table {mso-displayed-decimal-separator:”.”; mso-displayed-thousand-separator:”,”;} tr {mso-height-source:auto;} col {mso-width-source:auto;} td {padding-top:1px; padding-right:1px; padding-left:1px; mso-ignore:padding; color:black; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:”Aptos Narrow”, sans-serif; mso-font-charset:0; text-align:general; vertical-align:bottom; border:none; white-space:nowrap; mso-rotate:0;} .xl148 {font-size:12.0pt; text-align:left; vertical-align:top; border-top:.5pt solid black; border-right:none; border-bottom:.5pt solid black; border-left:none; background:white; mso-pattern:black none; white-space:normal;} How MLOps can be used to address this problem by connecting the different stages of the machine learning lifecycle into one automated pipeline.