An information processing apparatus comprising: a storage section configured to store a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image printed on the printing medium, the machine-learned model having inputs into the machine-learned model that consist of a medium type information of a printing medium, color space information, coloring material-type information, and a test image printed on the printing medium, and an output consisting of a limit value indicating a maximum value of an amount of the coloring material to be used for printing on the printing medium; a receiving section configured to receive input of selection information including the medium-type information regarding a type of the printing medium; an obtaining section configured to obtain test image information obtained by capturing the test image printed on the printing medium by a printing section that performs printing by using the coloring material; an estimating section configured to estimate, based on the selection information and the test image information, by using the machine-learned model the limit value indicating a maximum value of an amount of the coloring material to be used for printing on the printing medium by the printing section per unit area; and a creating section configured to create, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material; wherein the selection information input into the estimating section also includes color space information, and coloring material-type information; wherein the color space information is information regarding the type of the color space that is a color matching standard and the coloring material-type information is information regarding the type of coloring material used. a storage section configured to store a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image printed on the printing medium, the machine-learned model having inputs into the machine-learned model that consist of a medium type information of a printing medium, color space information, coloring material-type information, and a test image printed on the printing medium, and an output consisting of a limit value indicating a maximum value of an amount of the coloring material to be used for printing on the printing medium; a receiving section configured to receive input of selection information including the medium-type information regarding a type of the printing medium; an obtaining section configured to obtain test image information obtained by capturing the test image printed on the printing medium by a printing section that performs printing by using the coloring material; an estimating section configured to estimate, based on the selection information and the test image information, by using the machine-learned model the limit value indicating a maximum value of an amount of the coloring material to be used for printing on the printing medium by the printing section per unit area; and a creating section configured to create, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material; wherein the selection information input into the estimating section also includes color space information, and coloring material-type information; wherein the color space information is information regarding the type of the color space that is a color matching standard and the coloring material-type information is information regarding the type of coloring material used.