We begin by describing the advantages and disadvantages of different development strategies, so you can identify the ideal approach for your company.

The purpose and characteristics of AI frameworks (such as TensorFlow and PyTorch) and popular AI techniques (such as Support Vector Machines and Naïve Bayes) can be confusing. To catalyse your experimentation with AI, we then highlight and explain the AI frameworks and techniques best suited to solve a range of problems.

APIs offer specific functionality fast

You may be able to solve the problem you have identified by using an AI application programming interface (API) from a third party. These services fulfil specific, limited functions to a moderate or high standard at low cost. API calls can process your data and provide an immediate result.

Most AI APIs solve problems in the domains of vision and language. Language APIs include transcription, translation and topic extraction. Vision APIs include object recognition, scene detection and logo identification. Numerous AI APIs are available from Amazon, Google, IBM and Microsoft. Features vary (Fig. 13-15) and are advancing rapidly.

Transferring large volumes of data can become expensive. If you are using Amazon, Google, IBM or Microsoft for other aspects of your platform, and your platform provider’s APIs fulfil your requirements, your existing vendor may be an attractive option.

Many other companies, however, offer high-quality APIs in the fields of vision, language and forecasting (Fig. 16). Access a fuller list of nearly 200 APIs at www.programmableweb.com/category/artificial-intelligence/api (source: Programmable Web).

APIs offer immediate, useful results at the expense of niche functionality and differentiation. APIs deliver:

• Time-to-value: APIs provide immediate capability. By calling an API, your company can make immediate use of functions ranging from language translation to object recognition.
• Low initial cost: While extensive use can become expensive, APIs can cost as little as tens or hundreds of pounds to use – making AI accessible to companies of all sizes and organisations that seek proof of value before committing to greater budgets.
• Quality: Large companies, including Google and Microsoft, have invested billions of pounds in their AI services. Many are highly capable.
• Ease of use: AI APIs are accessible to developers without expertise in AI. Companies without knowledge of AI can immediately take advantages of AI via AI APIs.

Limitations of APIs include:

• Functionality: APIs offer specific functionality, often in the fields of vision and language. If your requirements fall outside of what is available, an alternative approach will be required.
• Configurability: APIs do not allow you to adjust the training data or models on which the services are based. If you wish to develop services based on unique training data you have, or tune underlying algorithms for improved results, APIs will be unsuitable.
• Genericness: APIs are designed for mass adoption; they tend to be generic and lack depth and domain specificity. Object recognition APIs can actually tell the difference between BMWs and Skodas but are unlikely to be able to tell the difference between a BMW 6 Series and 7 Series.
• Commoditisation: The APIs you use are available to your competitors. It will be challenging to create lasting competitive advantage, and associated market value, through use of third party APIs.
• Lifetime cost: Extensive use of APIs can attract a high cost relative to an in-house solution you own. Dependence: Large vendors have, on occasion, discontinued APIs. Smaller vendors can be acquired or cease to operate. Using third party APIs creates a dependency over which you have no control.
• Privacy: Using APIs involvespassing your data to third parties. Does this comply with your data permissions? Does the third party retain a copy of your data or use it for any other purpose?

Overall, APIs are ideal if you seek an immediate, low cost solution to a generic problem. APIs will be insufficient, however, if you have a niche challenge, seek greater control and configurability, or seek long-term differentiation through AI (Fig. 17).

Many companies adopt a ‘hybrid’ approach (page 54), using APIs for rapid proofs-of-concept while transitioning to an in-house team that can deliver improved, differentiated, domain-specific capabilities over time.

Managed services offer increased capability at low cost

Several vendors offer managed AI services. A step beyond pre-tuned, function-specific APIs, managed services enable you to upload your data, configure and train your own AI models using a simple interface, and refine the results. These services abstract away much of the difficult of developing AI and enable you to develop a custom solution rapidly, via a simplified interface and limited coding.

Peak, a leading managed AI service company in which we have invested, offers an effective solution. Solutions are also available from Amazon (SageMaker), Google (AutoML), IBM (Watson), Microsoft (Azure) and Salesforce.

Managed services have several advantages:

• Capability: greater flexibility and control than simple APIs; managed services enable you to develop custom models and, potentially, bespoke IP.
• Cost: cheaper than building an in-house AI team or outsourcing development.
• Speed: faster time-to-value than building an in-house AI team.

Limitations include:

• Control: less control than in-house development; access to underlying models will be limited, reducing customisation and tuning.
• Permissioning: you must be comfortable transferring your data to a third party.
• Reliance: it may be expensive or unappealing to migrate away from a managed service provider, given dependencies and data transfer costs.
• Intellectual Property: Some vendors retain your data to improve algorithms for all; in other cases, pragmatically or contractually ownership of the model you develop may be limited.

If basic APIs will not satisfy your requirements, managed AI services offer a fast, flexible, way to develop bespoke solutions at a lower cost than building an in-house team. Managed services are also ideal for prototyping. If you require more control, flexibility, autonomy and ownership in time, however, significant re-development may be required.

Outsourcing offers expertise for moderate initial investment

If a suitable API or third party product is unavailable, you will need to build an AI solution. However, investing in an in-house team is expensive – at least £500,000 per year, typically, even for a small team. There are cost-effective alternatives. Several companies provide outsourced AI capabilities, ranging from contractor developers, who work with your own engineers, to complete, outsourced AI development.

The nature of AI development enables reseachers to work on multiple workstreams simultaneously, so outsourcing can be cheaper than maintaining a permanent team. Conversely, transferring large volumes of data securely and frequently, and retraining models on an ongoing basis, can become expensive. Whether outsourcing is appropriate will depend upon a range of considerations including:

• Domain: will a third party offer the expertise you require in your problem domain and sector?
• Expertise: to what extent do you wish to build expertise in-house?
• Speed: do you require trusted expertise more rapidly than you can develop it in-house? Do you require a solution more quickly than you could build in-house?
• Data sensitivity: do you have permission to pass data to third parties?
• Operation: if an outsourcer builds your models, are you entitled to deploy them on your own infrastructure – or are you tied to your outsourcer on an ongoing basis?

Overall if maximising speed and minimising initial costs are your highest priorities, and APIs are unavailable, consider outsourcing (Fig. 19).

If outsourcing, specify:

• Frameworks: is there a specific AI framework you require the outsourcer to use?
• Standards: what accuracy (precision and recall – see Chapter 5) must models meet?
• Data: will you provide cleaned, labelled training data? Or is data to be created by the outsourcer?
• Costs: what costs have been agreed?
• Timescales: what timescales must be met? This can be more challenging than for traditional software development because improving a model may require experimentation.
• Deployment: how production-ready must the output be?

An in-house team offers differentiation – at a price

Investing in an in-house AI team offers maximum control, capability and competitive differentiation – at a price.

An AI team of your own can deliver:

• Flexibility: Control over the hardware, programming languages, frameworks, techniques and data you employ offers the flexibility to iterate and expand your solutions as your needs evolve.
• Capability: APIs offer defined functionality. Managed service environments limit your ability to tune underlying algorithms. Outsourced talent will lack your team’s domain expertise. With an in-house team you have the opportunity to create optimised solutions, potentially beyond the current state of the art.
• Differentiation: An in-house team can develop a unique AI offering that delivers competitive advantage, credibility in the market and associated value for your company.
• Resilience: Without reliance on third party APIs or outsourcers, your AI initiatives can enjoy greater resilience and longevity.
• Security: Retain control over your own data; none of it needs to be passed to third parties.

Drawbacks of an in-house team include:

• Cost: A small in-house team, comprising two to four people and the hardare they require, will cost at least £250,000 to £500,000 per year – potentially more to productise the resulting system. A large team, recruited to solve problems at the edge of research, will require a multi-million pound annual investment in personnel and hardware.
• Complexity: To develop an in-house AI team you must attract, structure, manage and retain AI talent; select the development languages, frameworks and techniques you will employ; undertake data gathering and cleansing; learn how to productise AI into real-world systems; and ensure compliance with regulatory and ethical standards.
• Speed: It will require months to build a productive in-house AI team, and potentially longer to collect the data you require and develop customised solutions that deliver results to the standard you require.

An in-house team may be necessaryif your challenge cannot be solved with existing AI techniques and solutions, if you face significant restrictions on your ability to pass data to third parties, or if you seek competitive differentiation through AI. Before incurring the cost and complexity of an AI team, however, explore whether alternative methods can deliver your requirements faster and for a lower cost (Fig. 20). A hybrid strategy, described below, may be ideal.

To develop an in-house AI team, review all chapters of this Playbook for best practices regarding strategy, talent, data, development, production and regulation & ethics.

A hybrid approach can offer the ‘best of both worlds’

For many companies, a ‘hybrid’ approach to AI is ideal. Plan for an in-house team that will address your requirements to a high standard over time, but use third party APIs (or even a non-AI solution) to solve an initial, simpler version of your challenge.

A hybrid approach may enable you to prove the viability or value of your idea and justify in-house spend. It can also serve as a cost-effective way to identify the aspects of your challenge can be readily addressed and those that will require bespoke work.

A hybrid strategy offers a rapid, low cost start that suits many companies (Fig. 21). Initial investment in hardware, team and software can be minimal. Many APIs offer free trial periods in which you can assess scope for results. Even if your data restrictions prohibit use of third party APIs, you can adopt a hybrid approach with in-house developers using pre-trained AIs. Further, many academic papers and coding competition entries have code uploaded to GitHub and many have unrestricted licenses.

If you adopt a hybrid approach, develop a data strategy (Chapter 1) and pipeline of training data upfront. You can continue to use third-party APIs if they fulfil your needs unless costs become prohibitive, you wish to build resilience, or you seek improved results and differentiation with your own technology. As you gather additional data, you can create more accurate and complex models in-house, as needed and when the business case has been proven.

While the risk of interruption to services from Amazon, Google, IBM and Microsoft is low, vendors do occasionally remove APIs. Smaller vendors offering APIs may be acquired, or their services changed or discontinued. If you adopt a hybrid approach, develop a strategy for resilience. Once elements of your product are in place, examine the pre-trained models and consider moving these services in-house if you can achieve results comparable with the API. You may be able to use your chosen APIs in perpetuity and continually develop niche AI to complement these – a popular approach.

To develop AI, optimise your technology stack

To develop AI – via a managed service provider, outsourcer or in-house team – you have choices to make regarding your AI technology stack. The stack comprises six layers: hardware; operating systems; programming languages; libraries; frameworks and abstractions (Fig. 22).

We offer hardware recommendations overleaf. The problem domain you are tackling (assignment, grouping, generation or forecasting) will then favour particular machine learning techniques and associated libraries and frameworks. Select components for your development stack accordingly.

The degree of abstraction you select will depend upon the skill of your development team, the speed of development you require and the degree of control you seek over the models you develop. Greater abstraction offers faster development and requires less skill, but limits your ability to tune models to your requirements. The size and speed of your models may also be limited.

Not all problems require the full stack; some solutions can be achieved rapidly, without frameworks or abstractions.

For effective R&D, use appropriate hardware

Research and development requires hardware. To process models quickly, ensure your team has hardware with graphical processing units (GPUs) that support NVIDIA’s Compute Unified Device Architecture (CUDA) libraries. These allow your AI programmes to use the specialised mathematics of the GPUs and run at least ten times faster than on a CPU. For many, a laptop with a high performance graphics card is ideal. Current, potentially suitable cards include the NVIDIA GTX 1050 Ti, 1070, 1080 and the RTX 2080.

For greater power, desktop machines with more powerful GPUs are preferable – but at the expense of flexibility for your team. If you are a multi-premise team, or expect your personnel to travel, your team may expect a laptop in addition to a desktop machine you provide for research.

For large models, or rapid training, you will require dedicated servers. Buying and hosting servers yourself, either on-premise or in a data centre, is the most cost-effective over the long term but requires considerable upfront capital expenditure. The majority of early stage companies will find it more appropriate to use cloud-based servers offered by large providers including Google, Amazon and Microsoft. All offer GPU servers, costed according to usage time. Using the cloud providers, at least at the early stages of your AI initiatives, will enable you to control costs more effectively and push the decision regarding buying hardware to later in the process when you have a minimum viable product.

Apply AI techniques suited to the problem domain

For each problem domain (assignment, grouping, generation and forecasting – see Chapter 1, ‘Strategy’) – there are numerous established machine learning techniques.

Techniques vary in their data requirements, training dynamics, deployment characteristics, advantages and limitations. While deep learning methods are powerful, other techniques may be sufficient or better suited. Experiment with multiple techniques. Below, we highlight techniques popular for each domain.

For assignment problems consider SVCs, Bayes, KNNs and CNNs

Classification problems, which offer a defined, correct output to ease development, are frequently an attractive starting point for AI. While convolutional neural networks became popular, in part, due to their efficacy in solving classification problems there are many alternative techniques you can apply – many of which offer effective results and are quicker to implement.

Regression problems quantify the extent to which a feature exists. Because they are also assignment problems, the techniques used for assignment frequently overlap with those used for regression.

For grouping explore Meanshift Clustering, K-Means and GMMs

If you have unlabelled data and seek to cluster it into similar groups, you will require techniques that expose similarity. Definining similarity can be challenging when there are many dimensions to the data.

For generation, VAEs and GANs can be effective

Since its inception, AI has been used to synthesise text; MIT’s ELIZA natural language processing programme, created from 1964 to 1966, offered the illusion of understanding in psychology and other domains. In the decades since, the quality of generation techniques has been transformed – particularly following the introduction of Generative Adversarial Networks (GANs) – while domains of application have broadened to include visual imagery and sound.

For forecasting, causal models and HMMs are popular

Applying techniques to predict the future is challenging; forecasts may be affected by variables outside the data available to you. While the past is not always indicative of the future, AI forecasting techniques are effective when there are causal or periodic effects. Understanding the volume of data you require may need initial knowledge of causal and periodic effects, in the absence of which your model may miss these relations.

Use frameworks to accelerate development

If your team is developing an AI solution, use libraries to accelerate development. The algorithms described above have been coded into efficient libraries for Python and R. Implementing an algorithm directly in Python will be slower – in some cases 60 times slower (Fig. 35).

For numerical analysis, NumPy is a library of choice

There are many libraries available to support numerical analysis. The most popular include:

• NumPy: A library of choice for numerical analysis in Python. Functions are optimised in C so run quickly, matrices and vectors are well handled, and there are many in-built statistical algorithms to support AI.
• Scikit-learn: Released in 2010 as a lightweight library for deep learning, Scikit-learn is built on NumPy and offers considerable overlap, although the two complement each other well.
• Matplotlib: Predominantly a plotting library, to support visual analysis of plots Matplotlib requires its own numerical calculations. These are limited and further libraries are required for broader analytical techniques.
• R packages are not as extensive as those for Python but there are many for numerical analysis on top of core R functions – including caret, gimnet, randomForest and nmle.

In addition to libraries there are specific applications, such as Matlab and Mathematica, which offer extensive functions. While popular in academic settings, they are rarely used in industry given the high cost of software licenses compared with the free libraries available.

For deep learning, TensorFlow and Caffe are popular frameworks

Deep learning frameworks are typically more extensive than numerical analysis libraries and serve as ‘scaffolding’ for your projects. Your choice of framework will impact speed of development as well as the features and scalability of your solution.

With numerous frameworks available, take time to evaluate your project priorities and the framework best suited to your goals. The most popular framework may not be optimal for your initiative. When selecting a framework consider its advantages and limitations, the skills it requires, availability of skills, and scaling and speed requirements (both for development and production).

Unless you are using a pre-trained network, if you have implemented models in a single framework then reimplementing them in another will involve retraining from scratch. You may elect to use multiple frameworks for different problems, particularly if doing so allows consistency with existing development languages.

Frameworks evolve at different speeds and, particularly when maintained by a single business or university, may be discontinued with limited notice. In a rapidly evolving field, frameworks with high levels of community support can be attractive.