"Talent, time and trust!"

3 big challenges that an organization faces in adopting enterprise level AI.

Three Big Challenges


People with machine learning(ML) skills are not really easy to find. Often companies can't implement ML projects due to lack of skilled personal available withthin orgnization.


Many available solutions are difficult to trust. According to European Union (EU) General Data Privacy Regulation (GDPR) Article 22, as a decision maker using or planning to use AI/ML in company then understanding how to create trust in AI/ML is going to be crucial to the success.


ML Projects can involve months of data preparation followed by a process of
designing, developing, testing, tuning, and deploying a machine learning model.

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Feature engineering

Every algorithm need a basis for understanding the data they process, which is accoamplished through a process of transforming raw data into features that better represent the underlying structures and patterns in the data, optimizing what the algorithm can learn from the data. This is called feature engineering.

Model building

With a representative feature table, the algorithm is able to build a model of what
it expects the data will show. It can then compare the model to the data, learn from
what it observes, and modify the model iteratively so that it grows increasingly

Specialized functions

The specialized functions can be added to machine learning solutions and help organizations increase profits, minimize risk, and achieve their objectives. They also enable the development of an extensible platform that can handle current and future use cases.

ML interpretibility(MLI)

It aims to help humans understand or explain the results of models with actionable insights. There are many techniques and frameworks that are used for this purpose. It's also critical for regulated industries like financial services, insurance, and healthcare to help ensure that the machine learning models are safe, trusted, and pass regulatory oversight.

Deploying the scored model

The goal of the machine learning model is to deployed in the production environment. Application that can score the new unseen data using the model with less error and with less delivery time will be more appreciated by the orgnization or the business.

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