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.

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.

How to Choose a Public Cloud ?

This is a popular question, especially for startup and SMB leaderships, whom may not have an advance tech team at their disposal to support with queries and selections when picking a public cloud that returns the most value for new development or migrate existing workloads. Please note that these questions and methods are skewed towards screening IT infrastructure and platform service provider and not so much the Saas provider (we might cover that in another piece of cheat sheet separately).

Ask Questions
Here are couple of things (in no particular order), that your team can use to prepare before meeting the service providers.  

Please note that it's a really bad idea to meet them unprepared, as it provides a white canvas opportunity for the vendor to paint anything they like. It is highly recommended that you avoid such situations. 

Here are the questions/ areas worth exploring;

1. A list of current IT operations (e.g. applications, tools, analytics, security, data-warehouse and etc.) and technologies in place (e.g Operating systems, databases, development tools and languages, security, infrastructure or even other clouds in place).
2. A list of projected/strategic business activities and their automation/digitalisation needs (e.g mobile app, big data analytics, buyer & seller optimisation, etc.)
3. Customisability of compute and memory requirements (in other words, how much control do you have on your VM or VMs).
4. Pro and cons of owning the PaaS level...
5. Standard tech questions - Interoperability, portability,  new app development cycle, security, redundancy, backup and recovery features.
6. The type and size of technology eco-system that the cloud provider connects to - here you are simply looking for three things; suitability of the technology to your operation; standardisation and; provider that connects with a larger eco-system of technologies (e.g Hadoop, ML, AI, Blockchain, Mobile App Platform and a host of other opensource pieces).
7. Pricing mechanism and projections for on-boarding; utilisation blocks (e.g by minutes, hours, monthly, yearly, types of workload - predictive and unpredictive); exit or off-boarding (be aware that some vendors charge even during off-boarding to migrate data and other intellectual assets off of their platform – this happens when you complete contractual term and don’t plan to renew);
8. Discount and rebate applications - when you get them, and when you don't (ask both questions)?
9. What is the lock-in period (e.g 1 to 3 years). What happens if you exit prematurely. What is the unnatural discontinuation cost?
10. What service guarantee does the vendor provide (e.g. SLA, compliance, security)?
11. What happens when vendor fail to deliver service (system performance, security, data, compliance)”according to contractual term? 
12. What support procedures are in place when a support request is registered?
13. What if you need to scale ? How quickly you can scale the needed footprint?
14. What about trainings and upskill activities?
15. What SMB focused programs does the vendor have in place? Some service providers might have special programs for you to mingle with other users in your category and connect. This can be an added bonus to your business as you can tap onto the knowledge and resources of a wider network.

Request for a Pilot Test

if you are happy with the information gathered through the pre-meeting research and cloud salesperson's answers to aforesaid questions, move on to a test request for the tools and  platform services in question.  Scope the test areas according to your procurement needs to stay on course with your current needs. Look out for performance, usability, flexibility, scalability, security and inter-operability results to support and corroborate information you gathered (unless the test fail to do so).

Tap Talks in the Grapevine
It’s a good idea at this point to fraternise with other fellow startups and SMBs who may have already been using the services first hand.  Their experience could be a valuable addition of information to your decisions. Though, when collecting information from informal sources such as this, be aware of the timelines - a problem or disadvantage that existed a year ago may not apply anymore as cloud companies improve (especially the largest 3 or 4) at a rapid pace.

What is a Satisfactory Outcome?
What are you looking to map with these initial questions and explorative activities?

1. Your IT computing and operational requirement based on business roadmap.
2. Suitability of the service provider/s and their offerings to support your business and future growth.
3. Good understanding of tools and services recommended by vendor before you embark on them.
4. A clear idea on pricing matters; discounts and rebates applications; contractual terms.
5. Outline or scope of duties and responsibilities between you and your vendor in regards to your IT footprint. 
6. Cost of discontinuation of business relationship upon completion of contractual terms.
7. Cost and consequences of discontinuation of business relationship due to premature termination of contract.
8. Your rights when service provider fails you? e.g service delivery, compliance, security or data breach.
9. Exit and migration path to other clouds.

Finally, a piece of useful advice to stay productive and avoid frustrations. Expect for contact points to change at anytime while dealing with vendors or any external parties for that matter. As such, it would be wise to document all requirements, arrangements and agreements to terms, contractual and non. This will help you save time in the reestablishment and reinstatement of such interactions which looses sight due to change of contact points.

In addition, get feedback from both business(finance included) and technical team members on the selection before concluding.

Happy Screening!!!!

'Chip War' in search of AI Supremacy!

Ever wondered why we need GPUs or AI accelerators for optimal performance of AI Applications? Ever wondered why a field that existed since the the dawn of twentieth century is only now burgeoning with breakthroughs? 

As Kaplan and Haenlein puts it, "AI is a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.

Artificial intelligence applications that mimics cognitive functions involves deep neural networks, machine vision, sensor technologies, and machine learning techniques that depends on multi core designs, low precision arithmetics and in memory computing capabilities to work well in both cloud and the edge.

In fields like robotics, autonomous vehicles, drones, decease diagnosis, speech recognition, object or face recognition, capability to vastly expand AI calculations by embracing massively parallel processing power, means near real-time learning, energy efficiency and optimal performance. This is why AI applications requires AI accelerators in  its infrastructure design to support various types of computing based on the use cases for sustaining performance during training and inferences.

AI Accelerators

Researchers in scientific fields were probably the first to discover, experiment and adopt GPUs for accelerating AI applications, almost 2 decades ago.  Since then, a 'Chip War' has slowly formed along with other technological developments and is now reaching its peak, among cloud companies, Internet giants, chip makers and startups searching for supersonic growth, especially in the fields of artificial intelligence, machine learning and deep learning. 

Apart from GPUs (graphics processing unit), other types of microprocessors available in the market currently are FPGA (field programmable gate array), ASIC (application specific integrated circuits), in memory architecture and heterogenous computing. As of today, there is no dominant design that underpins all AI architectures, but NVDIA do seems to command some leadership here while others such as Intel, AMD, WAVE Computing, UK's Graphcore and Israel's Habana Labs  are all beginning to ship viable products. 

Chinese players...

China has been investing aggressively to produce indigenous AI chips to wean from dependence on US based producers, with ongoing researches underway by major tech corporations, such as Baidu, Alibaba, Tencent and Huawei, apart from government initiatives. 

Huawei released several products last year, claiming to be faster than NVDIA with ready adopters in datacenters globally. Another startup called Cambricon released products and is aiming for 30% marketshare in China, setting off the Chinese AI chip industry. One other company worth mentioning here and quite notably working on a customised AI chip is China's leading facial recognition system provider, Sensetime, which is planning to build another three or four supercomputers to process data feeds for facial recognition from millions of cameras nation-wide.

The AI chip leadership momentum will only continue to intensify in 2019....

Intel, Nvidia, AMD and Qualcomm are not the only one competing to produce an omni product that can infiltrate into gadgets, computers, machines and robots. There are others such as IBM, Amazon, Apple, Facebook, Google, Microsoft, Tesla and many more in the Deep Learning space, all making attempts at the design and potentially a universally accepted standard and product.

Facebook recently sent a strong signal by hiring Google's Head of Chip Development, that the social networking firm is serious about building its own semiconductors, joining the likes of Apple, Google, and Amazon.

Cerebras Systems, a startup still in stealth mode hired a top Intel executive, Dhiraj Mallick as its Vice President of Engineering and Business Development. Mallick served as the VP of architecture and CTO of Intel’s data center group prior to this.

What to expect...?

For now, the market for deep learning chips is overwhelmingly dominated by Nvdia graphics processors (or GPUs), which have also been widely used in games and other graphically-intensive applications.

Startups looking to attack this space, has the opportunity to beat the big chipmakers and create a new generation of hardware that will be omnipresent among any AI devices. Think autonomous vehicle, robotics, drones or even a server within a healthcare organization training models for medical problems.

As new products from companies such as WAWE, Huawei, Cambricon, Graphcore and Habana enters datacenters and selected enterprises this year, we might see a flow of special purpose devices being released into the market for AI and deep learning.

AI chip innovation will also aid researchers to make further breakthroughs in various fileds such as Computer Vision, Conversational AI, Natural Language Processing, Reinforcement Learning, Transfer Learning, and General AI. Eventually some businesses and governments from buyer nations, may start to take advantage of the available AI chip offerings and form their own discreet AI clouds for a variety of high profile projects across the organisation for deeper business differentiation and operational excellence as models train faster and learn in realtime.

LK Weekly Precis - New e-Commerce Regulations, Acquisitions and Expansions

This week, new ecommerce regulations in India shook the tech business community and indicated that government meddling and protectionism policies may continue to hinder progress of emerging markets in sectors such as ride hailing, hospitality and many others aside from ecommerce.

Ecommerce regulations was also a topic discussed in Davos at the World Economic Forum (WEF), lead by Singapore. In addition, the event for the first time hosted talks among tech executives and leaders, including from BAT, to shape up AI framework that addresses both the seller and buyer nations.

Other than that, ST Telemedia acquisition of Cloud Comrade and Travelstop expansion to 7 Asian markets simultaneously, along with JD.com's first drone delivery outside of China are some notable developments in the startup sphere this week.

ST Telemedia Acquires Cloud Comrade

Last year we saw a number of consultancy firms such as Deloitte and the likes, hunting for acquisitions in the partner space of large tech companies namely Oracle, Sap, AWS, GCP and Microsoft.  This trend is now picked up by several data-centre service providers in the region.

ST Telemedia is certainly moving in the right direction by acquiring Cloud Comrade to enhance its datacenter service offering portfolio, especially in cloud services, IT management, cybersecurity and overall datacenter performance. 

Cloud Comrade helps customers in Indonesia, Malaysia and Singapore deploy cloud to accelerate new development and migrate existing business applications for operational excellence. The startup works in alliance with almost all major public cloud providers such as Ali, Azure, AWS, GCP and Digital Ocean. 

Last year, ST Telemedia acquired stakes worth $27 million, in cloud management company, Bespin Global that operates in Korea and China. The new acquisitions will help ST Telemedia complete service offerings in cloud, AI, Bigdata, digital experience and cybersecurity.

https://bit.ly/2Uj7FKe

JD's Drone Delivers Books and Bags in Rural Indonesia

JD.com has been delivering to some rural parts of China using drones for the last two years. This week JD ran its first drone delivery trial outside of China after securing a government license for regional level operation in Indonesia. According to media the drone travelled 250km to deliver boxes of books and bag packs to school children.

Tencent has a 15% stake in JD.com and together the companies co-invested in a number of Chinese companies. Last year Google announced significant amount of investments in JD and Tencent to make inroads in China. 

Soon, same day and next day delivery will be a common offering, sighting of drones in residential areas should be expected and e-commerce logistic players may have to reinvent their game.

https://globalcosmeticsnews.com/jd-com-paves-the-way-for-drone-deliveries-in-southeast-asia-with-first-government-approved-flight-in-indonesia/

Travelstop Expands to 7 More Markets

Travelstop is a year old startup from Singapore, that simplifies business travel and expense management to the SMB and startup segments. Since inception, the T&E Saas platform has been updated continuously with features and functions to sufficiently meet the needs of both travellers and employers in a segment where such services were inaccessible. 

We believe they are in the path to join the likes of 'certify', 'coupa' and 'apptricity' to challenge other established players such as SAP Concur in the travel and expense solution space for the enterprises.

Recently, the company announced service availability in Indonesia, Thailand, Hong Kong, Taiwan, Japan, South Korea and Vietnam.  The company also announced a mobile app for iPhone users to easily access services. 

https://www.eventsnewsasia.com/20190122052718000454-business-travel-startup-travelstop-strengthens-presence-asia-launches-seven-markets/

New e-commerce Rules/Restrictions in India

The new rule restricts online retailers or marketplaces from sourcing more than 25% of inventories from a single vendor, vendors where the online retailers may have a stake and exclusive deals that results in deeply discounted products. 

The new rules seems to be aimed at protecting millions of small traders, operating offline and suffering from huge losses due to deep discounting practices of both Amazon and Flipkart. According to analysts and mainstream media, the recent electoral losses is seen as one of the contributing factor to this unusually regressive move.

https://www.zdnet.com/article/india-issues-new-e-commerce-rules-to-check-amazon-and-flipkart/

AI Discourse at World Economic Forum, Davos 

Finally AI takes a critical spot in WEF this year with US (Alphabet, Apple, Facebook, Amazon, IBM, Microsoft) and China ( Baidu, Alibaba, Tencent) seen as leaders of the space. Economic potential, social threat, globalisation 4.0., ethical practices, AI nationalism, global policies for both AI sellers and buyers were some of the issues beginning to shape the global AI agenda.

https://asia.nikkei.com/Business/Technology/Asian-tech-execs-voice-hopes-and-fears-for-AI-in-Davos

Singaporean Ride-hailing Startup, 'Tada' in Vietnam

This year we might see more ride hailing players emerging in the region, including traditional players modernising their business and competing for their pie with larger competition namely Grab and Go Jek. 

New entries might come from taxi operators, affected driver groups and rental service providers. 

We might also see, new country level regulations, niche plays, convergence of industries/sectors, significant mergers and acquisitions in this space as we cool off in quarter four.

https://www.retailnews.asia/singaporean-ride-hailing-startup-tada-launches-in-vietnam/

HG Exchange

HG Exchange, a fintech industry backed initiative has recently submitted a regulatory application to Monetary Authority of Singapore (MAS). This move will provide investors in the region with better access to high growth companies such as Grab, Go Jek, Didi, Deliveroo and others. 

The exchanged will be built by blockchain developer Zilliqa and Taiwanese digital asset platform MaiCoin.

https://www.techinasia.com/in-brief-hg-exchange-seeking-launch

It's seems to be a slow week in anticipation of CNY next week but we believe businesses will keep up momentum till quarter 3 as a slow down is expected in quarter 4. 

Happy Sunday!