Is RPA Shaping the Future of Test Automation?
Unleashing the Power of Automation Today, organizations face a perfect storm – technology changes, the fragility of customer’s loyalty, and the intense pressure from all across to keep the cost low. Businesses are forced to explore newer technologies, constantly evolve, and cut down the time from conception to deployment to meet these challenges. Furthermore, the COVID-19 pandemic has accelerated the organizations’ need to be hyper-productive. Organizations realize that they have to transform to build the capabilities that will prepare them for the future. They are thinking of ways to drive efficiency and effectiveness to a level not seen before. Moreover, there is a strong push for automation to play a pivotal role in making that happen. It is also clearly reflected in the testing domain, where any opportunity for improvement will be welcome. Organizations want to adopt RPA in testing, realizing the need to drive efficiencies and reduce manual efforts. We are at a tipping point where RPA adoption benefits are clear; what is needed is to add further efficiencies to existing frameworks. Pandemic has ramped up innovation and scale automation. Business and technology leaders will demand clear, direct benefits from investments in a digital workforce in the post-pandemic world. Automation will be taking on an even more critical role in a post-pandemic world as business resilience and cost takeout become the main destinations on the technology roadmap. A surge in RPA’s value According to Gartner, Robotic Process Automation Software Revenue worldwide will reach nearly $2 Billion in 2021. Despite Economic Pressures from COVID-19, RPA market forecast to grow at double-digit rates by 2024. In the last decade, automation has evolved and matured with time and changing technologies. Automation in testing is not new, but its effectiveness has been a challenge – especially the associated expense and lack of skill sets. Within an enterprise, RPA can cut through the maze of toolsets, replacing them with a single tool that can talk to heterogeneous technology environments. From writing stubs to record and playback to script less and modular testing, and now to bots, we witness a natural evolution of test automation. In this next-gen testing brought about by RPA orchestration, an army of bots will drastically transform the time, energy, and effort required for validation and testing. We are gradually heading towards test automation that requires no touch; no script works across heterogeneous platforms, creates extreme automation, and allows integration with opensource and other tools. According to Forrester, “RPA brings production environment strengths to the table.” It translates into production-level governance, a wide variety of use cases, and orchestration of complex processes via layers of automation. RPA allows organizations to democratize automation very rapidly within the testing organization. Needless to say, RPA has an advantage over traditional tools in that it can be deployed where they fail to deliver results. For instance, when the testing landscape is heterogenous with complex data flows, there is a need for attended and unattended process validation and to validate digital system. These all need an RPA solution that can bring in a tremendous amount of simplicity for building out bots quickly and deploying them with the least amount of technical know-how that even business stakeholders can understand. The adoption of RPA is gradually gaining immense momentum. However, some organizations get perplexed when it comes to identifying and selecting the right processes for RPA as some operations are more suited for automation whereas some are not. Selecting the right process and internalizing a technology require a lot of thinking through prior to implementation. As a thumb rule, however, processes that are manual, repetitive, and rule-based are more suited for RPA implementation. Within the world of automation, RPA’s role is quickly growing. It has already gained popularity in software testing for efficiently eliminating repetitive manual efforts in end-to-end testing by automating workflows and dissolving data silos. Accelerating the pace of digital Transformation with RPA Digital transformation has led to a paradigm shift in how quality is perceived and assured. RPA helps organizations reshape how they operate and their level of responsiveness at most touchpoints within the value chain. The core component of any organization’s intelligent automation tech stack is RPA. It enables rapid end-to-end business process automation and accelerates digital transformation journey. Robotic Process Automation (RPA) is a key driver on the digital transformation journey—which is why, according to Deloitte, 53% of companies surveyed are ready to begin implementing RPA. Are you automation-ready? According to recent stats 30 – 50% initial RPA implementation projects fail (Report: Get Ready for Robots – Ernst & Young). With this, businesses are not sure about the desired results after investing in the technology. Enterprises should start bringing RPA into the testing fold if they plan to save resources, reduce defects, and prepare for a future that is already closer than we perceive. Fast-track your Intelligent Automation journey with Tavant. We believe that intelligent automation is not only about new-age technology. For Tavant, it’s much more. We are working with organizations to orchestrate new ways of working and embed intelligent automation into their operations to drive continuous innovation and business impact. Want to know more about the potential of RPA in testing? Reach out to us at [email protected] or visit us at https://www.tavant.com to understand how we can help your business navigate next.
Accelerating Agility and Innovation with Salesforce Financial Services Cloud
Evolving customer expectations Uncertainty is the name of the game in 2021. The surge and ebb in markets drive the financial sector to look for technology solutions that keep them anchored. The disruption unfolded by the COVID 19 pandemic brought to light some stark realities. Customer expectations are shifting goalposts, and the trick to be ahead is being well informed. Artificial intelligence, advanced analytics, and automation make it easier to leverage insights and meet customer expectations. But to leverage those capabilities requires a solid underpinning technology foundation. How upgrading to Salesforce Financial Services Cloud can enable transformation. Salesforce Financial Services Cloud brings advanced capabilities to stitch together marketing, sales, and services and break down silos to deliver a seamless customer experience. If you haven’t done so already, migrating to Financial Services Cloud should be one of your top priorities in 2021. However, before you make a move, reflect on these three key considerations: Objective Assessment of Existing Systems Financial institutions have been running on a relatively limited set of standard technology solutions historically. As the complexities of markets increase, constant customization to meet evolving business needs has become a limiting factor to business growth. A move to the cloud to leverage tools that offer increased flexibility is a clear path to take, but how best to chart this journey? You may decide to take an incremental migration route and integrate legacy systems or completely move to FSC. If you are an existing Salesforce user, you have an edge as the updates are likely more straightforward. However, it is important to have a thorough assessment of your current CRM and IT landscape to arrive at a migration strategy that balances the operational challenge of completing the migration promptly with the objective of ensuring a platform that empowers support for strategic business needs over a longer-term. Just getting on FSC is one thing – it is another thing to get on FSC in a manner that will support long term business needs effectively. Clear Strategy and Executable Plan Demands of instant gratification from an increasingly digital-savvy clientele drive banking, insurance, and wealth management institutions towards solutions tailor-made for their needs. The urgency makes it all the more important to have a well-defined plan that delivers on the promise. A clear strategy to make a move to FSC keeps you anchored and allows you to build a thorough execution plan. Given that businesses have realized a 41% increase in customer satisfaction, 37% uplift in productivity, and accelerated decision making by 40%, the benefits of FSC are well established. However, your move to FSC must be guided by your business goals, and the timeline you set for the migration must deliver to business expectations. Strict Evaluation of Security Requirements The financial sector has been at the receiving end of security breaches and cybercrime. In 2020, 71% of cyberattacks were directed at financial institutions. Security of systems, applications, and data is thus, non-negotiable. Salesforce Financial Services Cloud comes with advanced security features to alleviate your concerns. However, you must map your security requirements and define metrics to suit your business priorities. With FSC, you have the opportunity to revisit your security posture with advanced encryptions and better access control. Creating value with the cloud Salesforce Financial Services Cloud is a force multiplier. It equips your teams with the right insights and better collaboration. Your advisors deliver quality advice to clients, and your retail touchpoints win customer delight. However, you must decide on the right partner with proven capabilities to steer the migration to optimize your path to these objectives. What next? Tavant has won the trust of financial institutions across the globe, delivering them an unmatched cloud advantage with custom made migration strategies and comprehensive managed services. To learn more about how we can help you gain a competitive edge with Salesforce Financial Service Cloud, visit here or mail us at [email protected]. FAQs – Tavant Solutions How does Tavant integrate with Salesforce Financial Services Cloud to accelerate innovation?Tavant provides seamless integration with Salesforce Financial Services Cloud through pre-built connectors, API integrations, and shared data models. This integration enables lenders to leverage Salesforce’s CRM capabilities while utilizing Tavants specialized lending technology, creating comprehensive customer relationship management and loan processing workflows. What benefits do lenders gain from Tavant Salesforce Financial Services Cloud integration?Lenders benefit from unified customer views, streamlined lead-to-loan processes, enhanced customer service capabilities, and improved sales productivity. The integration provides 360-degree customer insights, automated workflow triggers, and consistent data across sales, lending, and customer service teams. What is Salesforce Financial Services Cloud?Salesforce Financial Services Cloud is a specialized CRM platform designed for financial services organizations. It provides tools for relationship management, client onboarding, compliance tracking, and collaboration, specifically tailored to meet the unique needs of banks, credit unions, and other financial institutions. How does Salesforce Financial Services Cloud improve agility?Salesforce Financial Services Cloud improves agility through rapid customization capabilities, automated workflows, real-time collaboration tools, mobile accessibility, and integration with third-party financial services applications. It enables quick adaptation to market changes and customer needs. What are the key features of Salesforce Financial Services Cloud?Key features include household and relationship mapping, financial account management, goal tracking and planning, collaborative action plans, compliance and audit trails, mobile banking integration, and specialized financial services analytics and reporting capabilities.
How to Decipher Customer Journey with Relevant Advertisement Attribution
Every advertiser has a unique context. Are you enabling the right choices? A large multinational company is launching a new lifestyle product and is looking to gain the attention of high-income, mid-career women across tier-one cities. Their advertising campaign has unique requirements, and they want the best slots suited to their product promotion. How would you guide them to make the best choices and drive premium returns for your platform? You need deep insights on advertising performance across segments that demonstrate the optimal ROI to win the trust of the advertisers. You can promote specific segments, drive higher revenue, and make better pricing decisions based on quantifiable metrics unearthed with actionable analytics. Accuracy in advertising attribution is your key for precise audience targeting, campaign optimization and improved performance that boosts the bottom line while bolstering top-line growth. Campaigns leave important cues. Are you listening? The new product line you launched last week has caught the imagination of young shoppers. Your e-commerce site has an upsurge in traffic. You used different media to advertise and promote the product line, and it has worked. The ad spots on prime-time TV and jingles on FM radio are on for a week now. You placed inserts in newspapers with QR code for discount coupons. The redemption of those coupons is doing well too. Your social media campaign is running in parallel, and you are ready for another round of emails to roll out referral offers. You are convinced that your ad spend has delivered the desired results. It is important to trace your customer journey through all the different touchpoints up to the conversion or buying stage. Your ad spend needs to be rationalized and focused on the media mix that delivers optimum results. In short, you need to analyse ad attribution, to zero in on your campaign effectiveness. Ad attribution unlocks the significance of every touchpoint to conversion. Personalized campaigns demand an intimate understanding of customer behavior, as well as customers’ channel and platform preferences. Your campaign ROI depends on your knowledge of the customers. Advertising attribution processes allow you to trace your customers’ actions across multiple touchpoints to reveal the levels of interaction that brought them to the point of sale. This data is crucial for evaluating past campaign performance and intelligently planning the next ones for better outcomes. Advertising attribution is a quantitative measure of each touchpoint in nudging the customer journey towards conversion. Single-touch ad attribution – for example, measuring the first click or last click for a given promotion on any platform – can deliver straightforward analysis if the action is definitive, such as a discount offer for the first 50 customers within a day, advertised on Facebook. The marketer can assign success to the specific promotion. Multi-touch journeys are deciphered through various ad attribution models. A multi-touch customer journey is more difficult to attribute. For instance, a customer may have seen a newspaper insert ad, noticed a similar advertisement on a social platform, received a promotional offer through a friend, checked out the company website, and then received remarketing enforcement before making a purchase. Now, every touchpoint is a nudge forward and must be accordingly scored. This multi-touch attribution is a flexible scoring model for marketers to assign due credit to each interaction for a comprehensive performance insight. Linear models assign equal value to each touchpoint. Shapely values, such as U and W-shaped scoring models, give more credit to the first and last touchpoints and first, middle, and previous touchpoints, respectively. Some marketers prefer a time decay model that treats the touchpoints closer to purchase as more important than touchpoints at the beginning of the journey. Advanced algorithmic and statistical models leverage AI and ML for ad attribution. The complexity of advertising data requires advanced custom models to assign performance metrics to every touchpoint adequately. Data-driven and statistically evolved, these attribution models leverage AI-based algorithms to score the customer purchase decision milestones appropriately. Machine learning algorithms guide the marketers in deciphering conversion probability to plan promotions accurately. The right choice of ad attribution model drives campaign performance. The pressure on marketing budgets has created a greater need for campaign precision. Marketers must choose the right attribution model to improve campaign performance and sales lift. However, there are no absolutes in this game. No statistical model, no matter how evolved and data-rich can guarantee 100% accuracy. Marketers must consider models that align with their customer journey and campaign intricacies. An advanced AI and ML-powered analytics platform can algorithmically design attribution models to deliver timely and accurate metrics. Making the right choice for ad attribution is intrinsic to campaign success. Marketers can leverage these attributes to design just-in-time campaigns with higher confidence. Which ad attribution model would you bet on for your campaign analysis? Please share your thoughts with us at [email protected]; or to learn more about Tavant’s media solutions, click here.
Managing Product Recalls – Harness the power of Data
Product Recall – a word that could send chills down the spine for some manufacturers, spark media attention, hamper hard-earned reputation, and, no doubt, pose an unwanted financial burden. Despite stringent rules and regulations around product safety, recalls regularly make headlines. While no executive wants to face a recall, how it is handled determines the actual impact on the business. If an organization appropriately reacts and adjusts its operations, it can minimize damages and enhance its brand’s reputation for transparency, honesty, and genuine customer focus. Even after the need for a recall has been identified, the costs can quickly increase according to several associated factors list below: Identification The seriousness of the recall needs to be established; for example, is it contained to a consignment or batch, or a larger issue? If the product batch is at a warehouse waiting to be shipped, the recall cost will be far less than if the product is already in the consumer’s hand. Speed is of the essence at this point because if the product continues along the supply chain, the issue and cost can accelerate. Perception The emotional component of a recall can also wreak havoc on costs. For example, China’s parents are still haunted by an incident in 2008, the melamine baby milk scandal, and killed six infants. Many parents lost their trust in domestic brands, paving the way for foreign companies. Regulatory reporting Certain jurisdictions mandate reporting a product-related issue if any defects become known to a manufacturer or distributor, which may necessitate a recall. Companies need to decide which media channels should be used to release the recall notice. Social media platforms have reduced the potential costs, but advertising space in newspapers, television, and radio may still be required, and costs can increase considerably. The message about the recall will need to be carefully crafted to ensure it is clear and concise while also informing consumers that the company manages the issue effectively. Logistics The product may need to be physically removed from outlets, supermarkets, and/or showrooms. Retailers/dealers may also need to be reimbursed for their costs and loss of trade and remove the affected product from their shops/showrooms. The ripple effect If a faulty product is used as a component in other products, then the recall cost will also be considerably higher. While this is most common in the automotive sector, where a faulty part can easily damage other products in proximity, which must also be paid for, other sectors see similar situations. Business interruption Depending on the severity of the recall, lawyers, consultants, and extra staff may be required to help manage the recall. Also, the potentially lost person-hours associated with a recall’s distraction could be significant. Rehabilitation While the actual product recall is likely to be incredibly costly for a client, the expense does not stop once everything is safely off the shelves. The next significant cost can be returning the company and brand to its position before the recall, which may include extra promotional expense and sales promotion offers. Prevention is Key – what more can we do? AI and Machine Learning to the rescue! Big Data and emerging technologies underpin a positive response to an adverse event. Data analytics and emerging technologies in the supply chain, combined with customer intelligence and product knowledge, can help companies of all sizes more nimbly mitigate the detrimental effect of recall situations while simultaneously addressing customer concerns and preventing future recalls. We are in the era of industry 4.0, where smart and connected devices, powered by machine learning and AI, can predict faults and anomalies in the manufacturing process. Another way AI and machine learning can help the manufacturers is by analyzing the flood of manufacturing data received by machines. By analyzing this data thoroughly and looking for anomalies via machine learning, you can predict catastrophic failures earlier, avoiding total breakdown and saving businesses large amounts of revenue and brand equity. This, in turn, minimizes businesses’ need to issue recalls routinely or for consumers to suffer the potentially dangerous fallout from faulty equipment. Imagine being able to predict something before it does, pre-empt failures, and proactively take corrective actions. This is where artificial intelligence and machine learning come into play. The ability to create a full digital copy of an engine is achieved by creating ‘Digital Twins’, granular virtual copies of parts in the manufacturing process, which are enabled by deep learning and artificial intelligence. By creating ‘Digital Twins’, insights can be garnered to address the tiniest of issues that would otherwise be missed during a manual inspection process. When it comes to safety issues, the sooner they are discovered, the better. The advanced data analysis can help identify the early warning signs. Using multiple databases, complaints & reviews can be tracked and researched to pinpoint patterns with specific parts and performance. By investigating potential safety concerns and developing campaigns earlier, manufacturers can perform outreach to equipment owners more effectively to protect both the public and their brands. Text analytics platforms can empower big manufacturing companies to quickly assess their customers’ expectations, possible miscommunication issues, and the impact of the company’s actions on customer sentiment. This approach to crisis management enables businesses to seamlessly align internally and place their customers at the center of their product recall strategy. By combining machine learning and natural language processing, an organization can begin laying the foundation for cognitive analytics or artificial intelligence—sophisticated ways to make faster and more accurate decisions down the road. In the study, published in the Journal of the American Medical Informatics Association (JAMIA) Open, the researchers taught an existing “deep-learning” AI called Bidirectional Encoder Representation from Transformations (BERT) to predict food product recalls from Amazon reviews with about 74% accuracy. The AI also identified 20,000 reviews that suggested potentially unsafe food products that had not been investigated. Conclusion Putting strong measures to handle and counter the reasons that caused product recalls can help keep potential adversaries at bay. It fosters the goodwill needed to maintain
The Big Leap: Tavant Accelerates Growth; Surpasses Significant Milestones
2020 is now – finally – hindsight. It was undeniably a year of unprecedented disruption. Merriam-Webster recently announced that the Word of the Year for 2020 is pandemic. However, my personal vote goes to perseverance. Despite the challenges and twists and turns, Tavant is so proud to have achieved several major milestones last year –thanks to the perseverance of our team. These significant milestones demonstrate our commitment to enabling clients across multiple tech industries to rapidly accelerate their digital transformation journey and provide the best possible customer experience. Aligned with this trend, the key milestones that helped fuel the company’s unprecedented growth include: Product adoption and new product launches The debut of the digital software factory Launch of Banktech business Key strategic alliances 20 prestigious industry awards including Stevie® Award for AI and Machine Learning Reflecting on significant milestones in 2020: Tavant saw a surge in growth and increased adoption of its flagship AI-powered product suite, Tavant VΞLOX. Tavant’s core growth acceleration also came through its Digital Software Factories (“Digital Factory”) at its new technology innovation center in Dallas. Tavant further expanded its fintech and digital lending business by launching its Proptech business, bringing technology and innovation together to address common customer challenges in this burgeoning sector of Real Estate. Furthermore, at the American Banker’s Digital Banking 2020 conference, Tavant announced its expansion to new business lines with the launch of its Banktech practice in New York. Tavant expanded its industry-leading aftermarket product suite beyond warranty with salesforce connectors like Field service, B2B, and CPQ, available on the service cloud. They work seamlessly in conjunction with Tavant Warranty and support the entire gamut of Service- lifecycle management for a wider and faster digital transformation. Tavant Warranty got featured in Salesforce road to recovery apps. 2020 proved to be a year of key successful alliances. The company teamed up with Microsoft and Land O’Lakes Inc. to help farmers generate new insights from their crops, leveraging AI technologies. Tavant entered a strategic partnership with Softworks AI to deliver the touchless mortgage promise. The company also launched FinDecision, which improves loan quality while enhancing the overall borrower experience. Additionally, Tavant FinConnect and FinLeads made their debut on the Salesforce AppExchange, the world’s leading enterprise cloud marketplace. Early 2020, the company got recognized for driving fintech innovation forward during the year and bagged a few major fintech industry awards. Additionally, it was named to prestigious IDC FinTech Rankings by IDC Financial Insights. Tavant was also named the winner of a Stevie® Award in the 18th Annual American Business Awards® in the Business Technology category for Artificial Intelligence/Machine Learning solutions. Furthermore, Tavant’s next-gen quality engineering (QE) business was recognized by Everest Group in its PEAK MatrixTM assessment report. Tavant was also recognized as a leader in the IDC MarketScape for manufacturing warranty and service contract management applications and made it to the IDC TechScape: Worldwide Service Life-Cycle Management and Servitization Optimization in Manufacturing, 2020, where it was mentioned in Service Analytics and Business Intelligence and Warranty Software sections. The company was also mentioned in the aftermarket, professional, and life sciences services section in the IDC Market Glance: Next-Generation Automotive and Transportation Strategies report. Empowering businesses to build resilience for today and what is ahead. Tavant is grateful to its associates, partners, and community for navigating through the pandemic together. This adversity presented a historic opportunity for innovation and digital transformation. Tavant is uniquely positioned to leverage its technical and domain expertise to drive value for all its partners. We built a solid foundation in 2020 and are planning to continue to execute our strategy in 2021. We aim to empower companies to accelerate their digital transformation journey to respond, recover, and thrive in the new normal reality in a most secure and cost-effective way. These significant milestones are a true testament to how our customers bolster their digital strategy to improve profitability and enhance their customer experience using Tavant’s products and solutions. We will continually support your digital journey and help you rise to new levels of business resiliency, and stay relevant in an uncertain world. To learn how we help our customers use digital to create value by reinventing the core of their business, visit www.tavant.com or reach out to us at [email protected].
Agones – a Kubernetes-centric Game Server Toolkit
A typical multiplayer gaming deployment and its maintenance can be a complex process. Unlike web applications, game servers have a complex lifecycle. They need to connect to the clients directly, and the latency needs to be at the lowest for a better user experience. Other challenges include aspects like scaling for surges, planning, and continuous delivery of games changes to players across different regions, and the list goes on. Choosing the right solution becomes important to reduce the overall complexity. Kubernetes is one of the commonly used container orchestration and clustering solutions. It allows automating application deployment, scaling, and management. However, Kubernetes does not address several game-specific needs. Most companies end up writing custom solutions to allocate game servers, manage players, and auto-scaling. Though Kubernetes can work, it involves a certain degree of custom implementation. Agones Agones is an open-source platform for deploying, hosting, scaling, and orchestrating dedicated game servers for multiplayer games, built on top of Kubernetes. Agones replaces custom/proprietary cluster management and game server scaling solutions with an open-source solution to focus on more important aspects of building a multiplayer game. Agones can run on any cloud or on-premise and scale as needed. This helps use any existing on-premise infrastructure, while the cloud can be used for spikes during peak hours. Architecture Agones integrates with Kubernetes and exposes certain APIs, making it easy to handle the game-specific needs of clustering. Agones focuses on online multiplayer fast-paced games requiring dedicated, low-latency game servers with the state usually held in memory for match’s duration. These game servers have a short lifetime; a dedicated game server runs for a few minutes or hours. These fast-paced games are sensitive to latency, hence requiring dedicated game servers also need a direct connection to a running game server process hosting IP and port, rather than going through load balancers. Agones allows Kubernetes’ tooling and APIs to create, run, manage, and scale dedicated game server processes within Kubernetes clusters. Agones also supports out of the box metrics and log aggregation to track and visualize what is happening across all the game servers. Agones Integration Agones provides command-line integration through Kubernetes kubectl. But we likely want to interact with Agones programmatically. This allows Matchmaker to interact directly with Agones to provision a dedicated game server. Agones gives two ways to integrate, one Kubernetes APIs and another directly with Agones API using their SDKs. Agones SDKs are available in various programming languages like Unreal engine, Unity, C++, Node.js, GO, etc. The above diagram shows the game server allocation workflow. Matchmaker requests a game server through Kubernetes APIs where Agones intercepts the call to serve the request. Agones changes the game server’s status to Allocated and returns the game server IP and port details to the Matchmaker, which in turn is sent to the game client. Allocated game servers are not touched by Kubernetes for scaling down or termination if the status remains ‘Allocated.’ Once the game client finishes the session, the shutdown API called the game server is marked back to the ‘Ready’ state. Google Game Servers It is easy to run a cluster in a region or a zone. However, if we wish to run it in multiple areas around the world, things get more complicated. Google Cloud Game Servers is a management layer that sits on top of open-source Agones. It provides a great set of functionalities and features that make it easy to orchestrate and scale multiple clusters of Agones around the world while still not locking into a specific vendor package. It gives a lot of flexibility and power to run the game servers precisely the way we want to. The following diagram shows an example of the deployment of more than one Agones cluster across different cloud providers and on-premise data centers. Here Realms are a grouping of game server clusters defined by users based on latency, region, etc. Game server clusters are Agones clusters that have registered themselves with Google Cloud Game Servers so that Google Game Servers are aware of them. The rest of the Agones cluster functionalities remain the same. We can run the game servers on the Agones cluster the way we want while GCP deals with autoscaling and managing the fleets. Downsides Agones is a relatively newer framework that recently came out of the beta release. Agones still does not support Windows game servers, which means all the multiplayer games running on Windows will have to wait. Some of the basic game specific functionalities like player handling were added only recently as part of the alpha release. Still away from multi-cluster policies or multi clusters across the globe, and the only workaround is using GCP Game Server. In essence Overall, Agones can be a great open-source tool for deploying, orchestrating, and scaling dedicated game servers. Currently, there are no other ‘openly available’ tools in the industry, and the open-source community seems to be excited about this. Though there are other SaaS services like GameLift by AWS and Playfab by Azure, with the SaaS services, we will not be able to use on-premise infrastructure. The features provided may not in-line with what we are looking for, while open-source tools are primarily shaped by the community. Agones is here at the right time, and it is just a matter of time before its popularity explodes.
Is the Pandemic Accelerating Digital Disruption?
The pandemic has caused not only a global economic decline but a complete disruption in the way we rely on to communicate with our clients and prospects alike. This is the time when customer loyalty and conviction can be easily won or lost. And for those of us who have not perfected their omnichannel reach or launched their desired digital strategy – you are running against the clock!! We can all admit that none of us was ready, and many were knocked off balance, shifting from standard daily routines to a completely virtual workplace environment. Between longer working hours from home and reinventing the way we meet and communicate, there is no doubt COVID introduced a state of disruption upon all of us. As I see it, COVID didn’t disrupt the way we do business with our customers and colleagues. COVID simply accelerated the “State of Disruption” that many of us knew we’ll have to face sooner rather than later. The only question we should all be asking ourselves is how ready we are in our current “state of disruption”. Even those of us who practice ‘digital disruption’ are not always ready. I still catch myself sometimes telling clients I’m ready to book a flight and meet at their earliest convenience. Of course, reality sets in when I am politely reminded a video conference would suffice just fine. It’s never easy to face uncertainty, but during these challenging times, experience shows us that there’s also no better time to build and prepare for the next phase in our future. Outmaneuver Uncertainty with Tavant Organizations from across the world are working hard right now to serve their customers and frontlines. And many rely on partners like Tavant to ensure they receive the support, top IT talent, and cutting-edge financial technologies to help them get back to what matters most in an efficient and minimally invasive manner. Tavant’s partners happen to be some of the most renowned brands in financial services, professional sports, digital media, and even motion pictures brands. Rising to the challenge and remotely supporting partners during COVID is not easy and new challenges present themselves almost daily. However, the critical components that create Tavant’s formula for success somehow outweigh the risks and allow us to deliver WINS, even during these unprecedented times. Many of our clients refer to Tavant as truly an extension of their internal teams. And in an event such as COVID-19, we knew early on that formula to succeed and deliver results required us to reimagine how we do business by shifting technical capabilities and entire teams from brick-and-mortar locations to virtual. We realized from the get-go that in order to deliver disruptive innovations in a virtual environment, we had to prove to ourselves first that our virtual business model works and works well. Some of the new user experiences and operational excellence models we are deploying for partners entail: Digital account opening (DAO) with hyper-personalized customer recommendations AI-powered lending solution for digital loan origination (DLO) and auto-decisioning capability for consumer & commercial banking customers Digital engagement platform with API first approach and over 125 leading ecosystem partners for end-to-end compliance, KYC, and BaaS (banking as a service) capabilities. Add connectors to launch customized products and deliver personalized experiences Salesforce driven, Tavant’s Customer 360° solution for a complete customer and workforce view, as well as engagement across all digital channels At Tavant, we are always focusing on each other – supporting, informing, and inspiring our people. The most important business lesson anyone can learn from today’s pandemic is the importance of planning ahead, conditioning, and adopting new policies, operational procedures, and digital technologies to meet the rising levels of omnichannel expectations from our customers, no matter where they are and what they need. Moving forward, we remain diligent to continue and evaluate our clients’ exposures to technological vulnerability, risk and monitoring the stability of legacy systems while tracking and advising on various digital transformation paths moving forward. To all our clients, prospects, and industry colleagues leading their paths across the world, we wish good health and fortune to all the teams. Tavant is Helping Businesses Navigate the Pandemic Tavant is helping clients outmaneuver uncertainty in both the short- and long-term. We’re supporting organizations to emerge stronger and flourish in the days ahead. You can count on us to help your company stabilize revenue, keep pace with evolving demand and forge new, disruptive, and sustainable growth pathways. Through it all, we are here to help. For a deeper discussion, please mail the author Michael at [email protected] FAQs – Tavant Solutions How did Tavant help lenders adapt to pandemic-accelerated digital disruption?Tavant provided rapid digital transformation solutions during the pandemic, enabling lenders to shift to remote operations, implement touchless loan processing, and deploy digital-first customer experiences within weeks. Their cloud-based platforms allowed lenders to maintain business continuity while meeting increased demand for digital lending services. What pandemic-resilient features does Tavant offer for future disruption preparedness?Tavant offers cloud-native architecture, remote workforce enablement tools, automated processing capabilities, and flexible deployment options that ensure business continuity during disruptions. Their platforms provide scalable infrastructure, secure remote access, and automated workflows that maintain operations regardless of external circumstances. How did the pandemic accelerate digital transformation in lending?The pandemic accelerated digital transformation by forcing immediate adoption of remote operations, touchless processes, and digital customer interactions. Lenders rapidly implemented online applications, digital document processing, and virtual closings to maintain business operations while meeting social distancing requirements. What digital lending changes from the pandemic are permanent?Permanent changes include widespread adoption of digital applications, remote loan processing, virtual appraisals, electronic closings, and hybrid customer service models. Many customers now expect digital-first experiences, and lenders have maintained these capabilities as standard offerings. How can lenders prepare for future disruptions?Lenders can prepare for future disruptions by investing in cloud-based systems, implementing automation, establishing remote work capabilities, creating flexible operational processes, and maintaining robust cybersecurity measures. Building resilient, adaptable technology infrastructure is essential for business continuity.
The Intuition Behind U-Net CNN Architecture
The U-Net architecture initially developed for Biomedical Image Segmentation has found consistent success in being adapted to various additional tasks. It was also used as the backbone of the PCNet architecture in the Self-Supervised Scene De-Occlusion Paper which I discussed in my earlier blog. As a sequel to that blog, I share a few of my observations about the U-Net architecture. The main advances of the U-Net architecture are better localization on the end output and its speed. The output of the network is a labeled segmentation mask, as shown in the image (c) below, as taken from the paper. Labeled segmentation essentially stands for assigning a class label to each pixel. Below is an image from the U-Net paper showing the left half convolutional layers, the right half de-convolutional layers, and the residual connections which occur between corresponding levels of the ‘U’ architecture. The distinctive ‘U’ architecture occurs because of the greater number of layers dedicated to the de-convolutional steps than many other architectures (in many networks, the vast majority of layers are dedicated to convolutional layers). It is the additional de-convolutional layers which, along with the residual connections, lead to better localization in the segmentation mask output. The specific layers are fairly typical of a traditional CNN, so I will not address those here, but they can be found in the Network Architecture section of the paper. The U-Net paper-primarily shows that the de-convolutional task is non-trivial and requires a significant number of layers to produce useful masking of the original image. Additionally, the residual connections allow for greater access to information that is likely useful for the de-convolution task, both increasing the accuracy of the resulting de-convolution and freeing up the main bottleneck (at the base of the U) to encode more meaningful information. Furthermore, the paper shows that this architecture performed well with very little training data available (they relied heavily on typical augmentation techniques, specifically elastic deformations), which is clearly a useful feature for practical business applications. Wrapping up Thoughts Overall, the U-Net architecture was a clear choice for the Self-supervised Scene De-Occlusion paper. It is an excellent architecture for cases where output segmentation masks are most helpful than more traditional computer vision classification techniques such as bounding box.
Powering the Complete Lending Value Chain with AI
Mortgage lending is a data-intensive business. The volume of available data grows drastically in a mortgage company, with more added every day from calls and payment systems. Needless to say, the success of any lender is built on thoroughly understanding its borrower data, the speed at which it can leverage such data, the degree to which it can meet evolving customer expectations, and the technology it adopts to process it. Challenges faced by lending companies in changing times As the COVID-19 pandemic continues to create changes, many Fintech companies are under stress on many fronts. The pandemic has also exposed modernization needs for critical systems. At the same time, lenders also need to address the challenges such as fluctuating origination volumes, increasing costs, higher expectations from borrowers, and rising competition from new, technology-savvy entrants amid changing times. To compete in this environment — to even expect to stay in business — legacy lenders have started showing their willingness to abandon multiple disparate systems with fragmented data, rigid and inefficient legacy systems & processes, and embrace cutting-edge automation and digitization. How can Fintech fuel innovation amidst changing times? Fintech companies tend to have unique advantages that allow many to create new ways of delivering real value in the current environment and position themselves to thrive in the longer term. Fintech companies have several attributes that give them the agility needed to create and deliver new solutions rapidly. Generally speaking, they are Adept at analyzing and harnessing various types of data, such as credit and underwriting data Exceptionally focused on a seamless and delightful digital customer experience Unlocking the real potential of Artificial Intelligence and Machine Learning to power the complete lending value chain The Fintech space has always been about disruption and driven by innovation, whether it is investments, payments, lending, capital markets, wealth management, or personal finance. From growing revenues, reducing churn, expanding customer bases, or managing risk and efficiencies, AI and machine learning can provide powerful tools for the top fintech companies in the world. Fintech’s traditional tech stacks were not designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What is needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI and ML have proven to be very powerful at interpreting and recommending actions based on real-time data streams. Machine learning has become ubiquitous, but organizations are struggling to turn data into value. The stakes are high. Those who advance furthest fastest will have a significant competitive advantage; those who fall behind risk becoming irrelevant. It is time for a change Because of rising loan costs, improving operational efficiency has become just as important to lenders as enhancing the borrower experience, maybe even more so. Undoubtedly, why a growing number of lenders have begun embracing artificial intelligence (AI) and machine learning, which remains the two most talked-about next-gen technologies in the mortgage industry today. AI models can help fintech organizations throughout the lifecycle of the loan process. For instance, in the initial phase of the loan process, AI can automate and optimize processes around identifying new target customers, predicting propensity to convert, risk-based pricing. And further, along the lifecycle, AI and ML can bring efficiencies and speed in loan processing through more accurate risk models, detection of fraud, and assisting underwriters with decisioning, managing customer churn, default prediction. These reduce costs, improve processing times, and customer experience. The light at the end of the tunnel The current uncertainty has undeniably placed businesses across the globe under economic duress, and Fintech is no exception. Albeit, many companies in the mortgage arena are already rising to the challenge and arranging their products and services to keep up with the evolving needs of customers who are struggling through the pandemic themselves. What is more, given their differentiated capabilities—namely innovation, resilience, and adaptability— many Fintech companies are well-positioned to survive the crisis and contribute to the industry in meaningful ways once the crisis is behind us. For this unprecedented crisis, if history provides any lessons, it may be that adversity inspires creativity. Final Thoughts Maintaining operational resilience is top of mind of most mortgage companies. Lenders that capitalize on next-gen technology to re-imagine their credit risk scoring and decision systems can enhance the quality of leads and make better recommendations while cutting down manual activities, maintenance costs, and losses. Transform Decision Making Tavant solutions enable customers to make better business decisions every day by incorporating the latest developments in machine learning. To learn more about Tavant’s machine learning-based conditions management & decisioning platform, visit here or reach out to us at [email protected]. FAQs – Tavant Solutions How does Tavant implement AI across the complete lending value chain?Tavant integrates AI throughout every stage of the lending process, from loan origination and underwriting to servicing and collections. Their platform uses machine learning to automate decision-making, reduce processing times, and improve accuracy in credit assessment, document verification, and risk management. What specific AI capabilities does Tavant offer for lending value chain optimization?They provide intelligent document processing, predictive analytics for risk, automated underwriting engines, real-time fraud detection, NLP for customer interactions, and machine learning models that improve decisions based on historical patterns. What is AI in the lending value chain?AI in the lending value chain refers to applying artificial intelligence to all stages of lending, including origination, underwriting, processing, servicing, and collections, to automate processes, improve decision-making, and provide predictive insights. How does AI improve lending efficiency?AI automates repetitive tasks, reduces document review time, speeds credit decisions, minimizes errors, offers 24/7 processing, streamlines compliance checks, and provides predictive analytics for risk. What are the benefits of end-to-end AI lending solutions?Benefits include reduced costs, faster processing, better customer experience, enhanced risk accuracy, improved compliance, and scalability without proportional staff increases.
Driving Efficiency with MLOps & Microsoft Azure
The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. However, as is always the case with new technology, machine learning comes with its own set of challenges: Concept Drift – Accuracy of model degrades over time due to disparity in training data vs production data Locality – Pre-trained models’ accuracy levels change with changing demography/geography/customer Data Quality – Changes in data quality affect accuracy levels Scalability – Data scientists, while good at creating models, don’t necessarily have the skills to operationalize models at enterprise scale Process & Collaboration – A lot of models are developed and remain confined to sandboxes or within silos in an organization with no clearly defined process for the model lifecycle Model Governance – Most data science projects have no model governance – who can create models, who can deploy them, what datasets were used for training? Most ML projects do not have this defined clearly A typical ML model lifecycle Here’s what a machine learning model lifecycle looks like: What is MLOps According to Wikipedia, “MLOps (‘Machine Learning’ + ‘Operations’) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.” How is MLOps different from DevOps So, is MLOps just another fancy name for DevOps? Since machine learning is also a software system, most of the DevOps practices apply to MLOps too. However, there are some important differences: Team skills: a machine learning team usually has Data Scientists or/and ML researchers who may be excellent with different modeling techniques and algorithms but lack the right software engineering skills for building enterprise-grade production systems. Continuous Integration (CI) is not only about testing and validating code and components, but also testing and validating data, data schemas, and models. Continuous Deployment (CD) goes beyond deploying a package or service. It requires deploying an ML training pipeline that should automatically deploy another service (model prediction service). Continuous Testing is unique to ML systems, which is concerned with automatically retraining and serving the models. Monitoring – ML uses non-intuitive mathematical functions. It requires constant monitoring to ensure its operating within regulation and that the models are making accurate predictions. Implementing MLOps with Azure Machine Learning Tavant’s Manufacturing Analytics Platform (TMAP) is an analytics and machine learning-based platform that provides important business insights to our customers in the manufacturing domain especially Warranty. It is based on Azure and we use Azure Machine Learning’s MLOps features for managing our models’ lifecycle. Here’s a list of features that Azure provides for MLOps: Workspace – An Azure Machine Learning workspace is the foundational resource that is used to experiment, train and deploy machine learning models Development Environment – Azure ML provides multiple pre-configured ML specific VMs and computes instances. These come with most of the Machine Learning and Deep Learning libraries pre-installed and configured. One can also choose to create a local development environment if required Data Set – This step involves connecting to different data sources like Azure Blob Storage, Azure Data Lake, etc. and create a Machine Learning Data set. This Dataset can be used to access the data and its metadata when we create a Run Experiment & Runs – An experiment is a logical grouping of all the trials or runs. For each ‘Run’, you can log the metrics, images, data or enable logging. All these will be attached to the corresponding ‘Run’ under the Experiment. Compute Target – Creating a compute target helps you run your machine learning training. This compute target can be local or remote Azure GPU/CPU VMs Model Training – Azure ML already comes with multiple Estimators for Sklearn, Pytorch, Tensorflow and Keras. These Estimators helps you organize the ML training. Azure ML also has a capability to create Custom Estimators of your choice. All training logs, versions, and details will be logged in under the ‘Run’ of your experiment. Model Registry – Once the ML training is complete with different ‘Runs’ and you get the ‘best model’, the next step is to register the model in the Azure Model Registry. Model Registry maintains the model versions, descriptions of the model, model metadata, etc. Model Profiler – Before you deploy the model for real-time inference, profiling the infrastructure requirements for the model is very important. Profiling will give you a better understanding of how much minimum memory and CPU’s required for the model to give low latency and high throughput. Model Deployment – A model can be deployed to Azure Container Instances or Azure Kubernetes. This step involves providing which model and what version to deploy, its configurations, and the deployment configurations. Data Collection – It is used to capture real-time inputs and predictions from a model. It is used to analyze the performance of a model. Data Drift – It helps you understand the change in features, data quality issues, natural data shift, change in the relationship between features, etc. Conclusion MLOps is a must for enterprises using machine learning at scale. It allows for managing the complete model lifecycle including model governance and should be made mandatory for all Machine Learning projects. Azure Machine Learning provides has a great feature set for implementing MLOps. It does lack some of the advanced features like model lineage but one can always use dedicated MLOps platforms like MLflow or DotScience on Azure to bring in any missing features.