Achhar Kalia has nearly 5 years of experience in application development, production support, and system integration. He has expertise in Linux/Unix administration, virtualization, databases, and networking. Some of his key skills include OpenStack, Oracle, DB2, networking protocols, and Ericsson products. He has worked on projects for clients such as Ericsson, Tata Consultancy Services, and Telstra involving the development, support and enhancement of various applications.
.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo
Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo
Azure Machine Learning provides enterprise-class machine learning and data mining to the cloud. This presenter will cover 1) what AzureML is, 2) technical overview of AzureML for application development, 3) a reminder to consider SQL Server Data Mining, and 4) a recommend path for resources and next steps.
Managing your ML lifecycle with Azure Databricks and Azure MLParashar Shah
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share and deploy machine learning applications using Azure Databricks. This is based on our talk at //build - https://www.youtube.com/watch?v=pe_OH07wAYc and https://mybuild.techcommunity.microsoft.com/sessions/76976
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This is a part of presentation done at Global Azure BootCamp 2017 Mohali Location.
We talked about how to get started with your first data science experiment using Azure Machine Learning Studio.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Experimentation to Industrialization: Implementing MLOpsDatabricks
In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.
We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”
The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.
Training of Python scikit-learn models on AzureMark Tabladillo
This intermediate-level presentation covers latest Azure technology for deploying Python sci-kit models on Azure. The presentation is a demo using a Microsoft Data Science Virtual Machine (DSVM), Visual Studio Code, Azure Machine Learning Service, Azure Machine Learning Compute, Azure Storage Blobs, and Azure Container Registry to train a model from a Python 3 Anaconda environment.
The presentation will include an architectural diagram and downloadable code from Github.
YouTube recording at https://www.youtube.com/watch?v=HyzbxHBpAbg&feature=youtu.be
Best practices with Microsoft Graph: Making your applications more performant...Microsoft Tech Community
Learn how to take advantage of APIs, platform capabilities and intelligence from Microsoft Graph to make your app more performant, more resilient and more reliable
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Insider's introduction to microsoft azure machine learning: 201411 Seattle Bu...Mark Tabladillo
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning
Introducing MLflow for End-to-End Machine Learning on DatabricksDatabricks
Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Forget becoming a Data Scientist, become a Machine Learning Engineer insteadData Con LA
Data Con LA 2020
Description
Machine learning is an essential skill in today's job market. But when it comes to learning Machine Learning, beginners get lot of conflicting advice. I have been teaching ML for software engineers for years. In this talk
*I will dis-spell some of the myths surrounding machine learning
*give you solid, tangible plan on how to go about learning ML
*and give you good pointers to start from
*and steer you away from common mistakes
Speaker
Sujee Maniyam, Elephant Scale, Founder, Principal instructor
Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This is a part of presentation done at Global Azure BootCamp 2017 Mohali Location.
We talked about how to get started with your first data science experiment using Azure Machine Learning Studio.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Experimentation to Industrialization: Implementing MLOpsDatabricks
In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.
We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”
The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.
Training of Python scikit-learn models on AzureMark Tabladillo
This intermediate-level presentation covers latest Azure technology for deploying Python sci-kit models on Azure. The presentation is a demo using a Microsoft Data Science Virtual Machine (DSVM), Visual Studio Code, Azure Machine Learning Service, Azure Machine Learning Compute, Azure Storage Blobs, and Azure Container Registry to train a model from a Python 3 Anaconda environment.
The presentation will include an architectural diagram and downloadable code from Github.
YouTube recording at https://www.youtube.com/watch?v=HyzbxHBpAbg&feature=youtu.be
Best practices with Microsoft Graph: Making your applications more performant...Microsoft Tech Community
Learn how to take advantage of APIs, platform capabilities and intelligence from Microsoft Graph to make your app more performant, more resilient and more reliable
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Insider's introduction to microsoft azure machine learning: 201411 Seattle Bu...Mark Tabladillo
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider's perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning
Introducing MLflow for End-to-End Machine Learning on DatabricksDatabricks
Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Forget becoming a Data Scientist, become a Machine Learning Engineer insteadData Con LA
Data Con LA 2020
Description
Machine learning is an essential skill in today's job market. But when it comes to learning Machine Learning, beginners get lot of conflicting advice. I have been teaching ML for software engineers for years. In this talk
*I will dis-spell some of the myths surrounding machine learning
*give you solid, tangible plan on how to go about learning ML
*and give you good pointers to start from
*and steer you away from common mistakes
Speaker
Sujee Maniyam, Elephant Scale, Founder, Principal instructor
Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
Learn to Use Databricks for the Full ML LifecycleDatabricks
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Experiencia de trabajo colaborativo y mediación de TICs a través del cual se propuso el diseño de un Zoologico virtual con la participación de todos los estudiantes.
Window Shades for Luxury Living by Hunter DouglasHunter Douglas
Browse our PDF version of "Window Shades for Luxury Living" coffee table book. Featuring outstanding examples of windows fashions created by some of Asia's leading architects and designers.
Taller sobre "Geoturismo: los destinos turísticos en Internet"
Autor: Gersón Beltrán López
Parte A: la oferta turística
Congreso Web de Zaragoza 5 de junio de 2011
Fund Your Innovation - SR&ED Tax Credits and other Government GrantsBoast Capital
So now that you’ve started a business and have everything figured out you’ve started to look at alternative financing options. The non-dilutive options are great but what are the options and how do you access this cash? We will walk you through the SR&ED tax incentive program and discuss IRAP, IDMTC and other government grants available to help you build your product(s).
Alex Popa presented the answers to the following questions for DevFestYVR on Feb 21:
-What you need to know about the SR&ED program.
-How the SR&ED program applies to product and process development in the real world.
-What does the SR&ED claim process look like – a detailed walkthrough.
-How can I make this source of funding more reliable?
-What are some of the government grants available to help lower the cost of building a product? A look at IRAP, IDMTC, and other sources of funding.
Se exponen los principales ejes de la propuesta educativa del Colegio Bertolt Brecht definida y desarrollada bajo el modelo pedagógico histórico cultural.
The SideSlip Angle Estimator is an algorithm developed by Addfor to estimate the Drifting Angle of a Vehicle.
The SideSlip also known as “Beta” or “Drifting” angle is the angle between the longitudinal axis of the vehicle and its speed direction. This angle cannot be measured with standard sensors but is very important for the modern Traction Control Systems.
Addfor has developed an high performance estimator based on the Deep Learning technology that outperforms the commonly known Kalman filtering approach.
For more information visit: www.add-for.com
Solution Architect with 9 years of experience in solutioning, designing, developing and integrating Network Management Solutions using Ericsson Experience Manager, Customer Experience Management, ENIQ, OpenNMS, Core Java, SQL, PL/SQL, Java-J2EE technologies, Web Content Management, OFBiz, Javascript, XML, UNIX scripting and other Ericsson proprietary products.
Functional expertise includes
o Designing Solution Architecture, Database.
o Implementation in various Java-J2EE technologies like Core Java, Struts, Hibernate, Spring MVC, AJAX, EJB, Restful Web Services, Flex, OFBiz, JSF-Seam, Jboss Portal
o Webcontent Management using Alfresco Content Management, Interwoven Web Content Management
Executed Roles include
o As Solution Architect for designing Service Models, KPIs and Adaptations of COTS products like Ericsson Experience Manager (EEM).
o As Solution Architect for designing and developing extraction algorithm for complex database architecture following several standards like base64 decoding, JAXB Parser and normal PL/SQL extraction.
o As Solution Architect for designing Database, Flex Cairngorm Framework, Spring MVC-Hibernate Integration of Web based Dashboard Solutions (CXO Dashboard)
o As Team Lead for designing Portal site of several State Governments using Alfresco Content Management, JBoss Portal, Core Java
o As Scrum member for ERP Solutions using OFBiz (J2EE based ERP Solution), JavaScripts, JUnit
o As developer for development using EJB, Restful Web Services, Struts, Hibernate, JUnit, AJAX etc
o As trainer of Ericsson Experience Manager (EEM), Customer Experience Manager (CEM), OpenNMS, CXO Dashboard, Java-J2EE to several teams
• Over 4 Years 1 Months experience in Mediation using INTEC/CSG Tool v5.0/v6.0/v8.0 & Ericsson Multi Mediation (EMM) v8/v15 along with UNIX and HP Service Manager.
• Having good working experience on INTEC IME V8.0/5.1, Ericsson Multi Mediation.
• Installation / Up gradation of the Internal BSS Data Charging and Mediation Products.
• End to End Integration of complete BSS Architecture followed by UAT / Migration.
• End to End Integration and business logics development for Ericsson Mediation system.
• Experienced in troubleshooting the End to End systems of IN & Charging Networks.
• Experience of working in a multi-vendor environment.
• Manage technical process and resolve technical issues.
• Superb communication skills and able to articulate technical jargon to a non-technical audience.
• Having the necessary drive and enthusiasm required for a tough competitive industry.
• Excellent documentation & report writing skills.
• Strong attention to detail and focus on task completion.
• Good conflict management and prioritization skills.
• Demonstrated ability to work with and support cross-functional project teams.
Business and IT agility through DevOps and microservice architecture powered ...Lucas Jellema
IT needs to run in production in order to generate business value. DevOps is among other things a way of thinking focusing on production software. A business application requires a tailor made platform to generate business value. The combination of application and its platform is a DevOps product. The DevOps team has full responsibility for that product through its entire lifecycle.
The microservices architecture promises flexibility, scalability, and optimal use of compute resources. Via independent components with well-defined scope and responsibility, interface, and ownership that are evolved and managed in an automated DevOps process, this architecture leverages current technologies and hard-learned insights from past decades.
This session defines the objectives of Business with IT, of microservices and DevOps and introduces Containers and the container platform Kubernetes as crucial ingredients for making DevOps happen.
How to bring innovation to your organization by streamlining the deployment process ?
IaaS, PaaS or Docker containers are all valid methods that can be tailored for your needs. They each come with advantages and drawbacks, and are opposed each day by vendors and providers along. Should we really impose a standard for every team ?
1. ACHHAR KALIA
Mobile: +91-9999338675 ~ E-mail: er.achharkalia@gmail.com
SENIOR PROFESSIONAL
Nearly 5 year's expertise in Application Development, Production Support & System Integration with prime focus in creating
maximum business value and profitability through strategic IT implementation, planning & leadership
Brings fresh and innovative approaches to develop/manage IT applications, up-skill people, effectively engage with stakeholders
and drive continuous improvement across organization.
SNAPSHOT
• A dynamic professional with nearly 5 years of experience in Cloud
Environment, Virtualization, Application Development, Production
Support, System Integration & Design Development.
• Worked on Virtual environment/KVM: IN Unix/Linux/Solaris
environment, have performed virtualization for preparing cloud
environment with help of various tools like Ericsson CEE, HDS 8000, and
VMware & Oracle VM Virtual box.
• Working Exposure on Hardware and software defined virtualization.
• Proficient in IBM Tivoli Netcool Suite- TNPMW, Omnibus, Impact, Tivoli
Integrated Portal, Tivoli Service Request manager, Tivoli Business Service
Manager on Linux/Windows platform. Expertise in end-to-end
implementation of various projects; including designing, development,
coding and implementation of software applications.
• Experience on IBM DB2 and Oracle 11g databases. Enabling automation
through DB procedures, triggers and shell script efficiently.
• Successfully executed major projects such as TTSL PMS, TTSL FMS SQM,
Ericsson SOEM and Ericsson IPT-NMS.
• Skilled in handling end-to-end development of application products from
inception, planning, designing, implementation, documentation to
closure with cross-cultural teams.
• Expertise in smooth execution of applications Go-live, production
support and maintenance activities within SLA’s.
• Demonstrated excellence in development of framework and user
interfaces for various products which made the product truly friendly to
customer
• Valued for defining best practices for project support & documentation;
implementing project plans within preset budgets & deadlines.
CERTIFICATIONS
• Red Hat Certified System Administrator in Red Hat OPEN STACK.
• Red Hat Certified System Administrator (RHCSA).
• Red Hat Certified Engineer (RHCE).
• 5 Months OJT at Sweden on Cloud Technology and Hyper Scale
Datacenter System (HDS 8000) for s/w and h/w Virtualization.
IT Competencies:
• Linux / Solaris Administration
• Open stack Cloud
• Virtualization
• Shell scripting
• PL/SQL
• Oracle 11g database
• IBM DB2 9.7 database
Application / Product Competencies:
• Hyper Scale Data Center (HDS
8000)
• Ericsson Cloud Execution
Environment (CEE)
• Tivoli Netcool Suite Products
• TNPMW/Omnibus/ Impact / TBSM
/ TSRM/TCR/Net-act
• Ericsson SOEM, Ericsson IPTNMS
Functional Expertise:
• Application
Development/Production Support
• Solution Integration
• Service Delivery
• Change Management
• Product Delivery
2. CAREER HIGHLIGHTS
• Extensive background and knowledge in Linux/Solaris administration, Shell scripting, PL/SQL, Oracle & IBM DB2
database, Tivoli Netcool Suite Products like Omnibus, Impact, TBSM or TSRM, Net-act, Huawei M-2000 etc.
• Designing, testing & implementing infrastructure components that meet the specifications of projects & service
requests
• Performing infrastructure design & implementation activities for application development, infrastructure
deployments, stability, security and performance
• Gathering requirements with direct client interfacing, Preparing design documentations, Developing & integrating
modules, analyzing and debugging code for fixing errors and improving performance, Proof of Concepts for new
trends and technologies
• Experience in leading and providing architectural solutions pertaining Ericsson Products such as HDS 8000, Ericsson
CEE, SOEM, IPTNMS, etc.
• Creating technical design & solutions evaluating options considering constraints and risks and assist in proofs of
concepts of options, with review from senior engineers or architects.
• Contributing towards project planning, implementation plan & costing; creating test cases, performing tests
analyzing results
• Prepare and publishes technical documentation current for platform, service technical components with review by
senior engineers and architects when necessary
CAREER HISTORY
Since Mar’15 with Ericsson India Pvt. Ltd. , Gurgaon as PIDS/Tier -2 Support Engineer.
Jan’14 - Mar’15 with Tata Consultancy Services Ltd, Noida as Production Support Engineer
Dec’12 - June’13 with Tata Consultancy Services Ltd, Hyderabad as Production Support
Engineer
SIGNIFICANT CONTRIBUTIONS
• Achieved on the Spot Award from TCS in recognition of successful execution of planned events & automation.
• Recognized with TCS Gems Award for close & clean wrap-up of TTSL PMS-FMS SQM development project.
• Identified issues & risks in a timely manner; developed/implemented appropriate mitigation and contingency plans.
• On the Spot Performance Award' for ‘Successful completion of TRAI Audits’ in 21 Circles
• Got “Gems Performance Award'’ for 'Zero-Percent-Downtime' of the servers during Migration. Appreciation from
Client and Managers.
• Awarded 'President Award' for ‘Successfully implementing & providing development support to GSM PMS project in
GO Live’ in limited time scenario smoothly.
• 2nd Prize in CSI ADM for technology idea and patent filed for “Surveillance based Traffic security system”.
• Awarded 1st Prize for “INCENERATOR” project on CS Day.
EDUCATION
• B.Tech. (Electronics & Comm.) from Jai Prakash Mukund Lal Engineering College, Radaur, Kurukshetra University in
2011 with 73 %
• XII from BBMB DAV Public School, Nangal, CBSE Board in 2006 with 65 %
• X from BBMB DAV Public School, Nangal, CBSE Board in 2004 with 73 %
_____________________________________ PROJECTS____________________________________________
3. PROJECT #1 : Ericsson Cloud Execution Environment & HDS 8000 :
Project Ericsson Cloud Execution Environment & HDS 8000
Period May 2016 Onwards
Platform Linux, Ubuntu, Virtualization, Open stack, VM and vPOD in Software defined
Infrastructure.
Application Ericsson CEE and Ericsson HDS 8000
Role PIDS Developer & Tier 2 Support
Description
• Architecture information and understanding on Intel Rack scale Architecture: Logical
architecture for efficiently building and managing cloud infrastructure—and providing the
simplest path to a software defined data center.
• Hub of data, providing single integration point through full functional open RSD/Redfish
REST API.
• Seamless management and integration of Ericsson and 3PP infrastructure.
• Manage vPOD’s across 3PP and Ericsson HW.
• Independent of virtualization environment.
• Logical partitioning of installed hardware resources: Compute, Storage and Network
resources that make a vPOD.
• Virtual POD’s can interface with multiple Virtual Interface Modules (VIM) on the same
physical POD, to create logical tenancy:
–Windows (Azure)
–OpenStack (Mirantis, Red Hat)
–VMWare (ESXi)
• Separate management interface for each tenant (vPOD) from CCM.
• Tenancy created through L2 network (VLAN and VxLAN) slices.
• VNF server port management coordinated through CCM.
• Management interface is constrained and secured to each vPOD.
• CEE is a software product, which can execute on several hardware configurations. CEE
consists of the following parts:
Compute, Networking & Storage : These resources are managed via the Atlas Dashboard or
via CEE OpenStack Rest API and combined they are referred to as a CEE region.
• Redundant (x3) virtual Cloud Infrastructure Controllers (vCICs).
• Continuous Monitoring High Availability (CM-HA) & Persistent affinity.
• High throughput with low latency, through: Accelerated vSwitch (OVS) & Single Root
Input/output Virtualization (SR-IOV).
• CEE contains the virtualization layer (hypervisors) and virtual switches (vSwitches). It also
provides hooks to integrate and orchestrate external physical appliances, for example,
physical switches and storage arrays.
• The long-term goal for CEE is to provide support for the Infrastructure as a Service (IaaS)
cloud services.
• Cloud management provides high-level orchestration for the following:
Application deployment, monitoring, and management &
CEE infrastructure planning, fulfilment, assurance, and charging.
• A unified cloud infrastructure Operations and Maintenance, through:
Fault and performance management, Upgrade and rollback, Backup and restore.
4. PROJECT #2 : Ericsson Product management & enhancement:
Project Ericsson Product Management and Enhancement
Customer Telstra, Vodafone, Comtel, Robi Axiata etc.
Period May 2015 to May 2016
Platform Linux, Ingres DB
Application Ericsson (IPT NMS), Service on Element manager (SOEM)
Role Customer Support Engineer
Description • Supporting customers on L2/L3 issues that were being escalated to us after first level
analysis by CNS team and our main aim is to find the product bug and send it to Product
Line management so that patch can be provided to the customer for permanent solution
and it can be included in next update version as part of enhancement of product quality
and assurance of the best services.
• Finding the root cause by putting the logs in Debug mode to analyze in detail in the
minimum remote availability customer environment by collecting log & backups.
Sometimes we need to recreate the issue in our labs with backup taken from customer
network and try various scenarios to recreate problem and provide the remedy as a part
of workaround at the earliest and permanent solution as well.
Responsibilities • Management of Network elements and hands on experience with strong command on
Service on Element Manager Administration (SOEM).
• Knowledge on Protection scheme like MSP/MSSP ring creation.
• Experience on NE management, Links creation/Deletion, MSSP ring creation.
• Exposure to various Node type such as SPO, Mini- link, OMS etc.
• Application installation and software upgrades activity to the customer systems &
Network after their approvals.
• Troubleshooting in the area of NE Visibility, server not accessible, NE zoom-in issue, plugin
related issues, backups, filesystem and critical issues like server crash.
• Providing RCA on Critical Cases, Evaluate answer provided by Tier2 and design unit on a
CSR/TR. Exposure working in High Availability (HA) environment.
• Co-ordination with Product Design team of Ericsson and escalation of product and
software related issues to them.
• Providing recommendations on Hardware/software compatibility.
• Installation/Upgrade activities related to new software patches to customer.
• Providing regular updates and solution to the customers as per the SLAs.
• Database maintenance activities in case of DB errors.
• Patch Installation for the workaround or remedy to be shared with proper checks to
ensure proper availability of services.
• TMN Architecture understanding and sound understanding in FCAPS.
• DB maintenance activities & Metis Solution creation on unique problems for knowledge
base creation.
• Good Knowledge on SMS (Ericsson Ticketing Tool) functionality.
• Sharing of new ideas for the purpose of innovation in the field of tools used specifically
for IPTNMS, SOEM and SMS.
5. PROJECT #3 : TTSL OSS-PMS-FMS-SQM Project
Customer Tata Tele Services Limited
Period May 2012 to Mar 2015
Platform Linux, ORACLE/DB2
Application Tivoli Netcool Performance Manager Wireless (TNPMW), Netcool Omnibus, TSRM,
TCR, NETACT, Huawei M2000 Metrica 7.2, PL/SQL Developer
Role Production Support Engineer & Developer
Description PMS IBM product with the enhanced functionality for the PMS which processes the raw data
receives from different network elements present in the client network with multiple
protocols and then by creating various rules as per the requirement to parse data to
provide readable and meaningful Key performance indexes made up of formulae’s given
by the customer and store them in the database to provide meaningful technology wise
and vendor product like NETACT, Huawei reports and their generation of report with java
based IBM reporting tool named as TCR. It a web based application which is used to create
and edit reports using its Cognos engine & report studio. Prepared reports are then
available to users to provide a consistent approach to generating and customizing reports
as per the requirement of the users for the optimum performance of the network and to
identify the faults in the network.
Description FMS • Netcool Omnibus Roll-out – SME in Pre and Post customization of Netcool Omnibus
and Impact as per user and organization requirements.
• Probe Installation - Integration of multiple ASCII, SNMP, CORBA probes into Netcool
and customizing rules, properties files and Impact policies to process alarm to TIP.
• Enrichment - Design and Implementation of CNMS DB portal database for proper
enrichment of alerts without putting extra load on the Impact server.
• Visual Enhancement – Modification of the TIP event windows and the fields of the
Object server as per the user requirements and the integration of other vendor
products with the IBM Netcool TIP such as TSRM.
• Procedures – Developing procedures and automation scripts to increase utility of the
Netcool such as Auto SMS and auto email to alerts of high priority and developing
triggers for auto TT creation for alerts active beyond threshold limit.
Responsibilities • Troubleshooting & ADM on upper layer OSS system. Exposure in creating a virtual
environment for development environment.
• Performance/Inventory report management, Experience in planning Parsing Process &
re-processing of data.
• Integration of TNPM with Netcool Omnibus, NETACT and Huawei.
• Processing and rules implementation on NETACT OSS for FCAPS functions on FM/PM.
• Integrating new nodes of Nokia, Huawei, ZTE in the system and process them in N/W.
• Installation of Product Patches, Interim Fixes and fixing bugs with testing done at
development level.
• Cold Backup of Servers & online backup of Database. Creation of Backup
Policies, Integration of New servers and Policies to ensure all kinds of Backup
6. Personal Details
Date of Birth : Jan 21, 1988
Language Known : English, Hindi, Punjabi
Father’s Name : Mr. Padam Kumar Sharma
Marital Status : Unmarried
Nationality : Indian
Permanent Address : VPO Kalitran, Tehsil: Nangal, District: Ropar, Punjab – 140133.
I do hereby declare that the Information furnished above Is true and correct as per best of my knowledge.
PLACE: - Noida Achhar Kalia