際際滷shows by User: NenadBozic2 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: NenadBozic2 / Wed, 20 Nov 2019 11:44:29 GMT 際際滷Share feed for 際際滷shows by User: NenadBozic2 What it takes to build production ready AI solution /slideshow/what-it-takes-to-build-production-ready-ai-solution/195528782 aipitfalls-191120114429
We are data company that works with other companies to help them build AI solutions. We are a blend of data scientists and data engineers and that makes us question from different angles how next big AI module will be integrated in your platform. We have pushed more then dozen AI solution in production over past few years. In this presentation I will share our experience working with various clients on AI solutions. We will give you a list of the most common AI pitfalls that prevent AI solutions to end in production. Are you familiar with PoC drawer, where RnD departments allocate some money to try something new, build that up to a working solution, but it never ends up in production? This presentation will help you prepare for your next AI project and it will help you lower down the chance for it to end up in the PoC drawer. ]]>
We are data company that works with other companies to help them build AI solutions. We are a blend of data scientists and data engineers and that makes us question from different angles how next big AI module will be integrated in your platform. We have pushed more then dozen AI solution in production over past few years. In this presentation I will share our experience working with various clients on AI solutions. We will give you a list of the most common AI pitfalls that prevent AI solutions to end in production. Are you familiar with PoC drawer, where RnD departments allocate some money to try something new, build that up to a working solution, but it never ends up in production? This presentation will help you prepare for your next AI project and it will help you lower down the chance for it to end up in the PoC drawer. ]]> Wed, 20 Nov 2019 11:44:29 GMT /slideshow/what-it-takes-to-build-production-ready-ai-solution/195528782 NenadBozic2@slideshare.net(NenadBozic2) What it takes to build production ready AI solution NenadBozic2 We are data company that works with other companies to help them build AI solutions. We are a blend of data scientists and data engineers and that makes us question from different angles how next big AI module will be integrated in your platform. We have pushed more then dozen AI solution in production over past few years. In this presentation I will share our experience working with various clients on AI solutions. We will give you a list of the most common AI pitfalls that prevent AI solutions to end in production. Are you familiar with PoC drawer, where RnD departments allocate some money to try something new, build that up to a working solution, but it never ends up in production? This presentation will help you prepare for your next AI project and it will help you lower down the chance for it to end up in the PoC drawer. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aipitfalls-191120114429-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We are data company that works with other companies to help them build AI solutions. We are a blend of data scientists and data engineers and that makes us question from different angles how next big AI module will be integrated in your platform. We have pushed more then dozen AI solution in production over past few years. In this presentation I will share our experience working with various clients on AI solutions. We will give you a list of the most common AI pitfalls that prevent AI solutions to end in production. Are you familiar with PoC drawer, where RnD departments allocate some money to try something new, build that up to a working solution, but it never ends up in production? This presentation will help you prepare for your next AI project and it will help you lower down the chance for it to end up in the PoC drawer.
What it takes to build production ready AI solution from Nenad Bozic
]]> 412 1 https://cdn.slidesharecdn.com/ss_thumbnails/aipitfalls-191120114429-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Coming to cassandra from relational world (New) /NenadBozic2/coming-to-cassandra-from-relational-world-new comingtocassandrafromrelationalworld2-190329081643
Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality. In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex... In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it. ]]>
Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality. In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex... In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it. ]]> Fri, 29 Mar 2019 08:16:43 GMT /NenadBozic2/coming-to-cassandra-from-relational-world-new NenadBozic2@slideshare.net(NenadBozic2) Coming to cassandra from relational world (New) NenadBozic2 Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality. In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex... In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/comingtocassandrafromrelationalworld2-190329081643-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Relational databases are something that we are familiar with, we have all started with them, we are using them for a while and we are taking common patterns for granted. When we need to choose, we go with vendor we are most familiar with, since all databases have similar functionality. In NoSQL space however, story is completely different. Choice of type of database and even vendor is based solely on use case and non-functional requirements. You can try to force one vendor for various use cases, but soon you will see that you are having hard time modeling data, your queries are slow, your application layer is complex... In this talk we will briefly touch the types of NoSQL databases out there. We will connect use cases with some databases which should give you good starting point when exploring solutions to your problem. Then we will switch to Cassandra, columnar key value store, and explain its architecture, specifics and give overview of common use cases. We will end up with things to avoid when using this database and our guidelines how to start with it.
Coming to cassandra from relational world (New) from Nenad Bozic
]]> 73 1 https://cdn.slidesharecdn.com/ss_thumbnails/comingtocassandrafromrelationalworld2-190329081643-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Tuning Java Driver for Apache Cassandra /slideshow/tuning-java-driver-for-apache-cassandra/82160612 tuningjavadriverforapachecassandra1-171116130246
Apache Cassandra is distributed masterless column store database which is becoming mainstream for analytics and IoT data. Many use cases where Cassandra is natural fit require latency tuning in order to serve requests really fast. DataStax driver has many options, some less familiar, which can greatly influence performance aspect. This talk will focus on Java applications and options at your disposal in DataStax Java driver which became standard when you want to use this database. We will concentrate on both monitoring and tuning aspect of things and we will provide different options for different use cases. There is no silver bullet solution and having many options requires deep dive when you want to figure out right decision. This talk will narrow down options and give you push in the right direction.]]>
Apache Cassandra is distributed masterless column store database which is becoming mainstream for analytics and IoT data. Many use cases where Cassandra is natural fit require latency tuning in order to serve requests really fast. DataStax driver has many options, some less familiar, which can greatly influence performance aspect. This talk will focus on Java applications and options at your disposal in DataStax Java driver which became standard when you want to use this database. We will concentrate on both monitoring and tuning aspect of things and we will provide different options for different use cases. There is no silver bullet solution and having many options requires deep dive when you want to figure out right decision. This talk will narrow down options and give you push in the right direction.]]> Thu, 16 Nov 2017 13:02:46 GMT /slideshow/tuning-java-driver-for-apache-cassandra/82160612 NenadBozic2@slideshare.net(NenadBozic2) Tuning Java Driver for Apache Cassandra NenadBozic2 Apache Cassandra is distributed masterless column store database which is becoming mainstream for analytics and IoT data. Many use cases where Cassandra is natural fit require latency tuning in order to serve requests really fast. DataStax driver has many options, some less familiar, which can greatly influence performance aspect. This talk will focus on Java applications and options at your disposal in DataStax Java driver which became standard when you want to use this database. We will concentrate on both monitoring and tuning aspect of things and we will provide different options for different use cases. There is no silver bullet solution and having many options requires deep dive when you want to figure out right decision. This talk will narrow down options and give you push in the right direction. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tuningjavadriverforapachecassandra1-171116130246-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Apache Cassandra is distributed masterless column store database which is becoming mainstream for analytics and IoT data. Many use cases where Cassandra is natural fit require latency tuning in order to serve requests really fast. DataStax driver has many options, some less familiar, which can greatly influence performance aspect. This talk will focus on Java applications and options at your disposal in DataStax Java driver which became standard when you want to use this database. We will concentrate on both monitoring and tuning aspect of things and we will provide different options for different use cases. There is no silver bullet solution and having many options requires deep dive when you want to figure out right decision. This talk will narrow down options and give you push in the right direction.
Tuning Java Driver for Apache Cassandra from Nenad Bozic
]]> 262 2 https://cdn.slidesharecdn.com/ss_thumbnails/tuningjavadriverforapachecassandra1-171116130246-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Challenges of monitoring distributed systems /slideshow/challenges-of-monitoring-distributed-systems-76090175/76090175 challengesofmonitoringdistributedsystems-170518113211
Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will focus on monitoring stack we like to use including Riemann, InfluxDB, ELK and Grafana. Cassandra will be used as an example of distributed system. Problem will be separated in two domains: metric collection and log collection and we will finish with example how you can combine both to pinpoint issues.]]>
Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will focus on monitoring stack we like to use including Riemann, InfluxDB, ELK and Grafana. Cassandra will be used as an example of distributed system. Problem will be separated in two domains: metric collection and log collection and we will finish with example how you can combine both to pinpoint issues.]]> Thu, 18 May 2017 11:32:11 GMT /slideshow/challenges-of-monitoring-distributed-systems-76090175/76090175 NenadBozic2@slideshare.net(NenadBozic2) Challenges of monitoring distributed systems NenadBozic2 Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will focus on monitoring stack we like to use including Riemann, InfluxDB, ELK and Grafana. Cassandra will be used as an example of distributed system. Problem will be separated in two domains: metric collection and log collection and we will finish with example how you can combine both to pinpoint issues. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challengesofmonitoringdistributedsystems-170518113211-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will focus on monitoring stack we like to use including Riemann, InfluxDB, ELK and Grafana. Cassandra will be used as an example of distributed system. Problem will be separated in two domains: metric collection and log collection and we will finish with example how you can combine both to pinpoint issues.
Challenges of monitoring distributed systems from Nenad Bozic
]]> 610 2 https://cdn.slidesharecdn.com/ss_thumbnails/challengesofmonitoringdistributedsystems-170518113211-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Challenges of monitoring distributed systems /slideshow/challenges-of-monitoring-distributed-systems/67054401 challengesofmonitoringdistributedsystems2-161012103505
Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will try to help you understand all the challenges and you will get an idea which tools and technology stacks are good fit to successfully monitor Big Data systems. The focus will be on open source and free solutions. The problem can be separated in two domains which both are the subject of this talk: metrics stack to gather simple metrics on central place and log stack to aggregate logs from different machines to central place. We will finish up with a combined stack and ideas how it can be improved even further with alerting and automated failover scenarios.]]>
Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will try to help you understand all the challenges and you will get an idea which tools and technology stacks are good fit to successfully monitor Big Data systems. The focus will be on open source and free solutions. The problem can be separated in two domains which both are the subject of this talk: metrics stack to gather simple metrics on central place and log stack to aggregate logs from different machines to central place. We will finish up with a combined stack and ideas how it can be improved even further with alerting and automated failover scenarios.]]> Wed, 12 Oct 2016 10:35:05 GMT /slideshow/challenges-of-monitoring-distributed-systems/67054401 NenadBozic2@slideshare.net(NenadBozic2) Challenges of monitoring distributed systems NenadBozic2 Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will try to help you understand all the challenges and you will get an idea which tools and technology stacks are good fit to successfully monitor Big Data systems. The focus will be on open source and free solutions. The problem can be separated in two domains which both are the subject of this talk: metrics stack to gather simple metrics on central place and log stack to aggregate logs from different machines to central place. We will finish up with a combined stack and ideas how it can be improved even further with alerting and automated failover scenarios. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challengesofmonitoringdistributedsystems2-161012103505-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Back in the days, you had a single machine and you could scroll down the single log file to figure out what is going on. In this Big Data world you need to combine a lot of logs together to figure out what is going on. Data is coming in huge volumes, with high speed so choosing important information and getting rid of noise becomes real challenge. There is a need for a centralized monitoring platform which will aid the engineers operating the systems, and serve the right information at the right time. This talk will try to help you understand all the challenges and you will get an idea which tools and technology stacks are good fit to successfully monitor Big Data systems. The focus will be on open source and free solutions. The problem can be separated in two domains which both are the subject of this talk: metrics stack to gather simple metrics on central place and log stack to aggregate logs from different machines to central place. We will finish up with a combined stack and ideas how it can be improved even further with alerting and automated failover scenarios.
Challenges of monitoring distributed systems from Nenad Bozic
]]> 545 3 https://cdn.slidesharecdn.com/ss_thumbnails/challengesofmonitoringdistributedsystems2-161012103505-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Functional Testing of RESTful Applications /slideshow/functional-testing-of-restful-applications-54222043/54222043 functionaltestingrestfulapplications-151021151201-lva1-app6891
Rise in popularity of the microservice architecture on one side and need to have the server which has many clients (mobile, web, machine to machine) brought both the challenge and the opportunity to better test RESTful applications on level of features. Main feature of RESTful application are exposed endpoints which enable creating test application as REST client which will view our application as blackbox. Test application can prepare input and wait for output which can be compared against expected one. In this presentation we will give overview of types of test you can do, concentrate on blackbox testing over REST Api, touch the terms of whitebox testing and graybox testing and why later approach is useful for external dependencies outside of our control and explain why you should use tools such as Cucumber to better communicate features with business people. Presentation will walk through our experiences and how we overcame problems along the way.]]>
Rise in popularity of the microservice architecture on one side and need to have the server which has many clients (mobile, web, machine to machine) brought both the challenge and the opportunity to better test RESTful applications on level of features. Main feature of RESTful application are exposed endpoints which enable creating test application as REST client which will view our application as blackbox. Test application can prepare input and wait for output which can be compared against expected one. In this presentation we will give overview of types of test you can do, concentrate on blackbox testing over REST Api, touch the terms of whitebox testing and graybox testing and why later approach is useful for external dependencies outside of our control and explain why you should use tools such as Cucumber to better communicate features with business people. Presentation will walk through our experiences and how we overcame problems along the way.]]> Wed, 21 Oct 2015 15:12:01 GMT /slideshow/functional-testing-of-restful-applications-54222043/54222043 NenadBozic2@slideshare.net(NenadBozic2) Functional Testing of RESTful Applications NenadBozic2 Rise in popularity of the microservice architecture on one side and need to have the server which has many clients (mobile, web, machine to machine) brought both the challenge and the opportunity to better test RESTful applications on level of features. Main feature of RESTful application are exposed endpoints which enable creating test application as REST client which will view our application as blackbox. Test application can prepare input and wait for output which can be compared against expected one. In this presentation we will give overview of types of test you can do, concentrate on blackbox testing over REST Api, touch the terms of whitebox testing and graybox testing and why later approach is useful for external dependencies outside of our control and explain why you should use tools such as Cucumber to better communicate features with business people. Presentation will walk through our experiences and how we overcame problems along the way. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/functionaltestingrestfulapplications-151021151201-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Rise in popularity of the microservice architecture on one side and need to have the server which has many clients (mobile, web, machine to machine) brought both the challenge and the opportunity to better test RESTful applications on level of features. Main feature of RESTful application are exposed endpoints which enable creating test application as REST client which will view our application as blackbox. Test application can prepare input and wait for output which can be compared against expected one. In this presentation we will give overview of types of test you can do, concentrate on blackbox testing over REST Api, touch the terms of whitebox testing and graybox testing and why later approach is useful for external dependencies outside of our control and explain why you should use tools such as Cucumber to better communicate features with business people. Presentation will walk through our experiences and how we overcame problems along the way.
Functional Testing of RESTful Applications from Nenad Bozic
]]> 897 6 https://cdn.slidesharecdn.com/ss_thumbnails/functionaltestingrestfulapplications-151021151201-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-NenadBozic2-48x48.jpg?cb=1574250213 Software engineer with more than 10 years of experience currently focused on server side development and Big Data technologies (Apache Cassandra, Spark, Kafka, Akka). Certified Cassandra developer since 2015. Strong believer in code decoupling and micro-service architecture. My mantra is choose the right technology for the right job. Java is my weapon of choice. Had excursions to other languages (ruby, objective-C, javascript, PHP), which made me think outside of Java box and I picked up other goodies and flavors of other languages along the way. Most of the time when I work with Java as backend technology, like to use parts of Spring framework. As part of SmartCat big data team we are w... https://cdn.slidesharecdn.com/ss_thumbnails/aipitfalls-191120114429-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/what-it-takes-to-build-production-ready-ai-solution/195528782 What it takes to build... https://cdn.slidesharecdn.com/ss_thumbnails/comingtocassandrafromrelationalworld2-190329081643-thumbnail.jpg?width=320&height=320&fit=bounds NenadBozic2/coming-to-cassandra-from-relational-world-new Coming to cassandra fr... https://cdn.slidesharecdn.com/ss_thumbnails/tuningjavadriverforapachecassandra1-171116130246-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/tuning-java-driver-for-apache-cassandra/82160612 Tuning Java Driver for...