Sunday, February 24, 2013

Scaled Agile Framework 3/5 - Program level pipeline management and the program kanban


Part 2 of the series concluded with the final stage of the demand management process – 1 or more Epics generated from the COE level initiative and handed off to the relevant delivery groups.  In this post we will explore the lifecycle of an Epic as it travels through the delivery group functioning as an Agile Release Train – Strategic Delivery.

As described in Part 1, the adoption of the Agile Release Train in Strategic Delivery commenced in early 2012 (April).  Whilst it is being continually refined, the implementation has gone through a number of step changes in approach. 

After describing the initial adoption context and approach and covering some of the early experiences, the post will conclude by illustrating the current operating model.

Initial adoption context

At the time of adoption, the group was modelled roughly as follows:
  • 5 project-based Scrum teams all with the same team-shape, following a reasonably similar model and in the final stages of delivery for their projects
  • 1 project-based ‘pseudo-Kanban’ team with 3-4 months remaining on delivery of their project
  • 2 stakeholder-aligned ‘pseudo-Kanban’ teams with very different team shapes and 4-5 months of work remaining in their pipeline
  • A newly formed ‘System Team’ working on implementing a continuous integration capability
  • 10-15 projects running under an outsourced/offshored waterfall model
  • A group of system analysts fulfilling requirements and design elaboration for the waterfall projects
  • 18 project managers
The initial approach was defined in a series of workshops between the general manager of the delivery group and her extended leadership team. The objectives of the workshops were:
  • Establish a shared understanding of SAFe fundamentals
  • Determine the initial implementation model and approach to transitioning/maturing beyond initial implementation
  • Determine the most effective organisational structure for the group
Upon conclusion, the group had reached agreement on the following plan:
  • Immediately establish an Agile Release Train involving the 5 Scrum teams, converting them from project based to stable feature teams. All new work entering the group would be fed to the train.
  • Manage the outsourced waterfall projects through to conclusion.
  • Allow the ‘pseudo-Kanban’ teams to conclude their current commitments, whilst gradually reshaping them to be ready to join the train upon completion.
The organisation to support this had the following structure:
  • Agile Pipeline Management Services (APMS) – composed of the group of project managers from the Scrum teams and the system analysts. Primary responsibility: Manage preparation of the pipeline of work for PSI’s and make sure the teams had the support they needed in delivery.
  • Development Services – run by the equivalent of the “Release Train Engineer” and composed of the feature teams. Primary responsibility: Deliver PSI outcomes
  • Deployment Services – run by the deployment manager and incorporating the System Team. Primary responsibility: Liaise with enterprise release management and operations and ensure smooth deployment and ongoing continuous integration capability uplift.
  • Transition Services – composed of the ‘pseudo-Kanban’ teams and the outsourced waterfall projects/PM’s. Primary responsibility: Ensure graceful conclusion of inflight work whilst preparing the teams for transition onto the train.

Early Days

Some insights into the early experiences of the leadership group in the implementation can be found in this earlier post, but I’ll delve here into a number of key activities.

For the first month or so, it was ‘business as usual’ for the feature teams. They were already operating on the same iteration cadence, and all had a number of sprints committed in order to deliver on existing commitments. The primary focus area was the formation of the APMS group, but before delving into that I’ll briefly cover the feature teams.

Early days for the feature teams

The feature teams basically had two things to do:
  • Become ‘stable feature teams’ instead of project teams 
  • Normalise sizing. 
The first was great fun and fairly trivial. Formerly known by the reference number for the project they were working on (ie the ‘5531 team’), they were asked to select a name for themselves. The name had to be ‘train related’ and ‘safe for work’ – so we wound up with teams such as Maglev and Astrotrain. A simple change but immensely powerful when it comes to sense of identity.

The second was painful. The concept of story point normalisation is perhaps one of the most controversial in SAFe. Having been reluctantly convinced of the need by Dean, I still found that no matter how carefully you approach it you will still be impacted by most of the evils used to argue against the concept. We began by using the past 8 iterations’ metrics for every team to construct a normalisation formula. Simplistically, some teams downshifted their sizing one notch on the Fibonacci scale and other upshifted by one. It was deceptively simple, and one of the key lessons learnt in the time since is that we should have far more actively coached and retrospected on sizing practices.

Early days for APMS

The APMS team had 4 initial objectives:
  • Establish the Epic Kanban system reflecting both the active work in the feature teams and work in the pipeline 
  • Prepare the ‘work in pipe’ and logistics for the first PSI 
  • Work with the rest of the COE (in particular the PMO) to agree governance, financial and other details of how the release train would mesh with the broader enterprise. 
  • Continue to provide project management support to the feature teams
APMS Challenge 1 – Team Formation 

Whilst APMS was composed of PM’s, System Analysts and an architect, there was a ‘no exception’ stance on the release train being ‘Agile top to bottom’. So, the team had a scrum-master and an aspiration to work as a cross-functional team with shared commitments, standups, retrospectives, transparency and accountability to the feature teams. Today, it is a high-performance team with an exceptional team ethic – but the journey was more challenging than for any other agile team I’ve ever worked with. In the end, it required a far larger change of mindset for the team members than for a typical dev team and involved significant tuning of team balance and membership.

APMS Challenge 2 – Feature Grooming: How much is enough? 

The second challenge lay in the approach to grooming features for the PSI. The reality of the funding model and the dependency complexity of the work required a fair amount of analysis and estimation work to create ‘ready to play’ features. The best way to summarise this issue lies in the fundamental paradox of SAFe. Part of the reason the model is so powerful is because when you present it to program/project managers and architects they can readily map it to their existing mental models. On the other hand, the most common criticism I hear from agilists is that it ‘looks too formal and RUP-like’. What we experienced was that the intent of the model is too easily interpreted in practice into waterfall-like behaviours.

The original feature grooming model involved the APMS group identifying the business intent and features for the Epic, resolving the high level architecture and feeding it through a feature team for ‘enough discovery work to size it to +-30% confidence estimates’. The teams would thus hold a portion of their velocity available to be dedicated to these activities, which would be done in parallel with fulfilling their delivery commitments. What eventuated was the analysts/architects in the APMS group identifying and specifying the features and handing fully articulated designs to the teams who then simply estimated the design they’d been handed and handed their estimates back to the PM. This caused no end of problems, and was eventually heavily course corrected as you’ll see once we reach the current-day model.

APMS Challenge 3 – The Product Owner construct 

Prior to the adoption of the release train, finding the right product owners for agile initiatives had been a serious challenge for the group. Given the nature of a data warehouse, there are extremely diverse groups of stakeholders for any given initiative and finding someone with sufficient diversity of domain knowledge, sufficient availability and sufficient empowerment had been entirely unsuccessful. Dean’s “Product Manager/Product Owner” separation was the key to unlocking the puzzle. Whilst we couldn’t employ it exactly as specified in the framework, the underlying principal of separation of concerns was a huge enabler. In standard SAFe, the Product Manager is basically the overall decision maker on prioritisation and scope for the release train whilst the product owner travels with the team. In our context, there could be 15 different funding sources active at once and prioritisation and scope had to belong to the sponsors providing the money.

The solution adopted was to employ “Epic Owners” and “Feature Owners”. The Epic Owner was the person providing the funding for an Epic, and was looked to for engagement at key points and strategic prioritisation and scope calls for their Epic. The Feature Owner, on the other hand, was to have deep domain expertise on a particular feature, have delegated authority within the scope of the feature and be far more actively engaged with the team whilst their feature was in play. The construct resolved a vast number of issues, particularly in addressing the contrast between availability and authority.

APMS Challenge 4: Can we really do PSI’s? 

I’ve left the biggest challenge for last. SAFe deliberately separates the deployment cycle from the PSI cycle. Whilst the PSI produces a ‘potentially shippable increment’, the framework allows for either deployment on the PSI boundary, multiple PSI’s before a deployment or multiple deployments within a PSI. Our intent at launch was to run 8-week PSI’s and use the PSI as a planning boundary with multiple deployments happening in any given PSI.

The first difficulty we encountered was feature shaping. Given that the standard guidance for a feature is ‘small enough to be delivered in a single PSI but large enough to represent a significant outcome for the business’, there was a clash between good feature shaping and deployment window requirements. Available deployment windows were specified by enterprise release management, with 1 per month for minor deployments without significant integration requirements and a varying cadence for ‘enterprise deployments’ with major integration involved. Further, the deployment window for a feature was generally dictated by external dependencies. It was impossible to find a PSI cadence which aligned well with deployment boundaries, and shaping features to PSI’s was also largely impossible as we would regularly encounter features which would need to be deployed 1 iteration into a PSI but needed 2-3 iterations to implement.

Feature shaping was difficult but not insurmountable – in the end, we could have resolved it by allowing ‘cross-PSI’ features whilst still maintaining strong size limitations. However, the ‘bridge-too-far’ lay in establishment of sufficient funded pipeline. Once again driven by enterprise level gating cycles and funding processes, an 8 week PSI that fully funded and committed the entire release train was just not emerging.

By this point, however, we had established significant momentum with every aspect of the release train other than the PSI launch. The pipe was operating, the work was flowing into the teams, deployments were going out the door and momentum was tangible. One large leadership workshop later, we made the call: “know that PSIs will start eventually, but in the meantime develop compensating mechanisms and keep maturing the release train”.

Our focus then turned to compensating mechanisms – what were the key things we lost by not having the PSI, and how could we utilise our knowledge of the principals to achieve the purpose. To my mind, we identified the first 3 of the 5 key ingredients initially:
  • The power of having the whole release train in a planning workshop together from the perspective of developing a sense of ‘release train as one large team with shared outcomes’ as well as ‘release train as team of teams’ 
  • Employing a deliberate set of disciplines to groom out 4 iterations worth of backlogs at the release train level and ensure that visibility, dependency, risks and issues were fully articulated at the story level for all active features. 
  • Release train level ‘Inspect and Adapt’ cycles 
  • Maintenance of a strategic view of capability buildout on the platform that effectively capitalises on synergies between features. 
  • Strategic showcasing and communication of PSI level outcomes to draw together the ‘whole of product’ view of progress for broad stakeholder groups and avoid fragmented and isolated demonstration and communication feature by feature/epic by epic to narrow stakeholder groups. 
I’d like to write that we rapidly implemented effective compensation strategies, but I’d be lying. Being brutally honest, I’d suggest that we initiated effective strategies for the first and third items immediately. Time and painful lessons triggered focus on the second and fourth, and the fifth is currently in the spotlight.

The first strategy was the concept of ‘PSI-lite’, better known as ‘Unity Day’. A 1-hour ‘whole of train’ session at the start of each sprint aimed at building a sense of ‘train as team’ and ensuring everyone started the sprint on the same page. These have been incredibly powerful from a cultural transformation perspective. They are always attended by numerous visitors, and repeat attendees regularly comment on the tangible change in atmosphere over time.  (for a deeper treatment, see this post)

The second strategy involved the establishment of ‘Team Loco 131’, composed of the extended leadership group and functioning as a ‘program level continuous improvement team’. Their stories are generated through learning generated by various forms of retrospective, and the leaders are fully bought into the lean management model of taking responsibility for improving the system of work.

Whilst the story of addressing backlog grooming challenges is too long and torturous to describe here, I will offer the following. Many organisations find the investment of putting a whole program and set of stakeholders in a room for 2 days planning every 8-10 weeks unpalatable. For Strategic Delivery, this commitment was one of the major sticking points in management workshops – although the general manager bought in and was willing to support it, most of her leadership group felt it was unjustifiable. I’ve heard similar stories from many other SAFe Program Consultants. Our journey, painful lessons and ongoing investment required to compensate in the area of release train level backlog grooming/planning have utterly convinced me of the vital importance of this activity. To put my systems thinking hat on, failure to implement effective PSI planning creates an insane amount of failure demand across the entire release train. Weighing the cost of the failure demand against the investment required for the PSI planning days provides a scale very heavily tipped towards the investment. 

The Program Kanban

Before digging into the specifics of how the Program (or Epic) kanban operates today, there are two final points to cover off. The first involves a holistic view across both the initiative level kanban system and the epic level. Astute readers will notice a number of glaring inefficiencies between the two. Whilst I’ll cover this in a little more detail in the concluding post for the series, I will offer a reminder at this time that the purpose of explicit visualisation in a kanban is to bring to light waste and opportunity.

The second point is that whilst this blog series focuses on wall visualisations, every wall depicted is replicated in Rally with far more underlying detail. The Rally Portfolio Management capability is a key enabler in effective management and measurement of the system, whilst the walls are vital for tactile insight and facilitation of communication.

The Epic Kanban is, obviously, dedicated to the flow of an Epic through the release train value chain.  Every card on the wall is an Epic.   The value chain is divided into 5 phases, each with a number of states:
  • Initiate Phase: Transition from demand management and connection with stakeholders.   
  • Discovery Phase: Feature elaboration and estimation refinement
  • Launch Phase: Approvals, funding and preparation to drop into teams for implementation
  • Evolve Phase: Implementation
  • Operate Phase: Deployment and follow-through to ensure happy users and successful transition to operations.
One of the primary aspects of the phases is to indicate the flow of responsibility between APMS, Development Services and Deployment Services. For any given phase, one group will be ‘moving the Epic’ and the others will be either supporting or being kept informed.  You'll notice on the photo there are phases which have "rows" as well as "columns".  The rows indicate teams, whilst the columns indicate states.  Any state which has rows is one where a feature team has the ball - you'll notice photos in the start of each swimlane in the "Prepare Discover" state indicating which team is which.

On the day PSI’s become viable for this train, the phases and states will change very little. Initiate & Discover effectively involve the identification, sizing and prioritisation of features in preparation for PSI injection and maintenance of roadmap, Launch equates to locking in PSI content and executing PSI planning, and Evolve is delivery of the PSI.

The Initiate Phase

This phase is led by APMS and basically loads the Epic onto the train. The primary goals are as follows: 
  • Execute handover with the demand management group (‘PMO’ and ‘Validate Entry’ states) 
  • Connect with the Epic Owner, ensure their value drivers for the epic are clearly articulated and introduce them to the operating model (‘Envision’ state) 
  • Identify candidate feature owners ( ‘Envision’ state) 
  • Determine the feature team which will execute discovery, the sprint when it will occur, and ensure logistics are organised for discovery to proceed smoothly. (‘Prepare discovery’ state) 
This phase is the primary evidence of the response to ‘APMS Challenge 2’. It explicitly limits the amount and nature of the work done by APMS before connecting the Epic and Feature owners with the teams for further analysis. In effect, it eliminated roughly 70% of the preparation work that was initially being conducted.

Looking at the wall, you'll see one epic that's just landed from demand management (with the smurf avatar on it indicating the project manager who's pulled the card).  One is in the envisioning state, one has completed envisioning but is blocked from being pulled into discovery preparation (the big red "X" on it).  Finally, there is one epic being prepared to drop into Discovery with the Astrotrain team.

The Discovery Phase

Discovery is led by the feature teams, and involves working with the Epic and Feature owners to ‘discover the features’ and explore the feature level requirements sufficiently to articulate and size the outcomes. A feature team will typically have roughly 10% of their sprint capacity dedicated to discovery work, with discovery for most epics concluding within a single sprint. The flow of states is as follows:
  • Kickoff Discovery: Workshop with Epic Owner and all Feature Owners to gain understanding of the vision for the epic, determine the exploration workshops required, identify key risks/issues and ensure the stakeholders understand the discovery process.
  • Explore Features: Detailed workshops with feature owners to validate the feature composition, high level stories and feature level acceptance criteria.
  • Consolidation: Consolidation of the outputs from feature exploration workshops, estimation and preparation of a high level release plan.
  • Discovery Outcome: Presentation back to Epic and Feature Owners of consolidated discovery outputs for validation and agreement of outcomes
The key outcome of Discovery is a set of prioritised, well-groomed features which have been estimated to a +- 30% level confidence and are ‘ready to play’ for implementation. The other potential outcome is an ‘Epic split’. Once an Epic starts to look larger than half a million dollars, the preferred approach is to identify the highest priority features for initial implementation and carve off the remainder into a new Epic for a future release. In PSI parlance, it’s making sure no more than roughly a PSI worth of features are progressed at once on the Epic.
Looking at the wall, you'll see Astrotrain finalising Discovery for one Epic, Kaizen (3rd row) finalising one and kicking off another and Maglev consolidating one set of outputs whilst kicking off another.  It's  unusual for a team to have more than one Epic in Discovery at a time, in this case it's likely that they are small Epics which minimal feature elaboration required.

Launch Phase

This phase is reasonably self-explanatory. The focus returns to APMS, who look after gaining the appropriate approvals of the discovery outcomes and funding to proceed. Whilst in an ideal world this phase would be traversed rapidly to achieve a smooth flow from discovery to evolve, the reality is epics regularly sit here for significant periods whilst business cases gain approval and enterprise gating cycles complete. It has generated one simple and obvious insight – the better the Initiate and Discovery phases are executed, the shorter and smoother the launch phase process.

Launch concludes with evolve preparation. The feature team or teams to execute evolve are identified, logistics are organised for the release planning session at the start of evolve, the commencement sprint is locked in and the feature teams re-familiarise themselves with the discovery outcomes.

The key aspect to evolve preparation is feature team selection. Obviously, the ideal scenario is for the entire epic to land with a single feature team and for the evolve feature team to be the same one which executed discovery. However, reality intervenes. Whilst evolve capacity availability is utilised in selecting the discovery team, when the launch phase is protracted that team is often not available for evolve. In these scenarios, some capacity is reserved in the backlog of the discovery team to ensure they are available to support the kickoff and rapid knowledge uptake of the evolve team(s). Likewise, where the Epic is too large to be executed by a single feature team, work will be spread across feature teams on a feature by feature basis.

Looking at the photo, you'll notice a few things.  Firstly, queues are very visible :)  Secondly, although the APMS team runs a kanban system for their work (as hinted at by the epic level cumulative flow diagram and control chart on the wall), they also track the sprint (or iteration) cadence.  Their priorities on any given day are driven by where in the sprint cadence the feature teams are.   Finally you'll see the cards with post-its on them ('1', '2' and '4').  These would have been identified at the standup that day as needing urgent attention.

Evolve Phase

Implementation and deployment of the epic will be covered in the next post, so I will keep this section intentionally light. The states are as follows:
  • Plan Release: Execute the release planning which would normally occur in a PSI planning session. The stories identified in discovery are broken down into ‘sprint-size’ and scheduled across sprints based on available capacity to create an initial release plan. One of the key outcomes of this workshop is establishment of working agreements with the feature owners. They know which sprints their feature will be active in, when acceptance testing for their feature will occur and can plan their availability accordingly. 
  • Implement: Build and acceptance testing of features. 
  • Integrate and Prep Deployment: This phase has a split personality depending on deployment type. For an ‘independent deployment’, it takes roughly a week and involves standard integration, deployment hardening and operational handover preparation activities. For deployments associated with an enterprise release (75-80%), it lasts 4-5 sprints. The work cannot be accelerated, as the timing and nature of involvement is dependent on enterprise level co-ordination. Teams thus wind up with a trickle-feed capacity reservation during this time. 

Looking at the wall, you'll see Astrotrain juggling 3 Epics in implementation, with a 'stakeholder concern flag' on one probably giving hints as to why another Epic is being prepared to drop into the team.  Moving down the board, Jacobite are implementing a single (large) Epic, Kaizen have one in implementation and are most likely getting close to done there based on the fact they're in release planning on a second.  Maglev have a more even spread, with 5 epics at various stages of Evolve.

Fans of limiting WIP (presumably most readers of this blog) are probably looking at this picture and saying "why aren't they finishing one Epic before starting the next".  My first, somewhat glib, response is "isn't it great that it's so obvious".  On a more serious note, in a highly integrated world you often can't go at full pace on a single initiative.  It has fast times and slow times as you synchronise with your interface partners and resolve other such dependencies.  Having the flexibility to deal with this has been a very important aspect of the framework success.

Operate Phase

Under the leadership of deployment services, the epic is deployed and in operation.  Whilst numerous ‘tidy-up’ activities around production verification testing and governance closure occur here, the key component introduced has been the Epic retrospective.  This closes a significant gap in the PSI Inspect and Adapt cycle.  Whilst numerous ‘release train’ feedback cycles existed, what was missing was the stakeholder learning.  The feature team holds a retrospective with their epic and feature owners looking at the ‘whole of lifecycle experience’, and an invaluable source of strategic learnings is introduced.

The second state is ‘Spanked’. It is the cause of much hilarity amongst visitors, but has become part of the cultural identity of the release train. Originally introduced for some personality on a team kanban wall by an English scrum-master, it stands for ‘done done’. The Epic’s in production, the customers are happy, and the team has ‘spanked it’.Looking at the wall, it's fairly easy to conclude that it's not long since a major deployment window.


The daily rhythm of all the groups will be covered in the next post, but it’s worth making a few concluding comments here. The first is to play back to Dean’s concept of the release train as a ‘self organising program’. Effective visualisation of the epic lifecycle is a vital ingredient, as it generates so much ‘knowledge at a glance’ of the state of play of the entire value chain. Within seconds, the group can identify what epics are where in the value chain, who has carriage, where the queues are and what needs focus. And of course, metric analysis through cumulative flow and cycle time charting at the Epic level. All of this across a system of work that at any given time generally has 30-40 epics somewhere in the value chain.

I'll close with a hint of things to come when we talk about results in the final post for the series. You might remember a mention of 18 project managers in the initial context at the beginning of the post - there are now 3.

PS Part 4 in the series is now posted and deals with the program level feature wall and in-play work.


  1. What a sensational blog! This blog is too much amazing in all aspects. Especially, it looks awesome and the content available on it is utmost qualitative. safe release train engineer

    1. I am glad that I saw this post. It is informative blog for us and we need this type of blog thanks for share this blog, Keep posting such instructional blogs and I am looking forward for your future posts. Python Projects for Students Data analytics is the study of dissecting crude data so as to make decisions about that data. Data analytics advances and procedures are generally utilized in business ventures to empower associations to settle on progressively Python Training in Chennai educated business choices. In the present worldwide commercial center, it isn't sufficient to assemble data and do the math; you should realize how to apply that data to genuine situations such that will affect conduct. In the program you will initially gain proficiency with the specialized skills, including R and Python dialects most usually utilized in data analytics programming and usage; Python Training in Chennai at that point center around the commonsense application, in view of genuine business issues in a scope of industry segments, for example, wellbeing, promoting and account. Project Center in Chennai

  2. Good job! Fruitful article. I like this very much. It is very useful for my research. It shows your interest in this topic very well. I hope you will post some more information about the software. Please keep sharing!!
    SEO Training in Chennai
    SEO Training in Bangalore
    SEO Training in Coimbatore
    SEO Training in Madurai
    SEO Course in Chennai
    SEO Course in Chennai
    SEO Course in Bangalore
    SEO Course in Coimbatore

  3. I would like to thank you for the efforts you have put in penning this blog. I really hope to check out the same high-grade blog posts from you later on as well. In truth, your creative writing abilities has encouraged me to get my own site now ;)

  4. Very interesting blog. Alot of blogs I see these days don't really provide anything that I'm interested in, but I'm most definately interested in this one. Just thought that I would post and let you know
    Digital Marketing Training Course in Chennai | Digital Marketing Training Course in Anna Nagar | Digital Marketing Training Course in OMR | Digital Marketing Training Course in Porur | Digital Marketing Training Course in Tambaram | Digital Marketing Training Course in Velachery

  5. One can think of several metaphorical comparisons to describe software development, such as writing a book or building a house. Some of them are a good light in the dark, some are rather misleading. website services and support

  6. One can think of several metaphorical comparisons to describe software development, such as writing a book or building a house. Some of them are a good light in the dark, some are rather misleading. download

  7. It's very frustrating to have spyware and adware in your computer because these programs can harm your software programs plus your hardware. If you use the world wide web, these types of bugs can be very annoying. They are a regular occurrence that we occasionally tend to ignore but God forbid we must in no way agree to. Legitimate Hire a Hacker in Singapore

  8. Thanks for the lovely blog. It helped me a lot. I'm glad I found this blog. Thanks for sharing with us, I too am always learning something new from your post.

    360DigiTMG Data Science Courses

  9. I just found this blog and hope it continues. Keep up the great work, it's hard to find good ones. I added to my favorites. Thank you.

    360DigiTMG Data Science Certification

  10. Woohoo! It is an amazing and useful article. I really like. It's so good and so amazing. I am amazed. I hope you will continue to do your job in this way in the future as well.

    Business Analytics Course in Bangalore

  11. Writing in style and getting good compliments on the article is hard enough, to be honest, but you did it so calmly and with such a great feeling and got the job done. This item is owned with style and I give it a nice compliment. Better!

    Data Analytics Course in Bangalore

  12. Nice Information Your first-class knowledge of this great job can become a suitable foundation for these people. I did some research on the subject and found that almost everyone will agree with your blog.
    Cyber Security Course in Bangalore

  13. Writing in style and getting good compliments on the article is hard enough, to be honest, but you did it so calmly and with such a great feeling and got the job done. This item is owned with style and I give it a nice compliment. Better!
    Cyber Security Training in Bangalore

  14. Really nice and interesting blog information shared was valuable and enjoyed reading this one. Keep posting. Thanks for sharing.
    Data Science Training in Hyderabad

  15. Happy to chat on your blog, I feel like I can't wait to read more reliable posts and think we all want to thank many blog posts to share with us.

    Artificial Intelligence Course in Bangalore

  16. Great article with excellent information found resourceful and enjoyed reading it thank you, looking forward for next blog.
    typeerror nonetype object is not subscriptable

  17. Fantastic blog with high quality content found very valuable and enjoyed reading thank you.
    Data Science Course

  18. The rising demand for mobile applications to streamline the business processes has posed a serious challenge in front of developers to adopt rapid and ideal development process, to deliver quality software product. The basic requirements of the user tend to alter at a fast pace, hence contemplative planning for the building of customer-centric mobile application becomes essential. Every smartphone users desire to fetch the most out of their application and they would promptly switch to the next closer alternative, if the existing one fails to meet their expectations. This is where, the crucial need for agile mobile development procedure comes into play. Anxiety Treatment South Melbourne

  19. We all know what a software is and what are the basic skills needed to develop one, but what many of us don't know is what are the phases involved in developing a new software. This article will tell you what is SDLC i.e. the phases involved in developing a new software. Siteklean

  20. Hi, I have perused the greater part of your posts. This post is presumably where I got the most valuable data for my examination. Much obliged for posting, we can see more on this. Are you mindful of some other sites regarding this matter. data scientist course

  21. One can think of several metaphorical comparisons to describe software development, such as writing a book or building a house. Some of them are a good light in the dark, some are rather misleading. CAD4Sale

  22. Excellent blog thanks for sharing the valuable becomes easy to read and easily understand the information.
    Useful article which was very helpful. also interesting and contains good information.
    to know about python training course , use the below link.

    Python Training in chennai

    Python Course in chennai

  23. One can think of several metaphorical comparisons to describe software development, such as writing a book or building a house. Some of them are a good light in the dark, some are rather misleading. delivery route planner

  24. Thank you for this. Thats all I can say. You most definitely have made this into something thats eye opening and important. You clearly know so much about the subject, youve covered so many bases. Great stuff from this part of the internet. 토토사이트

  25. Jason Sudeikis’s character Kurt is a hard working man who has a very nice boss, whose tragic incident leaves the company in the hands of his bullying, cocaine-addicted son, played by Colin Farrell. 메리트카지노

  26. I am really enjoying reading your well written articles. It’s hard to come by experienced people about this subject, but you seem like you know what you’re talking about! Thanks.

    Java Training in Chennai

    Java Course in Chennai

  27. Software testing is an important part of the systems development life cycle. It has its own phase in the process and has its own specialised IT professionals. Why is it so important? What would happen if we didn't do software testing? Read on to find out more. route plan software

  28. When it comes to the political arena, we hear a lot lately about, "fake news." If something is indeed, "fake," those hearing the "fake news" are led to believe something is true when it is not. There is another arena where one needs to beware of "fake news." best fake id

  29. I have to search sites with relevant information ,This is a
    wonderful blog,These type of blog keeps the users interest in
    the website, i am impressed. thank you.
    Data Science Course in Bangalore

  30. I have to search sites with relevant information ,This is a
    wonderful blog,These type of blog keeps the users interest in
    the website, i am impressed. thank you.
    Data Science Training in Bangalore

  31. The practice of technology transfer can greatly benefit an organization. What is technology transfer? Technology transfer is the sharing of technology between two or more organizations. fake id usa

  32. A lot of cheap Ugg Boots are around in the market today, but most of the time these boots are fakes. Find out how to spot fake ugg boots when you are shopping for cheap ones. 2021 fake id

  33. I have to search sites with relevant information ,This is a
    wonderful blog,These type of blog keeps the users interest in
    the website, i am impressed. thank you.
    Data Science Course in Bangalore

  34. Thank you for this. Thats all I can say. You most definitely have made this into something thats eye opening and important. You clearly know so much about the subject, youve covered so many bases. Great stuff from this part of the internet. Lumion 10.3.2 Pro sale

  35. Every business has diverse needs and need to implement efficient software solutions which can improve work flow, processes and output. Most of the software in use is called 'Commercial off-the-shelf' (COTS) software, also called as Packaged Software. How to Delete a YouTube Video

  36. Thanks, thanks for the awesome blog post. I’m having troubles subscribing to your blogs feed. Thought I’d let you know Autodesk AutoCAD Plant 3D 2021 sale

  37. I really enjoy every part and have bookmarked you to see the new things you post. Well done for this excellent article. Please keep this work of the same quality.
    Artificial Intelligence course in Chennai

  38. Fantastic blog with excellent information and valuable content just added your blog to my bookmarking sites thank for sharing.
    Data Science Course in Chennai

  39. Fantastic Site with useful and unique content looking forward to the next update thank you.
    Data Science Training in Hyderabad

  40. You have completed certain reliable points there. I did some research on the subject and found that almost everyone will agree with your blog.
    Data Analytics Courses in Bangalore

  41. With so many books and articles appearing to usher in the field of making money online and further confusing the reader on the real way to make money.
    Data Science In Bangalore

  42. Excellent site with great content and very informative. I would like to thank you for the efforts you have made in writing.
    Data Science Training in Bangalore

  43. I finally found a great article here. I will stay here again. I just added your blog to my bookmarking sites. Thank you. Quality postings are essential to get visitors to visit the website, that's what this website offers.
    Data Science Training in Pune

  44. I really enjoy reading all of your blogs. It is very helpful and very informative and I really learned a lot from it. Definitely a great article
    Data Science Course Bangalore

  45. SEO Group Buy Tools Group Buy SEO Tools offer a huge load of uncommon features which produce outstanding results. These SEO devices are used by SEO trained professionals and progressed promoters to drive more traffic to destinations. our organizations are open to SEO beginners, digital advertisers and on a limited financial plan.

  46. Fantastic blog extremely good well enjoyed with the incredible informative content which surely activates the learners to gain the enough knowledge. Which in turn makes the readers to explore themselves and involve deeply in to the subject. Wish you to dispatch the similar content successively in future as well.

    Data Science Training in Bangalore

  47. Wonderful blog found to be very impressive to come across such an awesome blog. I should really appreciate the blogger for the efforts they have put in to develop such an amazing content for all the curious readers who are very keen of being updated across every corner. Ultimately, this is an awesome experience for the readers. Anyways, thanks a lot and keep sharing the content in future too.

    Digital Marketing Training in Bangalore

  48. Truly mind blowing blog went amazed with the subject they have developed the content. These kind of posts really helpful to gain the knowledge of unknown things which surely triggers to motivate and learn the new innovative contents. Hope you deliver the similar successive contents forthcoming as well.

    Cyber Security Course

  49. An extraordinary blog filled with amazing content which no one has touched on this subject before. Thanking the blogger for all the terrific efforts put in to develop such awesome content. Expecting to deliver similar content further too and keep sharing as always.

    Machine Learning Course in Bangalore

  50. I need to thank you for this very good read and i have bookmarked to check out new things from your post. Thank you very much for sharing such a useful article and will definitely saved and revisit your site.
    Data Science Course

  51. Your site is truly cool and this is an extraordinary moving article and If it's not too much trouble share more like that. Thank You..
    Digital Marketing Institute in Bangalore

  52. Any time you connect with the administrations of an expert, you are basically going into an agreement. Since an agreement is lawfully restricting, you need to guarantee that you are ensured as to legitimate issue. The primary thing you would need to check is that the handyman has a substantial permit.this website

  53. Wonderful blog found to be very impressive to come across such an awesome blog. I should really appreciate the blogger for the efforts they have put in to develop such amazing content for all the curious readers who are very keen on being updated across every corner. Ultimately, this is an awesome experience for the readers. Anyways, thanks a lot and keep sharing the content in the future too.

    Digital Marketing Training in Bangalore

  54. Highly appreciable regarding the uniqueness of the content. This perhaps makes the readers feel excited to get stick to the subject. Certainly, the learners would thank the blogger to come up with innovative content which keeps the readers to be up to date to stand by the competition. Once again nice blog keep it up and keep sharing the content as always.

    Cyber Security Course

  55. Truly incredible blog found to be very impressive due to which the learners who go through it will try to explore themselves with the content to develop the skills to an extreme level. Eventually, thanking the blogger to come up with such phenomenal content. Hope you arrive with similar content in the future as well.

    Machine Learning Course in Bangalore

  56. Happy to chat on your blog, I feel like I can't wait to read more reliable posts and think we all want to thank many blog posts to share with us.

    Data Science Training in Bangalore

  57. I feel very grateful that I read this. It is very helpful and very informative and I really learned a lot from it.
    Data Science Course in Chennai