Changes

|style="width:33%; border-left:solid 2px #ededed;border-right:solid 0px #ededed;border-top:solid 0px #ededed;border-bottom:solid 0px 2px #ededed; text-align:centerleft; vertical-align:top; background-color:#ffffff"| <br />[[File:TBDNeuralNetwork.png|thumbnone|300pxx150px|center|Machine Learning Services: what we offer in short]]<br />'''Model design'''* PoC design for dataset acquisition* Dataset recording* Model design and definition (using the most popular techniques depending on the Scope of Supply)* Dataset analysis and preparation for Learning and Training phase|style="width:33%; border-left:solid 0px #ededed;border-right:solid 0px #ededed;border-top:solid 0px #ededed;border-bottom:solid 0px 2px #ededed; text-align:centerleft; vertical-align:top; background-color:#ffffff"| <br />[[File:TBD002425_%2B0.003355520.png|thumbnone|300pxx150px|center|Machine ]]<br />'''Model Learning and Training'''* Model deployment on target* Design of Learning Services: machine on local or cloud solution* test bench based solution for accuracy evaluation* Deployment on the on board mechanism]]field and fine tuning|style="width:33%; border-left:solid 0px #ededed;border-right:solid 2px #ededed;border-top:solid 0px #ededed;border-bottom:solid 0px 2px #ededed; text-align:centerleft; vertical-align:top; background-color:#ffffff"|<br /> [[File:TBDML-TN-001-MPSoC-PL1.png|thumbnone|300pxx150px|center|Machine Learning Services: the deployment]]<br />'''Continuous Learning and Update'''|-* IoT based continuous recording of new dataset information|style="width:25%; border-left:solid 2px #ededed;border-right:solid 0px #ededed;border-top:solid 0px #ededed;border-bottom:solid 2px #ededed; text-align:left; padding:13px; background-color:#ffffff"|* New dataset information review and integration| style="width:25%; border-left:solid 0px #ededed;border-right:solid 0px #ededed;border-top:solid 0px #ededed;border-bottom:solid 2px #ededed; text-align:left; padding:13px; background-color:#ffffff"|* Continuous model Training and Learning|style="width:50%; border-left:solid 0px #ededed;border-right:solid 2px #ededed;border-top:solid 0px #ededed;border-bottom:solid 2px #ededed; text-align:left; padding:13px; background-color:#ffffff"|* Deployment OTA on the field of new model updates

| style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ededed; text-align:left; font-size:14px; color:#000000" |Machine Learning Services: what we offer in short

| style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ededed; text-align:left; font-size:14px; color:#000000" |Machine Learning Services: the on board mechanism

| style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ededed; text-align:left; font-size:14px; color:#000000" |Machine Learning Services: the deployment| style="width:1%; border-left:solid 0px #ededed;border-right:solid 0px #ededed;border-top:solid 2px #ffffff;border-bottom:solid 0px #ededed; background-color:#ffffff" || style="width:25%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ededed; text-align:left; font-size:14px; color:#000000" |Machine Learning Services: the maintenance/upgrade

|style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ffffff; vertical-align:top"|DAVE Embedded Systems offers the above services with two possible approaches:* based on binding formal quotation after discussions and effort evaluation* based on Time & Material approach with an initial estimation of the effort| style="width:1%; border-left:solid 0px #ededed;border-right:solid 0px #ededed;border-top:solid 0px #ededed;border-bottom:solid 2px #ffffff; background-color:#ffffff" ||style="width:25%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ffffff; vertical-align:top"|The standard approach requires an initial contact with the [https://www.dave.eu/get-a-quote technical team]. Customers together the team defines:* The technical specification* The scope of Supply* TBDThe Acceptance Criterias jointly used for evaluate the task and approve it

|style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ffffff; vertical-align:top"|* TBDDepending on the requested task, the project is managed via gantt approach with multiple tasks with peer review (both internals and shared with customers). The typical deployment is then shared with customers via a dedicated wiki structure (like this but private between DAVE Embedded Systems and customers) and a dedicated project branch on our Gitlab server.

| style="width:3025%; border-left:solid 2px #ededed;border-right:solid 2px #ededed;border-top:solid 2px #ededed;border-bottom:solid 2px #ededed; background-color:#ffffff; vertical-align:top"|* TBDDepending on the requested task, the project may require a regular service for updates and improvements (such as the improvement s on ML model thanks to an increased/modified dataset). In this case, this service is released regularly via sw updates in our Gitlab server with release candidates and official releases

* [link_TBD {{{nome-som}}} SOM http://mirror.dave.eu/marketing/Services/Machine%20Learning/Datasheets/Datasheet%20ML%20Services%20EN.pdf Brochure PDF ]

* [[TBD :Category:Machine Learning AN| Machine Learning Application Notes]]* [[TBD :Category:Machine Learning TN| Machine Learning Technical Notes]]* [[TBD :Category:Machine Learning WP| Machine Learning White Papers]]* [[TBD :Category:Machine Learning CH| Machine Learning Case Histories]]