Why migrating to cloud based open-source database makes sense ?

Over the years businesses in the SME and Enterprise segment build applications and deployed prepackaged applications on multi-modal commercial RDBMS systems  such as My SQL sever, Oracle and DB2. These DBMS was first designed in the 70s when optimal utilisation of memory and compute resources was critical for performance of application transactions and query processing of various decision support systems.

Since then these RDBMS have evolved into a common IT staple for managing clusters of structured and unstructured data in business environment of any sector. In fact, they have a strong influence on how applications were designed and built in the past.

Most RDBMS vendors also built prepackaged applications that resided on their RDBMS systems further locking customers into their environment, though it must be noted that many of these applications were the foundation for business process automation and integrations that drove workforce productivity.

The complexities of running open-source databases in house..

Growing data and high cost

While many of these solution resulted in high performing systems of the time (pre cloud era) over the years businesses started to incur high cost to maintain the exponentially growing organisational data due to automation, mobility, web and sensors. These included DB licenses, ETL related tools and software licenses, storage, compute, and increasing number of human resources to administer performance of the data layer empowering various business processes.

Migration to open-source databases..

In much recent times, businesses have been experimenting with open-source databases for some applications where data growth is significant in order to cut database licensing cost. But this attempt resulted in various problems such as limited features (or features are not as robust as commercial systems), steep learning curve, expensive support and constant config, bug fixing, patching and performance tuning activities. While businesses were able to escape the licensing cost, maintaining a dedicated database team that keeps the open-source databases at an optimal level was a time consuming and arduous effort on the organisation's part.

Scaling based on forecast demand

To add salt to the pain, all of these cost are incurred based on forecasted demand as most businesses are scaling systems in a conventional environment (without cloud capabilities) which means cost is incurred way before the business hits the projected demand. Though this is a universal problem with most conventional IT architectures that lacks cloud capabilities to instantly provision, scale, update, patch and de-provision based on internal demand.

New cloud based applications and microservices...

As we enter the cloud era, application architectures started to take effect of the SOA capabilities of cloud, with high levels of abstraction in every layer (e.g infrastructure, data, intergration and application).

A polyglot architecture where developers can take advantage of the best DBMS, programming language and other ready cloud services including AI, IOT, and Bigdata that accelerates application delivery, begin to take center stage. Apart from SQL type of DBMS, no SQL requirements to support other data types such as value store, document, graph, time series and in-memory DBMS started to emerge as demand rises to 40%. All of a sudden, purpose built DBMS were starting to look much appealing to developer communities every where as opposed to multi models.

Choosing to build such an applications on commercial database can make projects less viable in terms of both cost and feature sets required for a cloud environment where service orientation and scaling instantaneously based on demand is of high importance.

Why cloud based open-source databases are a good option?

Thanks to increasing popularity of public clouds, open-source databases today are mature enough in terms of features and support for wider adoption in businesses including large enterprises.

Overcome management constraints posed by on premise deployments...

It's common to find MySQL, PosgreSQL, MariaDB, MongoDB and countless others in organisations' on premise environment, underpinning many critical business application both prepackaged and customised, but then those implementations are plagued with similar issues surrounding commercial databases such as scalability, availability, security and high operational cost to keep it updated and running.

Cloud providers' DBPaas services eliminates this issue by removing day to day operational complexities from businesses that just want to use the services to build various business solutions to increase quality of engagement with existing and new types of audiences.

Wider adoption and value extension is now possible....

With database administrative task out of the equation, tech teams can now focus on migrating more applications on open-source databases for better value and standardisations within the organisations.

High priority business requirements can be addressed much faster than before with available team and funds, curbing the need for any shadow IT by LOBs.

Refactor apps for the cloud..

Another indirect benefit of this exercise is to modernise legacy applications sitting on premise environments. The migration process is an excellent opportunity to learn more on what pure cloud based databases can do for some applications.

Many cloud databases such as Aurora, Dynamodb, Azure, Google Cloud Bigtable, Google Cloud Datastore, Aspara and DocumentDB are quickly maturing in feature sets and support for other open-source database engines especially MySQL, PostgreSQL, MariaDB and etc. These cloud databases are designed for cloud applications and is highly service oriented to scale unprecedentedly in a highly available and secure manner.

Taking advantage to create low risk initiatives to migrate at least some applications to these cloud databases can result in elimination of years of technical debt inflicting your IT environment.

So, where to get started ?

Identify the open-source equivalent of your commercial database/s

Best method is to start from DBMS systems that contributes to a significant share of the licensing cost and prioritise those pending upgrades and require additional licensing. Identify how many applications are affected, documentations, customisions and integrations to external systems.

Research potential open-source databases and their fit to replace your current commercial databases. Upon a selection, start segregating apps that can and cannot be migrated to the new target database.

Identify cloud based open source database providers....

Next research and identify cloud vendors who provide DBpaas services for open source and cloud based databases. Most cloud vendors such as Aws, GCP, Ali, IBM, Azure provide services for MySQL, PostgreSQL, Maria DB, MongoDB, Firestore and other popular open-source databases. They manage bug fixes, patches, upgrades and scaling, along with day to day operations enabling a far less number of DBAs to manage a bigger number of database clusters.


Rate migration complexities for each app...

Once service providers of choice are identified, explore portfolio of tools provided and recommended by user communities (3rd party) to make an initial assessment on migration complexities of the selected applications. When choosing or building migration tools, think of repeatable usage for both data and refactoring requirements.

Once the potential tools are selected run some initial rest to rate apps, based on migration complexities (single step, multiple step, data size, refactoring levels) and other criterias such as critical app, number of users and etc. At the end of the phase select your pilot case or cases.

Contact 1 or 2 cloud providers to start experimenting...

Next contact your current or suitable cloud providers to compare services and pick one that best suit your business in the long run. You can decide this based on range of service offerings, supported open-source DBMS and cloud databases, price-service effictiveness, cloud eco system offered (other services, partners, user communities, availability zones and etc).

Upon selection of vendor, start experimenting your pilot cases ranging from low to high complexity. Document challenges faced and resolved with future migrations in mind, which means processes must be repeatable and automated as much as possible.

Create a migration schedule and milestone for large scale impact

Having assessed pros and cons of migrating to open-source databases operations to the cloud, build a schedule for migrating apps to the cloud and start executing them in line with business objectives. Leave room for changes in the event new priorities enters the business.

Start large scale migration of all target environment..

Upon completion of pilot migration case, refining and further automation of tools start to migrate applications with low complexities first to the new cloud based open-source database, one at a time.

Address any refactoring needs and test each service before moving to the next, till all low complexity migrations are completed. Repeat the process for complex migration projects. Keep updating schedules, findings and refine tools if neccesary.

Relieve resources for high impact projects

At the end of this process, your team would have not only realised licensing reduction and much stable performance of your databases powering critical applications but eliminated at least some technical debt around critical applications and relieved valuable IT resources for other high impact projects to build new business capabilities, improve access to business insight and performance of queries instead of maintaining 'lights on' projects.

Navigating Values - Cost, Technology Debt, Data ROI and IT Resources

Often a open-source database adoption is initiated due to lack of funds for commercial databases or to cut back on commercial licenses that results in millions of dollars of recurring cost.

Cost

But moving to a cloud provider and consuming DBpaas services results in cost reduction in all types of licensing including those used in ETL processes. In addition , users need not incur cost until a demand arises. This changed how tech organisations plan for future needs, while allocations are made, they don't have to be spent until need arise.

Technology debt that hinders availability, scalability and security design

Another key value is the migration process trigger application modernisation activities to refactor key business solutions on premise to scale and perform better in the cloud both public and private. Migrating to cloud based databases such as Aurora and Azure increases these values by leveraging services enabling high availability, concurrency, business continuity, security, and scallibility.

IT Resources

These values are best navigated with scale of adoption horizontally and vertically. The wider the adoption the higher the value accross the organisation, freeing resources time, money and people to do what matters most to your growth - improving experience for customers, creating business innovation, increasing efficiencies and speed to react to market opportunities.

LK Weekly Precis - Corporate investors, monetising 5G, and hustle to complete deals before the slowdown..

March seems to be starting on a higher note with several startups raising late stage funds in th range of US $1 billion and more. This includes Grab, Go Jek, NYSE listed Sea Group in the recent follow on share offer and the Alibaba backed, Chinese influencer power blogger marketing platform, Rhunn.

Government initiatives are picking up steam especially in Southeast Asia for coaching services, co-working spaces, tax exemption, seed fundings and other resources but requires program owners to improve execution, assesment criterias and reach for better outcomes.  

Here are some key highlights to note this week;

5G Opportunities for Operators and Startups

The recent MWC 2019 in Barcelona, Spain may have been a routine yearly event, but visitors certainly caught a glimpse of a very different future for communication sector in the coming years. Change in business model, operation, partner eco-system, new customer and service segments is imminent. Operators will need to move upwards of the infrastructure, basic voice and data services for revenue and quicker ROI or risk running into massive losses at the point of the next network upgrade.

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Various participants including KT, China Mobile, startup communities and others demonstrated usecases on Smartcities, Smartfactories, 5G Cloud, Augmented Reality and Virtual Reality connectivity, autonomous vehicles and many more, that is pointing to enterprises as the key segment to monetise 5G investments.

Traditional partners such as Samsung and LG are adding new IoT services to offerings, apart from a range of mobile and connected devices. For instance the grocery replenishment service with Samsung refrigerator.

Telco operators in Korea, Japan and China are certainly leading the race when it comes to implementing 5G use cases and without a doubt will be in the forefront of innovations, in this space. 

AirAsia launches Redbeat Capital in collaboration with 500 Startups

In our last weekly summary we covered how DBS was looking to invest in startups that can help distribute the banks products further into new territories. It seems that trend is here to stay with more conglomerates taking the same approach to unlock new markets and innovation.

This week AirAsia announces the launch of Redbeat Capital in collaboration with 500 Satrtups. The US$60 million  fund will be used to provide post seed funding for global startups making way into Southeast Asia in travel, lifestyle, logistics and fintech segments.

Huawei Cloud Region Opens in Singapore

Huawei adds a new cloud region in Singapore aside from China, Europe, Latin America, Hong Kong, Russia, Thailand and South Africa. Huawei Cloud now has 40 availability zones in 23 geographic regions. Offerings will include various platform services for artificial intelligence and machine learning.

Currently the company is actively hiring the regional team and is aware of the market's highly competitive landscape with several key cloud vendors already delivering values, where customers are rapidly adopting cloud for better IT efficiencies, investment flexibilities, consistent performance, geographic expansion and faster time to market.

In the past, Huawei depended on its portfolio of foreign based Chinese customers to penetrate into new markets, but we should anticipate some new field tactics beyond price cuts and equivalent services for businesses this time. A shift of sales focus on nailing at least sixty percent of revenue share from services that run above the basic compute, storage and networking infrastructure services is almost mandatory to differentiate from the rest of the pack.

Meituan-Dianping and Chope

Restaurant booking sites and apps in the region has certainly changed how restaurants perceive customer experience for better or worst.

On peak days, customers are rushed to cater next booking, cancellation fees for no show, constant interruption from servers to top up drinks and other add ons to keep the table, have just made it more of a hassle for diners lately.

However, this deal with Meituan-Dianping should particularly benefit Chope to increase utility of their app and tap into the chinese tourist market at the same time. But will these reservation and restaurant referral sites in anyway add value to diners experiences? Can they help restaurants create unique experiences with the data they are collecting?

Horizon Robotics Raises US$600 Million

The trade war is now opening up opportunities for AI chip makers from China including Huawei and Alibaba to accelerate release of products within this year.

Horizon Robotics is one of the highest valued unicorn in China currently, apart from Cambricon for developing AI chips. The company recently raised another US$600 million in funds to push through development, final designs and outsourcing of manufacturing processes.

With several Chinese AI chip makers planning to outsource manufacturing process and rush to release second generation chips by mid 2019, this could prove to be a prospeporous year for Taiwan based TSMC with a full factory load.

Some Cheers for Startup Communities in India

Finally some cheer from our startup entrepreneur communities in India as the Department for Promotion of Industry and Internal Trade (DPIIT) in India announces changes in the definition of startups (turnover not exceeding Rs 100 crore) and set the confusion over 'angle tax' to rest.

Fintechs Refining Playing Field

Lastly fintechs everywhere in the region are refining market strategies to bridge cashless payment, cryptocurrencies, lending, mobile wallets, e-wallets, and other financial services for both consumers and businesses through new alliances, effective sales programs and much polished product releases. 

Aside from startups, fintechs are increasingly seen as a lucrative attached revenue source for mobile operators, ecommerce platforms, conventional financial institutions and travel related sectors that has access to a broad audience of B2C and B2B buyers. 

Some noteworthy highlights of fintech activities this week are as follows; 

• Axiata and Singtel collaborating for cross border payment;
• Alipay having reached staggering 2 million users and 50,000 merchants in Hong Kong in just a year from launch;
• PayTM India introducing subscription programmes to increase utility;
• Razer launches beta services of Razer Pay digital wallet in Singapore;
• and Mobi Direct teaming up with Worldline for digital payment processing in Pakistan.

Some common trends persist and still an untapped B2B segment.... 

Overall the startup scene is still evolving around ecommerce, ride-hailing, logistics, travel, gaming, payment and other B2C segments where a majority of funding deals are channeled by investors at the moment. As a result, we continue to observe several common repeating themes from previous weeks as follows;

• Traditional sectors such as finance, telco and travel continue turning to startups eco-system to accelerate innovation, build new growth engines and discover business frontiers. 
• Chinese corporate investors such as Alibaba, Tencent, Didi and Meituan-Dianping continue to supply capital to various Southeast Asian startups in ride-hailing, travel, e-commerce and other lucrative B2C segments. 
•  Cloud companies such as Facebook (with IMDA) and Alibaba are working in deeper collaboration with regional startup incubators to lure and accelerate startup success on their platforms.

Nevertheless, this is an ideal period for startups in the region to reboot the drawing board in the B2B segments, leveraging the approaching 5G connectivity in IoT, augmented reality, virtual reality and smart factory arenas for new innovations. 

Telco operators in Southeast Asia are generally less prepared to monetise 5G services and may be more willing to pour investments in a startup ecosystem that fills the service gap in a shorter span of time. 

In addition, many of these telcos are positioned poorly for an internal transformation in terms of adding skills, reorganising business structures and constructing business capabilities for 5G use cases due to outdated business policies, practise of privilege systems and lack of diversity in workforce.  

AI Playbook for Startups and SMB

The AI Playbook for SMBs and Startups

Most businesses in the SMB and startup class, still struggle to grasp the broad definition of AI disciplines, what AI really is to their business, where to get started and what successful execution looks like for their businesses. Simplifying a focus area narrowing these concerns specifically to address your business pressures is helpful to keep team in line with long term business goals and strategies.

There are many AI Playbooks written by renown gurus such as Andrew Ng, meant for Fortune 500 organisations and they do contain some very useful guidance and tips for all businesses. Nevertheless, smaller ventures with limited funding, skills and resources may need to rework tactics to suit business.

Here we present steps to formulate a generic AI playbook meant for small and medium sized businesses, which includes stages to:

• internalise AI definition for the business; 
• outline and prioritise business problems that can be addressed with AI;  
• experiment with ready pre-trained model
• build required skills or internal structures;  
• prepare training and test data; 
• choosing a path between pre trained and custom built model for solutions, 
• and assessing overall impact to businesses performance. 

The generic AI Playbook should ideally incorporate other dimensions unique to your business, industry and commercial eco-system over time, for maximum results.

Define and internalise AI for your business...

AI itself is a broad subfield of computer science. It encompasses many branches of studies including rule based AI and decision trees; machine learning (regression, classification, neural networks/ deep learning, reinforcement learning, supervised and unsupervised learning); robotics; text and speech processing (NLP, NLU, NLG); vision (computer vision, image recognition) and several other applications that still have not left the laboratories. 

AI is a field where one or several of these approaches are used to create machines, agents and models that perceive, learn and adapt to perform various tasks and cognitive function similar to humans.  

The AI domain is practically exploding with hundreds and thousands of new approaches, methods and solution areas in transfer learning (learning from lesser data and smaller models), networks with memory (generalising AI, e.g. DeepMind’s differentiable neural computer), reinforcement learning, training simulations, hardware for training and inference that are rapidly advancing in the research, development and real world application stages. 

Communicate how AI should be perceived in your business....

As such, it's easy for businesses to get lost in an ocean of offerings that comes with vendor specific methods, best practices, strategies, tools and ready use cases. Charting and communicating how AI should be perceived within the business very early on, in your AI journey is crucial to ensure potent utility, informed expectations and performance. 

For instance should AI merely assist workers to perform better at tasks? Should AI considered as an avenue to outsource cognitive functions otherwise performed by human workers ? Or should AI be a form of cognitive technology to help expand ordinary worker's ability to explore meaningful solutions to business problems both creative and logical?

Setting the right internal tone on AI can guide alignment with work culture, strategies, high impact projects, investments, selection of tools and services that can drive AI values across the business.

Define business problems that can be solved with AI

The common misconception in this stage is, data science or business team alone is responsible to outline business problems solvable by AI. 

Every business unit can contribute to this stage including sales, marketing, services and tech departments. Imagine how it will affect your business if tech can reduce 40% of cooling needs in your datacenter or how your customer service or inbound sales department can improve performance by multi-fold with realtime speech to text analysis that prompts them with emotion, sentiment and other useful information during a call with a customer.

Build working team; conduct regular meet-ups; answer business questions around growth hacking, improving or staging customer experience, workforce productivity and innovation; keep a revisable record of issues pressing the business growth (this can be a simple excel sheet with list of items); prioritise problems to solve; assess data availability; experiment, assess skills requirement; select initiatives for executive sponsorship and create projects to execute. 

Assess data availability and prepare training data for the problem

Data sources...

Data can be gathered from within the business, public datasets, procured from external sources (e.g. Image Net, Google Data search) or a mix of these. Identify useful data assets for your business problem, list what data is missing or not available and which data can be excluded from your raw selections. Collect and aggregate sufficiently to fit your model. 

Data preparation is a significant contributor to cost of building AI solutions specifically ML models, aside from high performance computing resources though GPU innovations has brought so much economics since a decade ago.

Clean and transform data for training...

Most ML models requires large amounts of data to train and converge before they can be applied to business. Preparing these data may require domain experts to collect raw data, identify features with strongest prediction power and label them. Though the business problem you are aiming to solve, tools and selected algorithms ultimately influences data preparation approach and mechanisms. 

Data transformation is revisited several times....

It is common for developers to spend 50% or more time preparing and scrutinising data quality and revisit data transformation processes (or feature engineering) several times during the development of the model. 

Split data sets for training, test and validation...

The prepared data is usually split into training set to fit the model, test set to evaluates final model fit on the training dataset and validation set to evaluates model fit on the training dataset while tuning model parameters.

Consider pre-train models

Ready to train models for various use cases...

For common business problems which requires simple recommendations, classifications, regression, clustering, anomaly detection and ranking, look for pre-trained model offerings by public cloud vendors such as Google, AWS, Alibaba and Microsoft. Many large and niche AI vendors now offer pre trained models for other slightly complex solutions to perform predictions, speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

No need to build algorithm or ML models.....

These offerings saves businesses the hassle of building algorithms and models from scratch. Plus it's a great way to get started with applied AI for business with quicker results and fast ROI times without having to reinvent the entire process (the value is in the model outcome not building it, especially for non-tech businesses). 

No need to replicate work for  ML backend .....

The team need only concentrate on preparing data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models/ functions can then be deployed with other production applications and systems through APIs provided by cloud vendors to apply learnings on new data samples without replicating any work for ML backend.

ML and DL frameworks

In the event that the business problem you aiming to solve is much complex and does not have a ready pre trained model available, it is advisable to custom build the model custom build the model on a machine learning and deep learning frameworknof your choice, offered by cloud vendors through their ML-as-a-service offerings.

Frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, comes readily packaged with interface, libraries, tools, pre-built and optimised components to facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

However, it is highly recommended for new teams to get experience from simpler projects involving pre trained models first before venturing into challenging custom building models. It is easy to loose ROI when tinkering with ML or DL models without past experience and benchmarks.

Building AI skills 

When it comes to smaller businesses, building AI skills in-house is really a trial and error exercise to find the right blend of subject matter experts (or the minimal quo) to address AI problems and build solutions. This team's skill level is adjusted according to how they plan to address business problems, for instance with ready models or custom build models.

AI requires programming (e.g. R, Python, Lisp, Prolog, Scala, C, C++ and Java) data science (at least fundamental level) and business domain knowledge at the minimum to obtain some high level results. A strong foundation in mathematical fields such as probability, statistics, linear algebra, mathematical optimisation is necessary if your business wish to develop own algorithms or modify existing ones to fit specific goals and constraints.

Training existing workforce, hiring from other organisations and university grads are some of the common methods to build the right team.

Education sites such as Coursera, EDX, Machine Learning Mastery and many others provide neutral self learning content suitable for both technical and business audience. Content varies from fundamental to advanced level and comes with certification process that can be committed at one's own pace. 

Vendors such as AWS, GCP and Microsoft too have their own academies but syllabus may be skewed towards respective services. AWS and Microsoft are so far the best corporate academies I encountered and complies to many criteria mentioned in Andrew Ng's Fortune 500 AI Playbook in addition to easy to follow content and no fuss lab use (note that this is paid resources).

Assessing impact to business..

Once trained, tested and deployed, weigh the model's impact to your business performance. I use a no fuss method where I categorise what was increased, reduced, created and eliminated over a set period of time.

For instance:

• Did the model INCREASE overall customers value, revenue or profitability ? 
• Did the model REDUCE fraud risk, customer churn or cost ?
• Did the model CREATE new growth engines, data products or differentiators ? 
• Did the model ELIMINATE manual task, blind spots or redundancies ?

These answers are then scored and quantified for best alignment with overall strategic goals and objectives. The data is then collated for all AI projects to visualise and quantify the total impact to business financially (profit, revenue, growth rates, etc) and other areas such as competitiveness, reputation, and customer satisfaction. 

This exercise is helpful in two or three ways :

• to identify successful AI projects and approaches
• to build AI projects that can increase the value or performance of existing models
• find optimal opportunities to apply AI learnings to other functional areas 

There are many ways to measure impact, choose one that you are most comfortable with and put it to work. Use the discoveries to increase effectiveness of AI projects for your business while filling gaps in skill sets and access to useful data assets.

Conclusion - Ethics, Explainability and Human-Machine Interaction

In the end, AI systems are nothing but stacks of infrastructure and programs in various architectures that learns and executes continuously, depending on its utility. The machines aid human workers to predict outcomes, navigate a situation and decide on best action path with less mental drudgery while spanning solution options that was never possible before.

But that doesn't mean AI systems and models are perfect or free from self vested exploitation. Since the 2010s public concerns about racial and other biases in the use of AI for criminal sentencing decisions, creditworthiness and other forms of social credit systems (e.g. China) is driving demand for transparent AI systems and agents.

Explaining decisions of AI algorithms or models, especially those powered by deep learning or other complex neural networks is still a difficult task and is also known as the 'interpretability' problem.  Unlike decision trees and Bayesian networks that are more transparent to inspection, complex neural networks stack on one another and may involve millions of neurones processing towards the outcome.

As such, deploying sufficient governance protocols for data privacy and security; an architecture which facilitates transparency and tools to detect bias in algorithms is as critical as creating successful models or functions. Though in many cases engineering for more explainability and transparency may lead to significant accuracy trade offs. 

Making Sense of Public Cloud Investments

The public cloud infrastructure services (Iaas) market, according to Gartner's estimation is reaching $60 billion in 2019 and is growing at 27% YoY (fastest growing cloud services segment) from the previous year. Assuming Asia contributes 25% of the global share, turns the region into a critical geo for cloud vendors to battle for market leadership. 

However, cloud practitioners in Asia are still somewhat battling with what is considered as decade old cloud myths evolving around security, compliance, IT control and short term ROIs when conversing with regional customers riding on conventional structures and work cultures. This includes financial institutions, communication service providers, retailers, manufacturers and other asset intensive industries.

While these are all valid concerns, over the years premium cloud vendors have more than proven that their infrastructure and platform services (IaaS, PaaS) are superior to any single institutional  environment in terms of performance, security, reliability and overall economics to sustain just about any type of workloads. The public cloud values are now evolving rapidly with introduction of newer, advanced services in opensource databases, artificial intelligence, low code coding, and running mission critical workloads, enabling businesses to gain better control of operations as they cut waste, optimise performance, develop the much needed new capabilities and differentiators faster than ever. 

Open-source Database Services

Businesses have been dabbling with open-source databases (as well as open-source operating systems, libraries and tools) even before cloud computing services emerged and was aware of the potential to free organisations from growing commercial database licensing burdens. However, managing these open-source databases required additional training, operational and maintenance resources, apart from the unpredictable fixes and releases from the open source community. These issues limited the usage of open-source databases such as PosgreSQL to a small number of non critical, in-house development projects and applications in the past. 

This scenario changed in the recent years as cloud computing companies started to offer fully managed open-source database services such as PostgreSQL, MariaDB, MySQL and many others. Businesses start to increase adoption overnight by consuming cloud based open-source database services for existing and new applications while gaining new efficiencies, never possible before. The polyglot nature of cloud native development, where multiple types of databases and models are deployed in an application composed of micro-services and API calls only drives this trend further. 

Popularity of open-source databases according to DB Engine Ranking site, is about to converge with commercial databases in 2019 or in the early parts 2020. 

Artificial Intelligence, Machine Learning, Deep Learning

Explore pre-trained ML models

If new to artificial intelligence and its sub domains machine learning, deep learning and transfer learning, take advantage of the various pre-trained models offered by public cloud vendors such as Google, AWS and Microsoft to perform prediction, classifications, natural language processing (NLP), speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

Start by experimenting solutions to business problems while inflating returns on organisational data assets. This provides an opportunity for the business to quickly reap benefits from readily available models where the team need only prepare data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models can then be deployed with other production applications and systems through APIs to apply learnings on new data samples.

Building custom models..

As the internal team gain experience and get familiarise with ML projects, commence a process to create step-by-step AI playbook to define and prioritise business problems that can be addressed with AI, data preparation, algorithms selection, and developing a model to make predictions. This exercise should involve critical members from business, data science and the engineering teams, ideally aligned to addressing growth challenges, resources optimisation, innovation and enhancing customer experience. 

Machine learning and deep learning frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, that comes readily packaged with interface, libraries, tools, pre-built and optimised components facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

Pick a cloud vendor for ML-as-a-service that best fit your needs...

Top public cloud vendors such as AWS, Microsoft Azure, Alibaba and Google provide support to most of the frameworks mentioned above for custom building models apart from their pre trained model offers for common business problems. New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

Develop cloud native applications faster ...

Today a challenge sparks an idea, an idea turns into a prototype and a prototype transforms into a multi-platform first release in just matter of days and weeks. Aside from core infrastructure services, cloud vendors offer various advanced services ranging from devop, language SDKs, container orchestration, serverless computing, no to low code development platforms (both proprietary and open-source), APIs, code libraries to ready templates to help organisations accelerate application development arising from sudden business needs. 

Most post cloud applications are series of micro-services connected to one another via APIs leveraging languages, databases and code bases most suitable for the required function and the supporting technical environment.

No to Low Code Platforms

In addition, low code platforms such as Mendix, Outsystems, Google App Maker, Appian, and Microsoft Power App empowers traditional developers, IT professionals and to a certain degree the average business users to accelerate software delivery for business use cases made up of common features and components by simply utilising existing templates, forms, objects, drag and drop of prebuilt features.

Ensure performance of mission critical applications....

Learn which of your mission critical applications are suffering from intermittent or persistent performance issues affecting business processes critical to productivity, customer experience and revenue activities. Cloud vendors offer various services and pricing plans to optimise business applications from SAP, Oracle, Microsoft, IBM and others where data grows exponentially with transactions in time.

Exploring public cloud offers for the aforementioned type of applications can reveal credible areas of savings, operational simplification, performance improvement, speedy delivery of new business requirements, higher utilisation, improved security and compliance. Keeping a revisable record of requirements and working with cloud vendors to formulate cloud contracts to efficiently counter all planned and unplanned workloads can free internal resources for myriad of higher impact business activities.

Know values, learn constraints and strategise for long term...

Ultimately cloud values can vary significantly from business to business depending on the nature of operational model and the overall ecosystem. The four areas mentioned here are general enough for most sectors, though some businesses might benefit immensely from other emerging tech such as augmented reality, IOT, 3D or bigdata platform services.

In addition, identifying constraints that prohibit enterprise architectures from extracting full potential of public cloud eliminates aimless unproductive explorations. For instance legacy apps on rigid centralised architectures that may not gain much efficiencies when moved to the cloud.

Finally having a solid long term strategy, helps. For instance all new development and deployment to take the 'Cloud First' approach, or maintaining a hybrid cloud environment with only unpredictable workloads moving to public cloud or 'Cloud Only' approach where the internal teams build in-house brokerage capabilities to fully monitor its environments on multiple public clouds and perform various optimisation as needed.

LK Weekly Precis - A Quiet Post CNY Week

It's a quiet post CNY week with no major shifts really. Go-Jek gears up plans to add payment partners in the region, the ongoing Didi~OYO chemistry, DBS warming up to startups for fresh new growth, PayPal office closure in Malaysia, Huawei's security concern and impact to 5G rollouts are just a few things to highlight this week.

Go-Jek adds Coins.ph as Payment Partner

The battle to win ride hailing leadership in the region continues, with Go-Jek making several moves to progress in the past weeks. After completing alliance agreements with VietinBank with Go-Viet and Singapore's DBS last year, Go-Jek adds another fintech partner this week, called Coins.ph to mobilise things in the Philippines market despite some brush offs with regulators there, last year. 

Go-Jek, considers payment as the core of its super-app play and intend to complete payments gaps in every market. The Indonesian unicorn is currently backed by investors including Google, Tencent Holdings and JD.com and is pushing valuation to $9 billion.

https://asia.nikkei.com/Business/Companies/Indonesia-s-Go-Jek-invests-in-Filipino-payments-startup-Coins.ph

The Didi ~ OYO Chemistry 

"Ride comfortably with Didi and Stay comfortably with OYO"! The Didi ~ OYO chemistry is catching on naturally with riders and travelers in China, that the Chinese ride-hailing giant Didi Chuxing is investing $100 million in Indian hospitality chain Oyo despite cut backs in the Chinese market.

OYO is currently expanding actively in markets such as Southeast Asia, Europe and China apart from home market adopting similar campaigns with ride-hailing partners.

https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/oyo-gets-100-million-from-chinas-ride-hailing-giant-didi-chuxing/articleshow/67985663.cms

DBS is Open to investing in Startups

As more service sectors converge and customers turn to mobile app for all of their daily service needs, banks such as DBS too are eying the the app and super-app economy to realise new customer segments and growth engines. 

In a recent interview with the Nikkei Asian Review, Mr. Piyush Gupta stated that the bank is open to investing in Startups where the bank's products and services can be distributed to whole new segments. As stated before, DBS recently entered into a strategic partnership with Go-Jek for facilitating payment services but it's unclear if this will this result in new customer flow for the bank.

https://asia.nikkei.com/Business/Banking-Finance/DBS-open-to-investing-in-startups-CEO-says

Huawei, 5G Rollout, Security Concerns - It's business as usual in Southeast Asia

In the backdrop of an intense US-China trade war, are claims made by US intelligence community that Huawei products (particularly the 5G base stations and mobile phones) may contain serious security vulnerabilities that empowers the Chinese vendor with capabilities to conduct undetected espionage. 

This has lead global communication network operators, including long standing business partners such as BT, Vodafone, Dutch Telecom, Orange, LG U+ and others to temporarily suspend and reconsider Huawei agreements pertaining to 5G rollout. LG U+ also made a press statement recently, that the aforementioned equipment source code and various other materials have been sent to an international common criteria (CC) verification institution in Spain for security verification and the report is expected to be out in August or September this year. In the meantime LG U+ intends to rollout base stations for 5G in major city areas. Other Korean network operators such as SK Telecom and KT have suspended Huawei deals for the moment.

In the meantime, Huawei released a media statement informing clients that the company will work along customers with any additional security requirements or compliance towards meeting sufficient cybersecurity standards. The company has also set up a comprehensive FAQ Page to address accusations and correct misinformation.

In the meantime, it's business as usual in Southeast Asia with operators in countries like Philippine, Thailand and Malaysia affirming continued allegiance to Huawei.  Many have openly stated that it will be a tremendous effort to build the next 5G network without Huawei. Top executives further stressed the fact, that 5G is a non stand alone network, as it needs to integrate to LTE and other networks Installed previously, many of which use Huawei's equipments. As for Southeast Asian operators, rebuilding means undoing work accomplished in the last two decades, apart from acquiring huge losses and working forward with Huawei to patch any security concerns if valid, is the sensable way forward.

https://www.scmp.com/tech/big-tech/article/2185816/huawei-gets-vote-confidence-philippines-globe-telecom-which-says

PayPal closes Malaysian Operation Office 

Media reports this week that PayPal has offered VSS to all employees in Malaysia and is closing its operation office there. PayPal has been in Malaysia since 2011 and has offices in other Asian locations such as Philippines, China and Singapore. Reasons for closing the office is unclear but observers are pointing to competitive landscape and a weak business team as the contributing factors. The company however reaffirmed that the internal reorganisation will not affect customers in Malaysia.

https://www.bankingtech.com/2019/02/paypal-to-close-offices-in-malaysia/

aCommerce in Trouble?

aCommerce just released the upgraded BrandIQ line of products and services late last year. A relentless startup when it comes to helping clients accelerate online sales with many leading brands such as Unilever, Samsung, Nestle, Philips and L'Oreal in customer portfolio, the company like many other growing startups did change direction from purely an enabler of e-commerce to distribution of products. Operating in Thailand, Indonesia, Philippines and Singapore, the Google Premier Partner Award winner provide services including logistics, fulfilment, delivery and digital areas like marketing. 

But a recent report by Dealstreet Asia is indicating that the company might be in trouble with key executives leaving the operation including in country offices.  aCommerce was planning IPO in 2020.  

https://www.dealstreetasia.com/stories/momentum-works-acommerce-120055/

That's all for this week and wishing everyone a belated CNY!