data to collect. Problem formulation involves conducting a data exploration.

To formulate the problem, you need to follow the classify, cluster, regress, and rank approach.

2) Establish a suitable data collection mechanism to use.

A data collection mechanism helps in converting all the data into centralized storage. Establishing a data collection mechanism is vital in eliminating data fragmentation.

Businesses are comprised of several departments. As data passes through the departments and the different tracking points in a department, it is prone to soiling.

While converging all data to centralized storage is hard to achieve, it is manageable. A data specialist does data collection for machine learning, but in cases where you cannot afford one, you can use a software engineer with experience in databases.

3) Check for data quality.

The data collected for machine learning projects should be relevant. Machine learning algorithms are bound to fail if the data used is flawed. When checking for data quality, here are the things that you need to look at.

• Data tangibility to human error
• Technical problems associated with data transfer
• Omitted values in your data
• Relevancy of your data to the task you want to venture in
• Imbalanced data

4) Format data.

Once you have enough quality data, you need to file the data to fit the format you are using for your machine learning projects. The attributes of each dataset must be consistent with your file format.

Data formatting is essential when you have collected data from several sources or in case different people have manually updated your dataset.

5) Reduce data.

If gathering data for use in machine learning projects, you may end up collecting big data. When preparing the data for specific tasks, you need to reduce it.

There are two main approaches for reducing data: attribute sampling and record sampling. When reducing data using the attribute sampling approach, the first step involves establishing the target attribute to guide you.

Assume the values that are critical to your task and those that will add length and complexity. Under record sampling, you eliminate data with erroneous, missing, or fewer values to make the prediction more accurate.

The best starting point when gathering data for machine learning is financial data. Financial data meets all the other aspects of data collection for machine learning.

Economic data has many inbuilt data quality assurances since mere mistakes can lead to massive adverse effects on the business.

Historical financial records are accurate since they have timestamps that allow for data grouping based on different usage cases.

The steps to find data for your machine learning from financial records are:

• Estimating the following year’s budget based on past information (regression)
• Predicting valuable categories such as late payments or budget overruns (classification)
• Identifying abnormal patterns in expenditure (anomaly detection)

The ultimate goal of establishing a machine learning model is problem-solving. For successful problem-solving, machine learning must pass all the relevant stages, reach the production stage, and be used actively by the consumers.

Model deployment hence is as essential as building a machine learning model.

Datasets for machine learning projects can be gathered and made relevant by data scientists or information technology (IT) experts. The difference between the two is seen in the number of machine learning models that make it to the production stage.

IT invests in achieving uptime at all costs by staying focused on making things available and stable. On the other hand, data scientists focus majorly on iteration and experimentation rather than making it to production.

The gap established by data scientists and IT has given rise to machine learning engineers. A machine learning engineer is responsible for establishing machine learning models that reach the production stage, or the most suitable candidates.

However, business costs may not be enough to hire a machine learning engineer to actualize model deployment. Data scientists are advised to invest in learning ways of getting their models into production.

A machine learning model that lacks production is most likely going to lead to severe problems when deploying. Machine learning projects are not only time-consuming but also expensive.

It is therefore not worth investing in a machine learning project if you have no intention of putting it into production unless it serves pure research purposes.

Key areas that the team must consider before embarking on machine learning if they are interested in reaching the production stage are:

1) Data storage and retrieval

Machine learning projects are rendered useless unless data availability is made possible. The two critical aspects of data sets for machine learning are storage and retrieval.

Data storage is made possible by on-premise tools, cloud storage such as GCS, S3, Azure storage, etc., or a hybrid of both on-premise and cloud storage. Data retrieval and processing make the success of a model to production possible.

Consider the size of your data. Extensive data requires increased computing power for preprocessing and model optimization.

2) Frameworks and tooling

A machine learning model cannot train, run, or deploy on its own. A model to succeed in production needs the necessary frameworks, software, and hardware for effective deployment. The right combination of tools and frameworks is crucial.

The framework chosen decides the continuity, maintenance, and use of a model. The tool chosen should be popular, efficient, and supportive.

3) Feedback and iteration

Getting feedback from a machine learning model is very important. You should actively track and monitor your model to check for performance.

In case of data drift or skew, performance depreciation, or bias creep, active tracking and monitoring will ensure all the problems are addressed before reaching the consumers.