{"product_id":"machine-learning-in-production","title":"Machine Learning In Production","description":"\u003cb\u003eDeploy, Manage, And Scale Machine Learning Models With Mlops Effortlessly\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eDescription\u003c\/b\u003e\u003cbr\u003e Machine Learning In Production Is An Attempt To Decipher The Path To A Remarkable Career In The Field Of Mlops. It Is A Comprehensive Guide To Managing The Machine Learning Lifecycle From Development To Deployment, Outlining Ways In Which You Can Deploy Ml Models In Production.\u003cbr\u003e\u003cbr\u003e It Starts Off With Fundamental Concepts, An Introduction To The Ml Lifecycle And Mlops, Followed By Comprehensive Step-By-Step Instructions On How To Develop A Package For Ml Code From Scratch That Can Be Installed Using Pip. It Then Covers Mlflow For Ml Life Cycle Management, Ci\/Cd Pipelines, And Shows How To Deploy Ml Applications On Azure, Gcp, And Aws. Furthermore, It Provides Guidance On How To Convert Python Applications Into Android And Windows Apps, As Well As How To Develop Ml Web Apps. Finally, It Covers Monitoring, The Critical Topic Of Machine Learning Attacks, And A\/B Testing.\u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cbr\u003e Master The Machine Learning Lifecycle With Mlops.\u003cbr\u003e Streamline Your Ml Workflow With Mlflow.\u003cbr\u003e Use Docker And Kubernetes For Ml Deployment.\u003cp\u003e\u003c\/p\u003e \u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cbr\u003e Whether You Are A Data Scientist, Ml Engineer, Devops Professional, Software Engineer, Or Cloud Architect, This Book Will Help You Get Your Machine Learning Models Into Production Quickly And Efficiently. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable Of Contents\u003c\/b\u003e\u003cbr\u003e 1. Python 101\u003cbr\u003e 2. Git And Github Fundamentals\u003cbr\u003e 3. Challenges In Ml Model Deployment\u003cbr\u003e 4. Packaging Ml Models\u003cbr\u003e 5. Mlflow-Platform To Manage The Ml Life Cycle\u003cbr\u003e 6. Docker For Ml\u003cbr\u003e 7. Build Ml Web Apps Using Api\u003cbr\u003e 8. Build Native Ml Apps\u003cbr\u003e 9. Ci\/Cd For Ml\u003cbr\u003e 10. Deploying Ml Models On Heroku\u003cbr\u003e 11. Deploying Ml Models On Microsoft Azure\u003cbr\u003e 12. Deploying Ml Models On Google Cloud Platform\u003cbr\u003e 13. Deploying Ml Models On Amazon Web Services\u003cbr\u003e 14. Monitoring And Debugging\u003cbr\u003e 15. Post-Productionizing Ml Models","brand":"BPB Publications","offers":[{"title":"Default Title","offer_id":45879532781766,"sku":"DADAX9355518102","price":13.1,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0695\/9389\/1014\/files\/81KaUbSkhzL.jpg?v=1779710326","url":"https:\/\/ergodemedia.com\/products\/machine-learning-in-production","provider":"Ergodemedia","version":"1.0","type":"link"}